WO2018143369A1 - Method for evaluating pancreatic cancer in diabetes patient, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device - Google Patents

Method for evaluating pancreatic cancer in diabetes patient, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device Download PDF

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Publication number
WO2018143369A1
WO2018143369A1 PCT/JP2018/003477 JP2018003477W WO2018143369A1 WO 2018143369 A1 WO2018143369 A1 WO 2018143369A1 JP 2018003477 W JP2018003477 W JP 2018003477W WO 2018143369 A1 WO2018143369 A1 WO 2018143369A1
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evaluation
value
concentration
pancreatic cancer
control unit
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PCT/JP2018/003477
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French (fr)
Japanese (ja)
Inventor
浩通 伊佐山
卓 水野
實 山門
信矢 菊池
理浩 ▲高▼田
信和 小野
智行 田上
山本　浩史
今泉　明
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味の素株式会社
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Priority to KR1020197022734A priority Critical patent/KR102475008B1/en
Priority to JP2018566099A priority patent/JP7120027B2/en
Publication of WO2018143369A1 publication Critical patent/WO2018143369A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6806—Determination of free amino acids
    • G01N33/6812—Assays for specific amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
    • G01N33/57488—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites involving compounds identifable in body fluids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids

Definitions

  • the present invention relates to a pancreatic cancer evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device for diabetic patients using amino acid concentrations in blood.
  • Pancreatic cancer is the fifth leading cause of cancer death in Japan and the fourth leading cause of cancer death in the United States. Pancreatic cancer has few symptoms depending on the site of cancer and is often discovered after it has progressed. Pancreatic cancer often has metastasis to adjacent tissues outside the pancreas even if it is detected at 2 cm or less using diagnostic imaging, and if it cannot be excised, the prognosis is very poor even if chemotherapy is performed.
  • the overall 5-year survival rate for pancreatic cancer is about 5%. On the other hand, for a small pancreatic cancer of 1 cm or less that can be operated, a 5-year survival rate of 57% can be expected. ing.
  • pancreatic cancer uses abdominal ultrasound echo, CT and MRI, but none of them has a high discovery rate of pancreatic cancer.
  • image diagnosis using endoscopes such as ERCP and EUS has become widespread, and it is known that the detection rate of pancreatic cancer is high and effective, but the physical burden on patients is high, bleeding due to examinations, etc. Risk can also occur.
  • tissue diagnosis by biopsy is a definitive diagnosis but a highly invasive test, and it is not practical to perform a biopsy test at the screening stage.
  • Serum cancer markers include CA19-9, CEA, SPAN-1, and DUPAN-2. These markers have relatively high sensitivity and specificity for advanced cancer, but have a low positive rate in early cancers and may be positive in cancers other than pancreatic cancer.
  • pancreatic cancer has a relatively low morbidity compared to other carcinomas, and pancreatic cancer has no established method for cancer screening for the general public.
  • pancreatic cancer has no established method for cancer screening for the general public.
  • pancreatic cancer examples include diabetes, obesity, smoking, family history of pancreatic cancer, chronic pancreatitis, pancreatic findings such as IPMN (intraductive capillary mucinous neoplasms) and cysts.
  • IPMN intraductive capillary mucinous neoplasms
  • cysts cysts.
  • pancreatic cancer As a problem of screening for diabetes, although the population affected by diabetes can be narrowed down to a certain level as compared to the general medical examination group, the number of subjects is still large, and further narrowing down is necessary. While increasing the detection rate of pancreatic cancer among newly diagnosed diabetic patients, it is known that long-term diabetes is also a risk factor for pancreatic cancer. It is assumed that the former can be divided into diabetes as a result of pancreatic cancer, and the latter as diabetes as the cause of pancreatic cancer. However, these high-risk groups cannot be narrowed clinically.
  • CA19-9 which is widely used as a marker for pancreatic cancer, is known to be falsely positive due to an increase in blood glucose, and is considered inappropriate for narrowing down high-risk groups from diabetic patients. Therefore, it is expected to devise a method for performing screening easily and widely.
  • Patent Document 1 discloses a discriminant for a pancreatic cancer disease state based on multivariate analysis in which a healthy subject sample and a pancreatic cancer patient sample are compared.
  • Non-Patent Document 2 reports that changes in the amino acid profile in blood have been observed from 2 to 5 years before diagnosis of pancreatic cancer based on the results of cohort studies.
  • Non-Patent Document 2 suggests that metabolic abnormalities of branched chain amino acids (BCAA) have occurred before pancreatic cancer became apparent by diagnostic imaging or the like.
  • BCAA branched chain amino acids
  • Non-Patent Document 3 it is known that a BCAA metabolic abnormality occurs based on a diabetic condition.
  • Patent Document 1 does not perform analysis specified for diabetic patients.
  • non-patent document 3 suggests an association between diabetes pathology and pancreatic cancer, but the specific technique for applying the transient amino acid change shown in the document to the diagnosis is not clearly described in the document. Also, in this document, a search for a combination of two or more biomolecules or an index formula using multivariate analysis is not performed.
  • the present invention has been made in view of the above, and an evaluation method, a calculation method, an evaluation apparatus, and the like, which can provide highly reliable information that can be helpful in knowing the state of pancreatic cancer in an evaluation subject having diabetes,
  • An object is to provide a calculation device, an evaluation program, a calculation program, an evaluation system, and a terminal device.
  • the present inventors have intensively studied in order to solve the above-mentioned problems, and have a high pancreatic cancer discriminating ability compared with the multivariate discriminant group described in Patent Document 1 by limiting the subject to diabetic patients.
  • a correlation equation (index formula) using amino acid variables was found, and the present invention was completed.
  • the evaluation method according to the present invention includes 19 kinds of amino acids (Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, and Arg) are used to evaluate the state of pancreatic cancer for the evaluation object. It is characterized by including.
  • an expression including a variable into which at least two concentration values of the 19 kinds of amino acids are substituted is used. By calculating the value, the state of pancreatic cancer is evaluated for the evaluation object.
  • the evaluation apparatus is an evaluation apparatus including a control unit, and the control unit uses at least two concentration values of the 19 kinds of amino acids in blood to be evaluated having diabetes.
  • the evaluation object is characterized by comprising evaluation means for evaluating the state of pancreatic cancer.
  • the evaluation method according to the present invention is an evaluation method executed in an information processing apparatus including a control unit, and is executed in the control unit, the 19 kinds of amino acids in blood to be evaluated having diabetes An evaluation step of evaluating the state of pancreatic cancer for the evaluation object using at least two concentration values.
  • the evaluation program according to the present invention is an evaluation program for execution in an information processing apparatus including a control unit, and the 19 types in the blood to be evaluated having diabetes for execution in the control unit An evaluation step of evaluating the state of pancreatic cancer for the evaluation object using concentration values of at least two of the amino acids.
  • a recording medium is a non-transitory computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the evaluation method.
  • an evaluation system includes an evaluation device including a control unit, and a control unit, and concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood of an evaluation object having diabetes.
  • An evaluation system configured such that a terminal device to be provided is communicably connected via a network, wherein the control unit of the terminal device transmits the concentration data to be evaluated to the evaluation device.
  • Data transmitting means, and result receiving means for receiving an evaluation result related to the state of pancreatic cancer in the evaluation target, transmitted from the evaluation apparatus, and the control unit of the evaluation apparatus is transmitted from the terminal apparatus.
  • the density data receiving means for receiving the density data of the evaluation target, and the density data of the evaluation target received by the density data receiving means
  • the evaluation means for evaluating the state of pancreatic cancer for the evaluation object using the concentration values of at least two of the 19 kinds of amino acids, and the evaluation result obtained by the evaluation means as the terminal
  • a result transmitting means for transmitting to the apparatus.
  • the terminal device is a terminal device including a control unit, and the control unit includes a result acquisition unit that acquires an evaluation result regarding a state of pancreatic cancer in an evaluation target having diabetes, and the evaluation The result is a result of evaluating the state of pancreatic cancer for the evaluation target using at least two concentration values of the 19 kinds of amino acids in the blood of the evaluation target.
  • the terminal device is configured to be communicably connected to an evaluation device that evaluates the state of pancreatic cancer for the evaluation target via the network in the terminal device, and the control unit includes The apparatus further comprises concentration data transmitting means for transmitting concentration data relating to the concentration value of at least two of the 19 kinds of amino acids in the blood to be evaluated to the evaluation apparatus, and the result acquisition means is transmitted from the evaluation apparatus. Receiving the evaluation result.
  • the evaluation device is connected to a terminal device that provides concentration data regarding at least two concentration values of the 19 kinds of amino acids in the blood to be evaluated having diabetes via a network.
  • the evaluation apparatus includes a control unit, wherein the control unit receives the density data of the evaluation target transmitted from the terminal device, and the density data receiving unit receives the density data receiving unit.
  • the state of pancreatic cancer is evaluated for the evaluation object using at least two concentration values of the 19 kinds of amino acids in the blood of the evaluation object. Therefore, pancreatic cancer in the evaluation object having diabetes It is possible to provide highly reliable information that can be used as a reference in knowing the state of the device.
  • FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
  • FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment.
  • FIG. 3 is a diagram illustrating an example of the overall configuration of the present system.
  • FIG. 4 is a diagram showing another example of the overall configuration of the present system.
  • FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of this system.
  • FIG. 6 is a diagram showing an example of information stored in the density data file 106a.
  • FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b.
  • FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c.
  • FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1.
  • FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106e.
  • FIG. 11 is a block diagram illustrating a configuration of the evaluation unit 102d.
  • FIG. 12 is a block diagram illustrating an example of the configuration of the client device 200 of the present system.
  • FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system.
  • FIG. 14 shows from the area under the top 100 ROC curves obtained for a combination of two amino acids to the area under the top 100 ROC curves obtained for a combination of six amino acids. It is a figure which shows the result of having compared the area under the ROC curve obtained with respect to the existing formula group which consists of 200 multivariate discriminants described in 1 (International Publication 2014/084290).
  • FIG. 14 shows from the area under the top 100 ROC curves obtained for a combination of two amino acids to the area under the top 100 ROC curves obtained for a combination of six amino acids. It is a figure which shows the result of having
  • FIG. 15 is a diagram showing a list of combinations of three kinds of amino acids included in the new formula.
  • FIG. 16 is a diagram showing a list of combinations of four amino acids included in the new formula.
  • FIG. 17 is a diagram showing a list of combinations of five amino acids included in the new formula.
  • FIG. 18 is a diagram showing a list of combinations of six amino acids included in the new formula.
  • FIG. 19 is a diagram showing a list of combinations of two kinds of amino acids included in the new formula using combinations of three kinds of amino acids.
  • FIG. 16 is a diagram showing a list of combinations of four amino acids included in the new formula.
  • FIG. 17 is a diagram showing a list of combinations of five amino acids included in the new formula.
  • FIG. 18 is a diagram showing a list of combinations of six amino acids included in the new formula.
  • FIG. 19 is a diagram showing a list of combinations of two kinds of amino acids included in the new formula using combinations of three kinds of amino acids.
  • FIG. 20 is a list of combinations of two amino acids included in the new formula using a combination of four amino acids, a list of combinations of two amino acids included in the new formula using a combination of five amino acids, and 6 It is a figure which shows the list of the combination of 2 types of amino acids contained in the new type
  • FIG. 21 is a diagram showing a list of combinations of three amino acids included in the new formula using combinations of three amino acids and a list of combinations of three amino acids included in the new formula using combinations of four amino acids. It is.
  • FIG. 22 is a diagram showing a list of combinations of three amino acids included in the new formula using combinations of five amino acids and a list of combinations of three amino acids included in the new formula using combinations of six amino acids. It is.
  • Embodiment (1st Embodiment) of the evaluation method and calculation method concerning this invention and the evaluation apparatus, calculation device, evaluation method, calculation method, evaluation program, calculation program, evaluation system, and terminal concerning this invention
  • An apparatus embodiment (second embodiment) will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.
  • FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
  • concentration data relating to concentration values of at least two of the 19 kinds of amino acids in blood (for example, plasma, serum, etc.) collected from an evaluation subject (for example, an individual such as an animal or a human) having diabetes is acquired. (Step S11).
  • step S11 density data measured by a company or the like that performs density value measurement may be acquired.
  • concentration data may be acquired by measuring concentration values from blood collected from an evaluation object by, for example, the following measurement method (A), (B), or (C).
  • the unit of the concentration value may be, for example, a molar concentration, a weight concentration, or an enzyme activity, and may be obtained by adding / subtracting / dividing an arbitrary constant to / from these concentrations.
  • A The collected blood sample is collected in a tube treated with EDTA-2Na, and plasma is separated from the blood by centrifuging the tube. All plasma samples are stored frozen at ⁇ 80 ° C. until the concentration value is measured.
  • sulfosalicylic acid is added and protein removal treatment is performed by adjusting the concentration to 3%, and then the concentration value is analyzed with an amino acid analyzer based on the principle of high-performance liquid chromatography (HPCL) using a ninhydrin reaction in a post column.
  • HPCL high-performance liquid chromatography
  • Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are stored frozen at ⁇ 80 ° C. until the concentration value is measured.
  • sulfosalicylic acid is added to remove the protein, and then the concentration value is analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
  • the collected blood sample is subjected to blood cell separation using a membrane, MEMS technology, or the principle of centrifugation to separate plasma or serum from the blood.
  • Plasma or serum samples that are not measured immediately after plasma or serum are obtained are stored frozen at ⁇ 80 ° C. until the concentration is measured.
  • the concentration value is analyzed by quantifying a substance that increases or decreases by substrate recognition or a spectroscopic value using a molecule that reacts with or binds to a target blood substance such as an enzyme or an aptamer.
  • step S12 the state of pancreatic cancer is evaluated for the evaluation target using the concentration values of at least two of the 19 kinds of amino acids contained in the concentration data acquired in step S11 (step S12).
  • evaluating the state means, for example, examining the current state.
  • the concentration data of the evaluation target is acquired in step S11, and in step S12, the concentration data of the evaluation target acquired in step S11 is included in the 19 kinds of amino acids.
  • Use at least two concentration values to evaluate the status of pancreatic cancer for the evaluation target (in short, it can be useful for knowing information for evaluating the status of pancreatic cancer in the evaluation target or the status of pancreatic cancer in the evaluation target) Get reliable information). Accordingly, it is possible to provide information for evaluating the state of pancreatic cancer in an evaluation subject having diabetes or highly reliable information that can be used as a reference in knowing the state of pancreatic cancer in an evaluation subject having diabetes.
  • the concentration value of at least two of the 19 kinds of amino acids reflects the state of pancreatic cancer in the evaluation target
  • the concentration value is converted by, for example, the following methods
  • the converted value may be determined to reflect the state of pancreatic cancer in the evaluation target.
  • the concentration value or the converted value itself may be treated as an evaluation result regarding the state of pancreatic cancer in the evaluation target.
  • the possible range of the density value is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0 to
  • a predetermined range for example, exponential conversion, logarithmic conversion, Conversion by angle conversion, square root conversion, probit conversion, reciprocal conversion, Box-Cox conversion, power conversion, etc., and by combining these calculations for density values, the density values are converted. May be.
  • the value of an exponential function with the concentration value as an index and the Napier number as the base may exceed a predetermined state (for example, there is a possibility of suffering from pancreatic cancer)
  • a predetermined state for example, there is a possibility of suffering from pancreatic cancer
  • a value obtained by dividing the calculated exponential function value by the sum of 1 and the value may be further calculated.
  • the density value may be converted so that the value after conversion under a specific condition becomes a specific value.
  • the density value may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0. Further, for each metabolite and each amino acid, the concentration distribution may be converted into a normal distribution and then converted into a deviation value so that the average is 50 and the standard deviation is 10. These conversions may be performed by gender or age.
  • the density value in the present specification may be the density value itself or a value after the density value is converted.
  • position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper is the concentration value of at least two of the 19 kinds of amino acids or the concentration
  • the predetermined ruler is for evaluating the state of pancreatic cancer.
  • the ruler is a ruler with a scale, and the “concentration value or a range that can be obtained after conversion, or the range.
  • a scale corresponding to the upper limit value and the lower limit value in “part of” is shown at least.
  • the predetermined mark corresponds to the density value or the value after conversion, and is, for example, a circle mark or a star mark.
  • the concentration value of at least two of the 19 kinds of amino acids is more than a predetermined value (average value ⁇ 1SD, 2SD, 3SD, N quantile, N percentile, or a cutoff value with clinical significance).
  • a concentration deviation value (a value obtained by normalizing the concentration distribution by gender for each metabolite and each amino acid and then making the deviation value so that the average is 50 and the standard deviation is 10) It may be used.
  • pancreatic cancer is evaluated for the evaluation target.
  • the state may be evaluated.
  • evaluation is performed.
  • a subject may be evaluated for pancreatic cancer status.
  • the calculated value of the expression reflects the state of pancreatic cancer in the evaluation target
  • the value of the expression is converted by, for example, the method described below, and the converted value is You may determine that it reflects the state of the pancreatic cancer in an evaluation object.
  • the value of the expression or the converted value itself may be handled as the evaluation result regarding the state of pancreatic cancer in the evaluation target.
  • the possible range of the value of the expression is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0
  • a predetermined range for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0
  • an arbitrary value is added / subtracted / divided / divided from / to the value of the expression, or the value of the expression is converted into a predetermined conversion method (for example, exponential conversion, Logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by combining these calculations on the value of the expression,
  • the value of the expression may be converted.
  • the value of an exponential function with the value of the expression as the index and the Napier number as the base may be suffering from pancreatic cancer with a predetermined state (for example, exceeding a reference value) Is further calculated as the natural logarithm ln (p / (1-p)) is equal to the value of the equation) when the probability p is defined to be high)
  • a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, the value of probability p) may be further calculated.
  • the value of the expression may be converted so that the value after conversion under a specific condition becomes a specific value.
  • the value of the equation may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0. Further, the deviation value may be converted to an average of 50 and a standard deviation of 10. These conversions may be performed by gender or age. Note that the value of the expression in this specification may be the value of the expression itself, or may be a value after converting the value of the expression.
  • the predetermined ruler is for evaluating the state of pancreatic cancer, for example, a ruler with a scale, and “the range of the value of the formula or the value after conversion, or the That is, at least a scale corresponding to the upper limit value and the lower limit value in “part of range” is shown.
  • the predetermined mark corresponds to the value of the expression or the value after conversion, and is, for example, a circle mark or a star mark.
  • the degree of possibility that the evaluation target is suffering from pancreatic cancer may be qualitatively evaluated. Specifically, “at least two concentration values of the 19 amino acids and one or more preset threshold values” or “at least two concentration values of the 19 amino acids, the 19 types
  • the degree of possibility that the subject to be evaluated suffers from pancreatic cancer using an expression including a variable into which the concentration value of at least two of the amino acids is substituted and one or more preset threshold values ” May be classified into any one of a plurality of categories defined in consideration of at least.
  • categories for example, in the examples) for belonging to subjects that are highly likely to have pancreatic cancer (for example, subjects considered to be suffering from pancreatic cancer).
  • Rank C described classification for belonging to a subject having a low possibility of suffering from pancreatic cancer (for example, a subject regarded as not suffering from pancreatic cancer) (for example, described in the Examples)
  • a category for example, rank B described in the examples
  • the plurality of categories include a category for belonging to a subject having a high possibility of suffering from pancreatic cancer (for example, the pancreatic cancer category described in Examples), and the like.
  • Category for assigning a subject having a low possibility of being belonging for example, healthy category for assigning a subject having a high possibility of being healthy (for example, a subject considered to be healthy) described in the examples) ) May be included.
  • the density value or the expression value may be converted by a predetermined method, and the evaluation target may be classified into any one of a plurality of categories using the converted value.
  • the form used for the evaluation is not particularly limited, but for example, the following form may be used.
  • Linear models such as multiple regression, linear discriminant, principal component analysis, canonical discriminant analysis based on least square method
  • Generalized linear model such as logistic regression based on maximum likelihood method, Cox regression
  • Generalized linear mixed models that take into account random effects such as inter-individual differences, inter-facility differences, formulas created by cluster analysis such as K-means method, hierarchical cluster analysis, MCMC (Markov chain Monte Carlo method), Bayesian network, Formulas created based on Bayesian statistics such as Hierarchical Bayes method, formulas created by class classification such as support vector machines and decision trees, formulas created by methods not belonging to the above categories such as fractional formulas, sums of formulas of different formats Formula as shown in
  • the formula used in the evaluation is described in, for example, the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. You may create by the method.
  • the formula can be suitably used to evaluate the state of pancreatic cancer regardless of the unit of the amino acid concentration value in the concentration data as input data. .
  • a coefficient and a constant term are added to each variable.
  • the coefficient and the constant term are preferably real numbers, and more preferably May be any value belonging to the range of the 99% confidence interval of the coefficient and constant term obtained for performing the various classifications from the data, and more preferably, the value obtained for performing the various classifications from the data. Any value may be used as long as it falls within the 95% confidence interval of the obtained coefficient and constant term.
  • the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / subtracting an arbitrary real constant thereto.
  • the fractional expression means that the numerator of the fractional expression is represented by the sum of the variables A, B, C,... And / or the denominator of the fractional expression is the sum of the variables a, b, c,. It is represented by
  • the fractional expression includes a sum of fractional expressions ⁇ , ⁇ , ⁇ ,.
  • the fractional expression also includes a divided fractional expression. Note that each variable used in the numerator and denominator may have an appropriate coefficient. The variables used for the numerator and denominator may overlap. Further, an appropriate coefficient may be attached to each fractional expression. Further, the value of the coefficient of each variable and the value of the constant term may be real numbers.
  • the fractional expression includes one in which the numerator variable and the denominator variable are interchanged.
  • Albumin total protein, triglyceride (neutral fat), HbA1c, glycated albumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid, GOT (AST), GPT (ALT), GGTP ( ⁇ -GTP), glucose (blood glucose level), CRP (C-reactive protein), red blood cell, hemoglobin, hematocrit, MCV, MCH, MCHC, white blood cell, platelet count, etc.
  • FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment.
  • the description overlapping the first embodiment described above may be omitted.
  • the case of using the value of the formula or the value after the conversion when evaluating the state of pancreatic cancer is described as an example.
  • the concentration of at least two of the 19 kinds of amino acids is described.
  • a value or a value after the conversion (for example, a density deviation value) may be used.
  • the control unit includes the 19 types included in the concentration data acquired in advance regarding the concentration values of at least two of the 19 types of amino acids in the blood of an evaluation target (for example, an individual such as an animal or a human) having diabetes.
  • the value of the expression is calculated using the expression stored in advance in the storage unit including the concentration value to which at least two concentration values of the amino acids of the above and the concentration value of at least two of the 19 kinds of amino acids are substituted.
  • the state of pancreatic cancer is evaluated for the evaluation target (step S21).
  • step S21 may be created based on formula creation processing (step 1 to step 4) described below.
  • formula creation processing step 1 to step 4
  • an overview of the formula creation process will be described. Note that the processing described here is merely an example, and the method of creating an expression is not limited to this.
  • the control unit previously stores index state information (data having missing values, outliers, etc., previously stored in the storage unit, including concentration data and index data relating to an index representing the state of pancreatic cancer).
  • index state information data having missing values, outliers, etc.
  • concentration data data relating to an index representing the state of pancreatic cancer.
  • step 1 multiple different formula creation methods (principal component analysis and discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, determination from index state information
  • a plurality of candidate expressions may be created using a combination of multivariate analysis such as trees).
  • multivariate data composed of concentration data and index data obtained by analyzing blood obtained from a large number of diabetic groups not suffering from pancreatic cancer and a large number of pancreatic cancer groups suffering from diabetes
  • a plurality of groups of candidate formulas may be created simultaneously in parallel using a plurality of different algorithms.
  • discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate formulas.
  • the candidate formulas may be created by converting index state information using candidate formulas created by performing principal component analysis and performing discriminant analysis on the converted index status information. As a result, it is possible to finally create an optimum expression for evaluation.
  • the candidate formula created using the principal component analysis is a linear formula including each variable that maximizes the variance of all density data.
  • Candidate formulas created using discriminant analysis are high-order formulas (including exponents and logarithms) that contain variables that minimize the ratio of the sum of variances within each group to the variance of all concentration data. is there.
  • the candidate formula created using the support vector machine is a high-order formula (including a kernel function) including variables that maximize the boundary between groups.
  • the candidate formula created using the multiple regression analysis is a high-order formula including each variable that minimizes the sum of the distances from all density data.
  • the candidate formula created using Cox regression analysis is a linear model including a log hazard ratio, and is a linear expression including each variable and its coefficient that maximize the likelihood of the model.
  • the candidate formula created using logistic regression analysis is a linear model that represents log odds of probability, and is a linear formula that includes each variable that maximizes the likelihood of the probability.
  • k-means method k neighborhoods of each density data are searched, the largest group among the groups to which the neighboring points belong is defined as the group to which the data belongs, and the group to which the input density data belongs. This is a method for selecting a variable that best matches the group defined as.
  • Cluster analysis is a technique for clustering (grouping) points that are closest to each other in all density data. Further, the decision tree is a technique for predicting a group of density data from patterns that can be taken by variables with higher ranks by adding ranks to the variables.
  • the control unit verifies (mutually verifies) the candidate formula created in step 1 based on a predetermined verification method (step 2).
  • Candidate expressions are verified for each candidate expression created in step 1.
  • the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (candidate expression of candidate formulas are determined based on at least one of the bootstrap method, holdout method, N-fold method, leave one-out method, and the like. It may be verified with respect to at least one of the area under the receiver characteristic curve).
  • the discrimination rate is an evaluation method according to the present embodiment, and an evaluation object whose true state is negative (for example, an evaluation object not suffering from pancreatic cancer) is correctly evaluated as negative, and the true state Is a rate at which an evaluation target (for example, an evaluation target suffering from pancreatic cancer) is correctly evaluated as positive.
  • Sensitivity is the rate at which an evaluation object whose true state is positive is correctly evaluated as positive in the evaluation method according to the present embodiment.
  • the specificity is a rate at which an evaluation object whose true state is negative is correctly evaluated as negative in the evaluation method according to the present embodiment.
  • the Akaike Information Criterion is a standard that expresses how closely the observed data matches the statistical model in the case of regression analysis, etc., and is expressed as “ ⁇ 2 ⁇ (maximum log likelihood of statistical model) + 2 ⁇ (statistics).
  • the model having the smallest value defined by “the number of free parameters of the model)” is determined to be the best.
  • the value of 1 is 1 in complete discrimination, and the closer this value is to 1, the higher the discriminability.
  • the predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of candidate formulas.
  • Robustness is the variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate formulas.
  • the control unit selects a combination of density data included in the index state information used when creating a candidate formula by selecting a variable of the candidate formula based on a predetermined variable selection method.
  • the selection of variables may be performed for each candidate formula created in step 1. Thereby, the variable of a candidate formula can be selected appropriately.
  • Step 1 is executed again using the index state information including the density data selected in Step 3.
  • the candidate expression variable may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result in step 2.
  • the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index given by the candidate formula.
  • the control unit repeatedly executes the above-described step 1, step 2, and step 3, and based on the verification results accumulated thereby, candidates to be used for evaluation from a plurality of candidate formulas By selecting an expression, an expression used for evaluation is created (step 4).
  • the selection of candidate formulas includes, for example, selecting an optimal formula from candidate formulas created by the same formula creation method and selecting an optimal formula from all candidate formulas.
  • FIGS. 3 to 14 the configuration of an evaluation system according to the second embodiment (hereinafter may be referred to as the present system) will be described with reference to FIGS. 3 to 14.
  • This system is merely an example, and the present invention is not limited to this.
  • the case of using the value of the formula or the value after the conversion when evaluating the state of pancreatic cancer is described as an example.
  • the concentration of at least two of the 19 kinds of amino acids is described.
  • a value or a value after the conversion (for example, a density deviation value) may be used.
  • FIG. 3 is a diagram showing an example of the overall configuration of the present system.
  • FIG. 4 is a diagram showing another example of the overall configuration of the present system.
  • the present system includes an evaluation apparatus 100 that evaluates the state of pancreatic cancer for an individual to be evaluated, and individual concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood.
  • a client device 200 (corresponding to a terminal device of the present invention) that provides communication via a network 300.
  • the client device 200 that is a provider of data used for evaluation and the client device 200 that is a provider of evaluation results may be different.
  • this system stores a database apparatus that stores index state information used when creating an expression in the evaluation apparatus 100, an expression used during evaluation, and the like in addition to the evaluation apparatus 100 and the client apparatus 200.
  • 400 may be configured to be communicably connected via the network 300.
  • the information that is useful for knowing the state of pancreatic cancer is, for example, information about values measured for specific items related to the state of pancreatic cancer in organisms including humans.
  • information that is useful for knowing the state of pancreatic cancer is generated by the evaluation apparatus 100, the client apparatus 200, and other apparatuses (for example, various measuring apparatuses) and is mainly stored in the database apparatus 400.
  • FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
  • the evaluation device 100 includes a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in an integrated manner, a communication device such as a router, and a wired or wireless communication line such as a dedicated line.
  • the communication interface unit 104 that is communicably connected to the network 300, the storage unit 106 that stores various databases, tables, and files, and the input / output interface unit 108 that is connected to the input device 112 and the output device 114 are configured. These units are communicably connected via an arbitrary communication path.
  • the evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analysis apparatus).
  • a small analysis having a configuration (hardware and software) that calculates (measures) concentration values of at least two of the 19 amino acids in blood and outputs the calculated values (printing, monitor display, etc.)
  • the apparatus may further include an evaluation unit 102d to be described later, and output a result obtained by the evaluation unit 102d using the above configuration.
  • the communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.
  • the input / output interface unit 108 is connected to the input device 112 and the output device 114.
  • a monitor including a home television
  • a speaker or a printer can be used as the output device 114 (hereinafter, the output device 114 may be described as the monitor 114).
  • the input device 112 a monitor that realizes a pointing device function in cooperation with a mouse can be used in addition to a keyboard, a mouse, and a microphone.
  • the storage unit 106 is a storage unit, and for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.
  • the storage unit 106 stores a computer program for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System). As illustrated, the storage unit 106 stores a density data file 106a, an index state information file 106b, a designated index state information file 106c, an expression related information database 106d, and an evaluation result file 106e.
  • the concentration data file 106a stores concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood.
  • FIG. 6 is a diagram showing an example of information stored in the density data file 106a.
  • the information stored in the density data file 106a is configured by associating an individual number for uniquely identifying an individual (sample) to be evaluated with density data.
  • the density data is handled as a numerical value, that is, a continuous scale, but the density data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.
  • values related to other biological information may be combined with the density data.
  • the index state information file 106b stores the index state information used when creating the formula.
  • FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b.
  • the information stored in the index state information file 106b includes an individual number and index data (T) related to an index (index T1, index T2, index T3,...) Indicating the state of pancreatic cancer.
  • the density data is associated with each other.
  • the index data and the density data are handled as numerical values (that is, continuous scales), but the index data and the density data may be nominal scales or order scales. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.
  • the index data is a known index that serves as a marker of pancreatic cancer status, and numerical data may be used.
  • the designated index state information file 106c stores the index state information designated by the designation unit 102b described later.
  • FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. As shown in FIG. 8, the information stored in the designated index state information file 106c is configured by associating an individual number, designated index data, and designated density data with each other.
  • the formula related information database 106d includes a formula file 106d1 that stores formulas created by a formula creation unit 102c described later.
  • the expression file 106d1 stores expressions used for evaluation.
  • FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. As shown in FIG. 9, the information stored in the expression file 106d1 includes the rank, the expression (in FIG. 9, Fp (Phe,%), Fp (Gly, Leu, Phe), Fk (Gly, Leu, Phe,...)), A threshold value corresponding to each formula creation method, and a verification result of each formula (for example, the value of each formula) are associated with each other.
  • FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106d.
  • Information stored in the evaluation result file 106d includes an individual number for uniquely identifying an individual (sample) to be evaluated, concentration data of the individual acquired in advance, and an evaluation result regarding the state of pancreatic cancer (for example, described later)
  • the value of the formula calculated by the calculation unit 102d1 the value after converting the value of the formula by the conversion unit 102d2 described later, the position information generated by the generation unit 102d3 described later, or the classification obtained by the classification unit 102d4 described later Results
  • the like the like.
  • control unit 102 has an internal memory for storing a control program such as an OS, a program that defines various processing procedures, and necessary data, and various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a reception unit 102a, a specification unit 102b, an expression creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f.
  • the control unit 102 removes data with missing values, removes data with many outliers, and has data with missing values from the index state information sent from the database device 400 and the density data sent from the client device 200. Data processing such as removal of many variables is also performed.
  • the receiving unit 102a may receive information (specifically, concentration data, index state information, formulas, etc.) transmitted from the client device 200 or the database device 400 via the network 300 or the like.
  • the receiving unit 102a may receive data used for evaluation transmitted from a client device 200 different from the client device 200 that is the transmission destination of the evaluation result.
  • the designating unit 102b designates index data and density data that are targets for creating an expression.
  • the formula creating unit 102c creates a formula based on the index state information received by the receiving unit 102a and the index state information specified by the specifying unit 102b. Note that if the formula is stored in a predetermined storage area of the storage unit 106 in advance, the formula creation unit 102 c may create the formula by selecting a desired formula from the storage unit 106. The formula creation unit 102c may create a formula by selecting and downloading a desired formula from another computer device (for example, the database device 400) that stores the formula in advance.
  • another computer device for example, the database device 400
  • the evaluation unit 102d is a formula obtained in advance (for example, a formula created by the formula creation unit 102c or a formula received by the reception unit 102a), and concentration data of an individual having diabetes received by the reception unit 102a.
  • the state of pancreatic cancer is evaluated for an individual by calculating the value of the equation using the concentration values of at least two of the 19 kinds of amino acids included in the above.
  • the evaluation unit 102d may evaluate the state of pancreatic cancer for an individual using at least two concentration values of the 19 kinds of amino acids or a converted value of the concentration values (for example, concentration deviation value). Good.
  • FIG. 11 is a block diagram showing a configuration of the evaluation unit 102d, and conceptually shows only a portion related to the present invention.
  • the evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.
  • the calculation unit 102d1 uses an expression including at least two concentration values of the 19 kinds of amino acids and a variable into which at least two concentration values of the 19 kinds of amino acids are substituted. Is calculated. Note that the evaluation unit 102d may store the value of the expression calculated by the calculation unit 102d1 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1 using, for example, the conversion method described above.
  • the evaluation unit 102d may store the value after the conversion by the conversion unit 102d2 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the conversion unit 102d2 may convert at least two concentration values of the 19 kinds of amino acids included in the concentration data by, for example, the conversion method described above.
  • the generation unit 102d3 uses the value of the expression calculated by the calculation unit 102d1 or the conversion unit 102d2 for the position information related to the position of the predetermined mark on the predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper. It is generated using the value after conversion in (which may be a density value or a value after conversion of the density value).
  • the evaluation unit 102d may store the position information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
  • the classification unit 102d4 uses an expression value calculated by the calculation unit 102d1 or a value after conversion by the conversion unit 102d2 (which may be a concentration value or a value after conversion of the concentration value) to cause an individual to suffer from pancreatic cancer. And classifying it into any one of a plurality of categories defined in consideration of at least the degree of the possibility of being performed.
  • the result output unit 102e outputs the processing result (including the evaluation result obtained by the evaluation unit 102d) in each processing unit of the control unit 102 to the output device 114.
  • the transmission unit 102f transmits the evaluation result to the client device 200 that is the transmission source of the individual concentration data, or transmits the formula or evaluation result created by the evaluation device 100 to the database device 400. Note that the transmission unit 102f may transmit the evaluation result to a client device 200 different from the client device 200 that is a transmission source of data used for evaluation.
  • FIG. 12 is a block diagram showing an example of the configuration of the client apparatus 200 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
  • the client device 200 includes a control unit 210, a ROM 220, an HD (Hard Disk) 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280. These units are connected via an arbitrary communication path. Are connected to communicate.
  • the client device 200 is an information processing device in which peripheral devices such as a printer, a monitor, and an image scanner are connected as necessary (for example, a known personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone System) It may be based on a terminal, a portable terminal, a mobile communication terminal, an information processing terminal such as PDA (Personal Digital Assistant), or the like.
  • the input device 250 is a keyboard, a mouse, a microphone, or the like.
  • a monitor 261 which will be described later, also realizes a pointing device function in cooperation with the mouse.
  • the output device 260 is an output unit that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker or the like.
  • the input / output IF 270 is connected to the input device 250 and the output device 260.
  • the communication IF 280 connects the client device 200 and the network 300 (or a communication device such as a router) so that they can communicate with each other.
  • the client device 200 is connected to the network 300 via a communication device such as a modem, a TA (Terminal Adapter), or a router, and a telephone line, or via a dedicated line.
  • the client apparatus 200 can access the evaluation apparatus 100 according to a predetermined communication protocol.
  • the control unit 210 includes a reception unit 211 and a transmission unit 212.
  • the receiving unit 211 receives various types of information such as an evaluation result transmitted from the evaluation device 100 via the communication IF 280.
  • the transmission unit 212 transmits various types of information such as individual concentration data to the evaluation apparatus 100 via the communication IF 280.
  • the control unit 210 may be realized by a CPU and a program that is interpreted and executed by the CPU and all or any part of the processing performed by the control unit.
  • the ROM 220 or the HD 230 stores computer programs for giving instructions to the CPU in cooperation with the OS and performing various processes.
  • the computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU.
  • the computer program may be recorded in an application program server connected to the client apparatus 200 via an arbitrary network, and the client apparatus 200 may download all or a part thereof as necessary.
  • all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.
  • control unit 210 includes an evaluation unit 210a (a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100. ) May be provided.
  • evaluation unit 210a a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4 having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100.
  • the evaluation part 210a is based on the information contained in the evaluation result transmitted from the evaluation apparatus 100, and the value of a formula (in the conversion part 210a2) ( A density value), or position information corresponding to an expression value or a converted value (which may be a density value or a value after conversion of the density value) is generated by the generation unit 210a3, or a classification unit 210a4
  • the individual may be classified into any one of a plurality of categories using the value of the expression or the value after conversion (which may be the density value or the value after conversion of the density value).
  • the network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 so that they can communicate with each other.
  • the Internet for example, the Internet, an intranet, a LAN (Local Area Network) (including both wired and wireless), and the like It is.
  • LAN Local Area Network
  • the network 300 includes a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog / digital), a dedicated line network (including both analog / digital), CATV ( Community Antenna Television (PD) network, mobile circuit switching network or mobile packet switching network (IMT (International Mobile Telecommunication) 2000 system, GSM (Registered Trademark) Mobile Communications-PDC (PDC)) System), wireless paging networks, and local wireless networks such as Bluetooth (registered trademark) , Or PHS network, satellite communication network (CS (Communication Satellite), BS (Broadcasting Satellite) or ISDB (including Integrated Services Digital Broadcasting), etc.) may be like.
  • VAN Value-Added Network
  • a personal computer communication network including both analog / digital
  • a public telephone network including both analog / digital
  • a dedicated line network including both analog / digital
  • CATV Community Antenna Television (PD) network
  • IMT International Mobile Telecommunication 2000 system
  • GSM Registered Trademark
  • FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system, and conceptually shows only the portion related to the present invention in the configuration.
  • the database apparatus 400 has a function of storing index state information used when creating an expression in the evaluation apparatus 100 or the database apparatus, an expression created in the evaluation apparatus 100, an evaluation result in the evaluation apparatus 100, and the like.
  • the database apparatus 400 includes a control unit 402 such as a CPU that controls the database apparatus in an integrated manner, a communication apparatus such as a router, and a wired or wireless communication circuit such as a dedicated line.
  • a communication interface unit 404 that connects the apparatus to the network 300 to be communicable, a storage unit 406 that stores various databases, tables, and files (for example, files for Web pages), and an input unit that connects to the input unit 412 and the output unit 414.
  • the output interface unit 408 is configured to be communicable via an arbitrary communication path.
  • the storage unit 406 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used.
  • the storage unit 406 stores various programs used for various processes.
  • the communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line.
  • the input / output interface unit 408 is connected to the input device 412 and the output device 414.
  • a monitor including a home television
  • a speaker or a printer can be used as the output device 414.
  • the input device 412 can be a monitor that realizes a pointing device function in cooperation with the mouse.
  • the control unit 402 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, required data, and the like, and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into a transmission unit 402a and a reception unit 402b.
  • the transmission unit 402a transmits various types of information such as index state information and formulas to the evaluation apparatus 100.
  • the receiving unit 402b receives various types of information such as expressions and evaluation results transmitted from the evaluation device 100.
  • the evaluation apparatus 100 executes from the reception of the concentration data to the calculation of the value of the expression, the classification into the individual categories, and the transmission of the evaluation result, and the client apparatus 200 receives the evaluation result.
  • the client device 200 includes the evaluation unit 210a
  • conversion of the value of the expression, position information The generation and the classification into individual sections may be appropriately shared by the evaluation apparatus 100 and the client apparatus 200.
  • the evaluation unit 210a converts the value of the expression in the conversion unit 210a2, or the value of the expression or the value after conversion in the generation unit 210a3.
  • the classification unit 210a4 may classify the individual into one of a plurality of categories using the value of the expression or the value after conversion. Further, when the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a generates position information corresponding to the converted value in the generation unit 210a3, or converts it in the classification unit 210a4. An individual may be classified into any one of a plurality of divisions using a later value. When the client device 200 receives the value of the expression or the value after conversion and the position information from the evaluation device 100, the evaluation unit 210a uses the value of the expression or the value after conversion in the classification unit 210a4. The individual may be classified into any one of a plurality of sections.
  • the evaluation device, the calculation device, the evaluation method, the calculation method, the evaluation program, the calculation program, the evaluation system, and the terminal device according to the present invention have the technical idea described in the claims in addition to the second embodiment described above. It may be implemented in a variety of different embodiments within the scope.
  • each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.
  • all or some of the processing functions provided in the evaluation apparatus 100 may be realized by the CPU and a program interpreted and executed by the CPU. Alternatively, it may be realized as hardware by wired logic.
  • the program is recorded on a non-transitory computer-readable recording medium including programmed instructions for causing the information processing apparatus to execute the evaluation method according to the present invention, and is stored in the evaluation apparatus 100 as necessary. Read mechanically. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106 such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU.
  • this computer program may be stored in an application program server connected to the evaluation apparatus 100 via an arbitrary network, and the whole or a part of the computer program can be downloaded as necessary.
  • the evaluation program according to the present invention may be stored in a computer-readable recording medium that is not temporary, and may be configured as a program product.
  • the “recording medium” refers to a memory card, USB (Universal Serial Bus) memory, SD (Secure Digital) card, flexible disk, magneto-optical disk, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electric Electric). Erasable and Programmable Read Only Memory (registered trademark), CD-ROM (Compact Disc Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Register, etc.) Any “possible It is intended to include physical medium "of use.
  • the “program” is a data processing method described in an arbitrary language or description method, and may be in the form of source code or binary code. Note that the “program” is not necessarily limited to a single configuration, and functions are achieved in cooperation with a separate configuration such as a plurality of modules and libraries or a separate program represented by the OS. Including things. In addition, a well-known structure and procedure can be used about the specific structure and reading procedure for reading a recording medium in each apparatus shown to embodiment, the installation procedure after reading, etc.
  • Various databases and the like stored in the storage unit 106 are storage devices such as a memory device such as a RAM and a ROM, a fixed disk device such as a hard disk, a flexible disk, and an optical disk. Programs, tables, databases, web page files, and the like.
  • the evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured as the information processing apparatus connected to an arbitrary peripheral device.
  • the evaluation apparatus 100 may be realized by installing software (including a program or data) that causes the information processing apparatus to realize the evaluation method of the present invention.
  • the specific form of distribution / integration of the devices is not limited to that shown in the figure, and all or a part of them may be functionally or physically in arbitrary units according to various additions or according to functional loads. It can be configured to be distributed and integrated. That is, the above-described embodiments may be arbitrarily combined and may be selectively implemented.
  • FIG. 14 shows from the area under the top 100 ROC curves obtained for a combination of two amino acids to the area under the top 100 ROC curves obtained for a combination of six amino acids. It is a figure which shows the result of having compared the area under the ROC curve obtained with respect to the existing formula group which consists of 200 multivariate discriminants described in 1 (International Publication 2014/084290).
  • the blood amino acid concentration data measured in this example was analyzed using the existing formula group to obtain the area under the ROC curve, and the maximum value was 0.873. Then, a new formula using a combination of two amino acids whose area under the ROC curve exceeds the maximum value was examined based on a combination of two amino acids corresponding to the top 100. As a result, a new formula using a combination of Ser and His having an area under the ROC curve of 0.877 was detected.
  • the scope of study was expanded to new formulas using combinations of amino acids, and for each of these new formulas, the number of combinations of two types of amino acids contained in the formula was counted.
  • Example 1 The sample data used in Example 1 was used. Multiple logistic regression was performed on all combinations from 3 amino acids to 6 amino acids extracted in Example 1. And the discrimination performance regarding 2 group discrimination
  • the number of combinations of three amino acids contained in the formula was counted.
  • the present invention can be widely implemented in many industrial fields, in particular, in fields such as pharmaceuticals, foods, and medical care, and in particular, prediction of progression of pancreatic cancer status and disease risk prediction in diabetic patients. It is extremely useful in the field of bioinformatics for proteome and metabolome analysis.

Abstract

The present invention addresses the problem of providing an evaluation method, etc., capable of providing highly reliable information that is potentially helpful for discovering a state of pancreatic cancer in an evaluation subject having diabetes. In the present embodiment, a state of pancreatic cancer is evaluated for an evaluation subject having diabetes using the concentration value of at least two of Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, and Arg in the blood of the evaluation subject.

Description

糖尿病患者における膵臓癌の評価方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、評価システム、及び端末装置Pancreatic cancer evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device for pancreatic cancer in diabetic patients
　本発明は、血液中のアミノ酸濃度を利用した糖尿病患者における膵臓癌の評価方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、評価システム、及び端末装置に関するものである。 The present invention relates to a pancreatic cancer evaluation method, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device for diabetic patients using amino acid concentrations in blood.
　膵臓癌は、日本では癌死因の第五位、米国では癌死因の第四位である。膵臓癌は、癌の部位によっては症状が乏しく、進行してから発見されることが多い。膵臓癌は、画像診断を用いて２ｃｍ以下で発見されても膵臓外の隣接組織への転移がある場合が多く、切除不能となれば化学療法を行っても予後は極めて不良である。膵臓癌全体の５年生存率は５％程度であるが、一方で手術可能な１ｃｍ以下の小膵癌については５７％の５年生存率が期待できることから、手術可能なより早期の発見が望まれている。 Pancreatic cancer is the fifth leading cause of cancer death in Japan and the fourth leading cause of cancer death in the United States. Pancreatic cancer has few symptoms depending on the site of cancer and is often discovered after it has progressed. Pancreatic cancer often has metastasis to adjacent tissues outside the pancreas even if it is detected at 2 cm or less using diagnostic imaging, and if it cannot be excised, the prognosis is very poor even if chemotherapy is performed. The overall 5-year survival rate for pancreatic cancer is about 5%. On the other hand, for a small pancreatic cancer of 1 cm or less that can be operated, a 5-year survival rate of 57% can be expected. ing.
　膵臓癌の診断には腹部超音波エコー、ＣＴ及びＭＲＩが用いられるが、いずれも膵臓癌の発見率は高くない。近年では、ＥＲＣＰ及びＥＵＳなどの内視鏡を用いた画像診断も普及し、膵臓癌の発見率が高く有効であることが知られているが、患者の身体的負担が高く、検査による出血などのリスクも起こりえる。さらに、生検による組織診断は、確定診断になるが侵襲度の高い検査であり、生検による検査をスクリーニングの段階で施行するのは実際的でない。 Diagnosis of pancreatic cancer uses abdominal ultrasound echo, CT and MRI, but none of them has a high discovery rate of pancreatic cancer. In recent years, image diagnosis using endoscopes such as ERCP and EUS has become widespread, and it is known that the detection rate of pancreatic cancer is high and effective, but the physical burden on patients is high, bleeding due to examinations, etc. Risk can also occur. Furthermore, tissue diagnosis by biopsy is a definitive diagnosis but a highly invasive test, and it is not practical to perform a biopsy test at the screening stage.
　また、血清癌マーカーとしてはＣＡ１９－９、ＣＥＡ、ＳＰａｎ－１及びＤＵＰＡＮ－２等がある。これらのマーカーは、進行癌には比較的高い感度と特異度を有するが、初期癌における陽性率は低く、また膵臓癌以外の癌でも陽性になる場合がある。 Serum cancer markers include CA19-9, CEA, SPAN-1, and DUPAN-2. These markers have relatively high sensitivity and specificity for advanced cancer, but have a low positive rate in early cancers and may be positive in cancers other than pancreatic cancer.
　加えて、膵臓癌は他の癌腫に比べて相対的に罹患率が低いこともあり、膵臓癌に関しては、一般人を対象とした癌検診として確立された方法がない。膵臓癌発症の可能性の高い高危険群を絞り込み、絞り込まれた被験者に対してより高度な膵臓癌診断を実施することで、効率的に膵臓癌の発見につなげることが、患者に対する身体的負担および費用対効果の面から望まれる。 In addition, pancreatic cancer has a relatively low morbidity compared to other carcinomas, and pancreatic cancer has no established method for cancer screening for the general public. By narrowing down high-risk groups with a high probability of developing pancreatic cancer and conducting more advanced pancreatic cancer diagnosis for the selected subjects, it is possible to efficiently find pancreatic cancer. And cost-effective.
　膵臓癌の危険因子としては、糖尿病、肥満、喫煙、膵癌家族歴、慢性膵炎、ＩＰＭＮ（ｉｎｔｒａｄｕｃｔａｌ　ｐａｐｉｌｌａｒｙ　ｍｕｃｉｎｏｕｓ　ｎｅｏｐｌａｓｍｓ）や嚢胞といった膵臓所見が挙げられている。これらの危険因子のうち特に糖尿病に関しては、糖尿病を契機として膵臓癌を発見する意義について検討されており、非特許文献１にて勧告や提言がされている。 Examples of risk factors for pancreatic cancer include diabetes, obesity, smoking, family history of pancreatic cancer, chronic pancreatitis, pancreatic findings such as IPMN (intraductive capillary mucinous neoplasms) and cysts. Among these risk factors, particularly with respect to diabetes, the significance of finding pancreatic cancer with diabetes as an opportunity has been studied, and Non-Patent Document 1 provides recommendations and recommendations.
　糖尿病を対象としたスクリーニングの問題点としては、糖尿病罹患集団は一般健診集団に比べれば一定の絞り込みができるものの、対象人数としてはまだ多く、さらなる絞り込みが必要である。糖尿病患者の中でも新規糖尿病罹患者での膵臓癌の発見率の上昇が報告される一方で、糖尿病長期罹患も膵臓癌の危険因子であると知られている。前者は膵臓癌罹患の結果としての糖尿病発症、後者は膵臓癌発症の原因としての糖尿病と分けられることが想定されるが、臨床的にこれらの高危険群を絞り込むことはできていない。また、膵臓癌のマーカーとして多く利用されるＣＡ１９－９は、血糖上昇による偽陽性が知られており、糖尿病患者からの高危険群絞り込みには不適当と考えられる。そのため、簡便に広くスクリーニングを行う方策の考案が期待されている。 ス ク リ ー ニ ン グ As a problem of screening for diabetes, although the population affected by diabetes can be narrowed down to a certain level as compared to the general medical examination group, the number of subjects is still large, and further narrowing down is necessary. While increasing the detection rate of pancreatic cancer among newly diagnosed diabetic patients, it is known that long-term diabetes is also a risk factor for pancreatic cancer. It is assumed that the former can be divided into diabetes as a result of pancreatic cancer, and the latter as diabetes as the cause of pancreatic cancer. However, these high-risk groups cannot be narrowed clinically. CA19-9, which is widely used as a marker for pancreatic cancer, is known to be falsely positive due to an increase in blood glucose, and is considered inappropriate for narrowing down high-risk groups from diabetic patients. Therefore, it is expected to devise a method for performing screening easily and widely.
　なお、先行技術として、血漿中アミノ酸の測定およびプロファイリングにより膵臓癌の高危険群を予測する検査方法が報告されている（特許文献１）。特許文献１においては、健康人の検体と膵臓癌患者の検体を比較対象とした多変量解析に基づく膵臓癌罹患状態の判別式を開示している。 As a prior art, a test method for predicting a high-risk group of pancreatic cancer by measuring amino acid in plasma and profiling has been reported (Patent Document 1). Patent Document 1 discloses a discriminant for a pancreatic cancer disease state based on multivariate analysis in which a healthy subject sample and a pancreatic cancer patient sample are compared.
　また、非特許文献２では、コホート研究の結果より、膵臓癌と診断される２～５年前から血中のアミノ酸プロファイルに変化が見られることを報告している。非特許文献２では、画像診断等で膵臓癌が顕在化する以前から、分岐鎖アミノ酸（ＢＣＡＡ）の代謝異常が起きていることが示唆されている。また、非特許文献３において、糖尿病病態に基づいてＢＣＡＡの代謝異常が発生すると知られている。 Also, Non-Patent Document 2 reports that changes in the amino acid profile in blood have been observed from 2 to 5 years before diagnosis of pancreatic cancer based on the results of cohort studies. Non-Patent Document 2 suggests that metabolic abnormalities of branched chain amino acids (BCAA) have occurred before pancreatic cancer became apparent by diagnostic imaging or the like. Further, in Non-Patent Document 3, it is known that a BCAA metabolic abnormality occurs based on a diabetic condition.
国際公開第２０１４／０８４２９０号International Publication No. 2014/084290
　しかし、特許文献１では、糖尿病患者を対象として特定した解析は行っていない。また、非特許文献３から糖尿病病態と膵臓癌の関連が想定されるが、当該文献で示された一過性のアミノ酸変化を診断に応用する具体的な手法は当該文献に明示されておらず、また、当該文献では、２つ以上の生体分子の組み合わせ、又は、多変量解析を用いた指標式等を探索しているわけではない。 However, Patent Document 1 does not perform analysis specified for diabetic patients. In addition, non-patent document 3 suggests an association between diabetes pathology and pancreatic cancer, but the specific technique for applying the transient amino acid change shown in the document to the diagnosis is not clearly described in the document. Also, in this document, a search for a combination of two or more biomolecules or an index formula using multivariate analysis is not performed.
　つまり、少なくとも２つのアミノ酸の血中濃度値が代入される少なくとも２つの変数を含む式を利用して糖尿病患者の膵臓癌の状態を評価する方法は、開発されておらず、また実用化もされていない、という問題点があった。 In other words, a method for evaluating the state of pancreatic cancer in a diabetic patient using an expression including at least two variables into which blood concentration values of at least two amino acids are substituted has not been developed and has been put into practical use. There was a problem of not.
　本発明は、上記に鑑みてなされたもので、糖尿病を有する評価対象における膵臓癌の状態を知る上で参考となり得る信頼性の高い情報を提供することができる評価方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、評価システム、及び端末装置を提供することを目的とする。 The present invention has been made in view of the above, and an evaluation method, a calculation method, an evaluation apparatus, and the like, which can provide highly reliable information that can be helpful in knowing the state of pancreatic cancer in an evaluation subject having diabetes, An object is to provide a calculation device, an evaluation program, a calculation program, an evaluation system, and a terminal device.
　本発明者らは、上記課題を解決するために鋭意検討し、対象者を糖尿病患者に限定することで、特許文献１に記載された多変量判別式群と比較して高い膵癌判別能を有するアミノ酸変数を用いた相関式（指標式）を見出し、本発明を完成するに至った。上述した課題を解決し、目的を達成するために、本発明にかかる評価方法は、糖尿病を有する評価対象の血液中の１９種類のアミノ酸（Ｔｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇ）のうちの少なくとも２つの濃度値を用いて、前記評価対象について、膵臓癌の状態を評価する評価ステップを含むこと、を特徴とする。 The present inventors have intensively studied in order to solve the above-mentioned problems, and have a high pancreatic cancer discriminating ability compared with the multivariate discriminant group described in Patent Document 1 by limiting the subject to diabetic patients. A correlation equation (index formula) using amino acid variables was found, and the present invention was completed. In order to solve the above-described problems and achieve the object, the evaluation method according to the present invention includes 19 kinds of amino acids (Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, and Arg) are used to evaluate the state of pancreatic cancer for the evaluation object. It is characterized by including.
　ここで、本明細書では各種アミノ酸を主に略称で表記するが、それらの正式名称は以下の通りである。
（略称）　（正式名称）
Ａｌａ　Ａｌａｎｉｎｅ
Ａｒｇ　Ａｒｇｉｎｉｎｅ
Ａｓｎ　Ａｓｐａｒａｇｉｎｅ
Ｃｉｔ　Ｃｉｔｒｕｌｌｉｎｅ
Ｇｌｎ　Ｇｌｕｔａｍｉｎｅ
Ｇｌｙ　Ｇｌｙｃｉｎｅ
Ｈｉｓ　Ｈｉｓｔｉｄｉｎｅ
Ｉｌｅ　Ｉｓｏｌｅｕｃｉｎｅ
Ｌｅｕ　Ｌｅｕｃｉｎｅ
Ｌｙｓ　Ｌｙｓｉｎｅ
Ｍｅｔ　Ｍｅｔｈｉｏｎｉｎｅ
Ｏｒｎ　Ｏｒｎｉｔｈｉｎｅ
Ｐｈｅ　Ｐｈｅｎｙｌａｌａｎｉｎｅ
Ｐｒｏ　Ｐｒｏｌｉｎｅ
Ｓｅｒ　Ｓｅｒｉｎｅ
Ｔｈｒ　Ｔｈｒｅｏｎｉｎｅ
Ｔｒｐ　Ｔｒｙｐｔｏｐｈａｎ
Ｔｙｒ　Ｔｙｒｏｓｉｎｅ
Ｖａｌ　Ｖａｌｉｎｅ Here, although various amino acids are mainly represented by abbreviations in the present specification, their formal names are as follows.
(Abbreviation) (official name)
Ala Alanine
Arg Arginine
Asn Asparagine
Cit Circleline
Gln Glutamine
Gly Glycine
His Histide
Ile Isolucine
Leu Leucine
Lys Lysine
Met Methionine
Orn Origine
Phe Phenylalanine
Pro Proline
Ser Serine
Thr Threoneine
Trp Tryptophan
Tyr Tyrosine
Val Valine
　また、本発明にかかる評価方法は、前記の評価方法において、前記評価ステップでは、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値が代入される変数を含む式をさらに用いて、前記式の値を算出することで、前記評価対象について、膵臓癌の状態を評価すること、を特徴とする。 In the evaluation method according to the present invention, in the evaluation method, in the evaluation step, an expression including a variable into which at least two concentration values of the 19 kinds of amino acids are substituted is used. By calculating the value, the state of pancreatic cancer is evaluated for the evaluation object.
　また、本発明にかかる評価装置は、制御部を備えた評価装置であって、前記制御部は、糖尿病を有する評価対象の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を用いて、前記評価対象について、膵臓癌の状態を評価する評価手段を備えたこと、を特徴とする。 The evaluation apparatus according to the present invention is an evaluation apparatus including a control unit, and the control unit uses at least two concentration values of the 19 kinds of amino acids in blood to be evaluated having diabetes. The evaluation object is characterized by comprising evaluation means for evaluating the state of pancreatic cancer.
　また、本発明にかかる評価方法は、制御部を備えた情報処理装置において実行される評価方法であって、前記制御部において実行される、糖尿病を有する評価対象の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を用いて、前記評価対象について、膵臓癌の状態を評価する評価ステップを含むこと、を特徴とする。 The evaluation method according to the present invention is an evaluation method executed in an information processing apparatus including a control unit, and is executed in the control unit, the 19 kinds of amino acids in blood to be evaluated having diabetes An evaluation step of evaluating the state of pancreatic cancer for the evaluation object using at least two concentration values.
　また、本発明にかかる評価プログラムは、制御部を備えた情報処理装置において実行させるための評価プログラムであって、前記制御部において実行させるための、糖尿病を有する評価対象の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を用いて、前記評価対象について、膵臓癌の状態を評価する評価ステップを含むこと、を特徴とする。 The evaluation program according to the present invention is an evaluation program for execution in an information processing apparatus including a control unit, and the 19 types in the blood to be evaluated having diabetes for execution in the control unit An evaluation step of evaluating the state of pancreatic cancer for the evaluation object using concentration values of at least two of the amino acids.
　また、本発明にかかる記録媒体は、一時的でないコンピュータ読み取り可能な記録媒体であって、情報処理装置に前記評価方法を実行させるためのプログラム化された命令を含むこと、を特徴とする。 Also, a recording medium according to the present invention is a non-transitory computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the evaluation method.
　また、本発明にかかる評価システムは、制御部を備えた評価装置と、制御部を備え、糖尿病を有する評価対象の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値に関する濃度データを提供する端末装置とを、ネットワークを介して通信可能に接続して構成された評価システムであって、前記端末装置の前記制御部は、前記評価対象の前記濃度データを前記評価装置へ送信する濃度データ送信手段と、前記評価装置から送信された、前記評価対象における膵臓癌の状態に関する評価結果を受信する結果受信手段と、を備え、前記評価装置の前記制御部は、前記端末装置から送信された前記評価対象の前記濃度データを受信する濃度データ受信手段と、前記濃度データ受信手段で受信した前記評価対象の前記濃度データに含まれている、前記１９種類のアミノ酸のうちの少なくとも２つの前記濃度値を用いて、前記評価対象について、膵臓癌の状態を評価する評価手段と、前記評価手段で得られた前記評価結果を前記端末装置へ送信する結果送信手段と、を備えたこと、を特徴とする。 In addition, an evaluation system according to the present invention includes an evaluation device including a control unit, and a control unit, and concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood of an evaluation object having diabetes. An evaluation system configured such that a terminal device to be provided is communicably connected via a network, wherein the control unit of the terminal device transmits the concentration data to be evaluated to the evaluation device. Data transmitting means, and result receiving means for receiving an evaluation result related to the state of pancreatic cancer in the evaluation target, transmitted from the evaluation apparatus, and the control unit of the evaluation apparatus is transmitted from the terminal apparatus. The density data receiving means for receiving the density data of the evaluation target, and the density data of the evaluation target received by the density data receiving means The evaluation means for evaluating the state of pancreatic cancer for the evaluation object using the concentration values of at least two of the 19 kinds of amino acids, and the evaluation result obtained by the evaluation means as the terminal And a result transmitting means for transmitting to the apparatus.
　また、本発明にかかる端末装置は、制御部を備えた端末装置であって、前記制御部は、糖尿病を有する評価対象における膵臓癌の状態に関する評価結果を取得する結果取得手段を備え、前記評価結果は、前記評価対象の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を用いて、前記評価対象について、膵臓癌の状態を評価した結果であること、を特徴とする。 The terminal device according to the present invention is a terminal device including a control unit, and the control unit includes a result acquisition unit that acquires an evaluation result regarding a state of pancreatic cancer in an evaluation target having diabetes, and the evaluation The result is a result of evaluating the state of pancreatic cancer for the evaluation target using at least two concentration values of the 19 kinds of amino acids in the blood of the evaluation target.
　また、本発明にかかる端末装置は、前記端末装置において、前記評価対象について膵臓癌の状態を評価する評価装置とネットワークを介して通信可能に接続して構成されており、前記制御部は、前記評価対象の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの前記濃度値に関する濃度データを前記評価装置へ送信する濃度データ送信手段をさらに備え、前記結果取得手段は、前記評価装置から送信された前記評価結果を受信すること、を特徴とする。 Further, the terminal device according to the present invention is configured to be communicably connected to an evaluation device that evaluates the state of pancreatic cancer for the evaluation target via the network in the terminal device, and the control unit includes The apparatus further comprises concentration data transmitting means for transmitting concentration data relating to the concentration value of at least two of the 19 kinds of amino acids in the blood to be evaluated to the evaluation apparatus, and the result acquisition means is transmitted from the evaluation apparatus. Receiving the evaluation result.
　また、本発明にかかる評価装置は、糖尿病を有する評価対象の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値に関する濃度データを提供する端末装置とネットワークを介して通信可能に接続された、制御部を備えた評価装置であって、前記制御部は、前記端末装置から送信された前記評価対象の前記濃度データを受信する濃度データ受信手段と、前記濃度データ受信手段で受信した前記評価対象の前記濃度データに含まれている、前記１９種類のアミノ酸のうちの少なくとも２つの前記濃度値を用いて、前記評価対象について、膵臓癌の状態を評価する評価手段と、前記評価手段で得られた評価結果を前記端末装置へ送信する結果送信手段と、を備えたこと、を特徴とする。 In addition, the evaluation device according to the present invention is connected to a terminal device that provides concentration data regarding at least two concentration values of the 19 kinds of amino acids in the blood to be evaluated having diabetes via a network. Further, the evaluation apparatus includes a control unit, wherein the control unit receives the density data of the evaluation target transmitted from the terminal device, and the density data receiving unit receives the density data receiving unit. An evaluation means for evaluating a state of pancreatic cancer for the evaluation object using the concentration values of at least two of the 19 kinds of amino acids included in the concentration data of the evaluation object; and And a result transmitting means for transmitting the obtained evaluation result to the terminal device.
　本発明によれば、評価対象の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を用いて、評価対象について、膵臓癌の状態を評価するので、糖尿病を有する評価対象における膵臓癌の状態を知る上で参考となり得る信頼性の高い情報を提供することができるという効果を奏する。 According to the present invention, the state of pancreatic cancer is evaluated for the evaluation object using at least two concentration values of the 19 kinds of amino acids in the blood of the evaluation object. Therefore, pancreatic cancer in the evaluation object having diabetes It is possible to provide highly reliable information that can be used as a reference in knowing the state of the device.
図１は、第１実施形態の基本原理を示す原理構成図である。FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment. 図２は、第２実施形態の基本原理を示す原理構成図である。FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. 図３は、本システムの全体構成の一例を示す図である。FIG. 3 is a diagram illustrating an example of the overall configuration of the present system. 図４は、本システムの全体構成の他の一例を示す図である。FIG. 4 is a diagram showing another example of the overall configuration of the present system. 図５は、本システムの評価装置１００の構成の一例を示すブロック図である。FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of this system. 図６は、濃度データファイル１０６ａに格納される情報の一例を示す図である。FIG. 6 is a diagram showing an example of information stored in the density data file 106a. 図７は、指標状態情報ファイル１０６ｂに格納される情報の一例を示す図である。FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b. 図８は、指定指標状態情報ファイル１０６ｃに格納される情報の一例を示す図である。FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. 図９は、式ファイル１０６ｄ１に格納される情報の一例を示す図である。FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. 図１０は、評価結果ファイル１０６ｅに格納される情報の一例を示す図である。FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106e. 図１１は、評価部１０２ｄの構成を示すブロック図である。FIG. 11 is a block diagram illustrating a configuration of the evaluation unit 102d. 図１２は、本システムのクライアント装置２００の構成の一例を示すブロック図である。FIG. 12 is a block diagram illustrating an example of the configuration of the client device 200 of the present system. 図１３は、本システムのデータベース装置４００の構成の一例を示すブロック図である。FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system. 図１４は、２種のアミノ酸の組み合わせに対して得られた上位１００個のＲＯＣ曲線下面積から６種のアミノ酸の組み合わせに対して得られた上位１００個のＲＯＣ曲線下面積までと、特許文献１（国際公開第２０１４／０８４２９０号）に記載された２００個の多変量判別式からなる既存式群に対して得られたＲＯＣ曲線下面積と、を比較した結果を示す図である。FIG. 14 shows from the area under the top 100 ROC curves obtained for a combination of two amino acids to the area under the top 100 ROC curves obtained for a combination of six amino acids. It is a figure which shows the result of having compared the area under the ROC curve obtained with respect to the existing formula group which consists of 200 multivariate discriminants described in 1 (International Publication 2014/084290). 図１５は、新式に含まれる３種のアミノ酸の組み合わせの一覧を示す図である。FIG. 15 is a diagram showing a list of combinations of three kinds of amino acids included in the new formula. 図１６は、新式に含まれる４種のアミノ酸の組み合わせの一覧を示す図である。FIG. 16 is a diagram showing a list of combinations of four amino acids included in the new formula. 図１７は、新式に含まれる５種のアミノ酸の組み合わせの一覧を示す図である。FIG. 17 is a diagram showing a list of combinations of five amino acids included in the new formula. 図１８は、新式に含まれる６種のアミノ酸の組み合わせの一覧を示す図である。FIG. 18 is a diagram showing a list of combinations of six amino acids included in the new formula. 図１９は、３種のアミノ酸の組み合わせを用いた新式に含まれる２種のアミノ酸の組み合わせの一覧を示す図である。FIG. 19 is a diagram showing a list of combinations of two kinds of amino acids included in the new formula using combinations of three kinds of amino acids. 図２０は、４種のアミノ酸の組み合わせを用いた新式に含まれる２種のアミノ酸の組み合わせの一覧、５種のアミノ酸の組み合わせを用いた新式に含まれる２種のアミノ酸の組み合わせの一覧、及び６種のアミノ酸の組み合わせを用いた新式に含まれる２種のアミノ酸の組み合わせの一覧を示す図である。FIG. 20 is a list of combinations of two amino acids included in the new formula using a combination of four amino acids, a list of combinations of two amino acids included in the new formula using a combination of five amino acids, and 6 It is a figure which shows the list of the combination of 2 types of amino acids contained in the new type | formula using the combination of a kind of amino acid. 図２１は、３種のアミノ酸の組み合わせを用いた新式に含まれる３種のアミノ酸の組み合わせの一覧および４種のアミノ酸の組み合わせを用いた新式に含まれる３種のアミノ酸の組み合わせの一覧を示す図である。FIG. 21 is a diagram showing a list of combinations of three amino acids included in the new formula using combinations of three amino acids and a list of combinations of three amino acids included in the new formula using combinations of four amino acids. It is. 図２２は、５種のアミノ酸の組み合わせを用いた新式に含まれる３種のアミノ酸の組み合わせの一覧および６種のアミノ酸の組み合わせを用いた新式に含まれる３種のアミノ酸の組み合わせの一覧を示す図である。FIG. 22 is a diagram showing a list of combinations of three amino acids included in the new formula using combinations of five amino acids and a list of combinations of three amino acids included in the new formula using combinations of six amino acids. It is.
　以下に、本発明にかかる評価方法および算出方法の実施形態（第１実施形態）、及び、本発明にかかる評価装置、算出装置、評価方法、算出方法、評価プログラム、算出プログラム、評価システム及び端末装置の実施形態（第２実施形態）を、図面に基づいて詳細に説明する。なお、本発明はこれらの実施形態により限定されるものではない。 EMBODIMENT OF THE INVENTION Below, Embodiment (1st Embodiment) of the evaluation method and calculation method concerning this invention, and the evaluation apparatus, calculation device, evaluation method, calculation method, evaluation program, calculation program, evaluation system, and terminal concerning this invention An apparatus embodiment (second embodiment) will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.
［第１実施形態］
［１－１．第１実施形態の概要］
　ここでは、第１実施形態の概要について図１を参照して説明する。図１は第１実施形態の基本原理を示す原理構成図である。 [First Embodiment]
[1-1. Overview of First Embodiment]
Here, an overview of the first embodiment will be described with reference to FIG. FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.
　まず、糖尿病を有する評価対象（例えば動物やヒトなどの個体）から採取した血液（例えば血漿、血清などを含む）中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値に関する濃度データを取得する（ステップＳ１１）。 First, concentration data relating to concentration values of at least two of the 19 kinds of amino acids in blood (for example, plasma, serum, etc.) collected from an evaluation subject (for example, an individual such as an animal or a human) having diabetes is acquired. (Step S11).
　なお、ステップＳ１１では、例えば、濃度値測定を行う企業等が測定した濃度データを取得してもよい。また、評価対象から採取した血液から、例えば以下の（Ａ）、（Ｂ）、または（Ｃ）などの測定方法により濃度値を測定することで濃度データを取得してもよい。ここで、濃度値の単位は、例えばモル濃度、重量濃度又は酵素活性であってもよく、これらの濃度に任意の定数を加減乗除することで得られるものでもよい。
（Ａ）採取した血液サンプルをＥＤＴＡ－２Ｎａ処理したチューブに採取し、そのチューブを遠心することにより血液から血漿を分離する。全ての血漿サンプルは、濃度値の測定時まで－８０℃で凍結保存する。濃度値測定時には、スルホサリチル酸を添加し３％濃度調整により除蛋白処理を行った後、ポストカラムでニンヒドリン反応を用いた高速液体クロマトグラフィー（ＨＰＣＬ）を原理としたアミノ酸分析機により濃度値を分析する。
（Ｂ）採取した血液サンプルを遠心することにより血液から血漿を分離する。全ての血漿サンプルは、濃度値の測定時まで－８０℃で凍結保存する。濃度値測定時には、スルホサリチル酸を添加し除蛋白処理を行った後、ニンヒドリン試薬を用いたポストカラム誘導体化法を原理としたアミノ酸分析計により濃度値を分析する。
（Ｃ）採取した血液サンプルを、膜やＭＥＭＳ技術または遠心分離の原理を用いて血球分離を行い、血液から血漿または血清を分離する。血漿または血清取得後すぐに濃度値の測定を行わない血漿または血清サンプルは、濃度値の測定時まで－８０℃で凍結保存する。濃度値測定時には、酵素やアプタマーなど、標的とする血中物質と反応または結合する分子等を用い、基質認識によって増減する物質や分光学的値を定量等することにより濃度値を分析する。 In step S11, for example, density data measured by a company or the like that performs density value measurement may be acquired. In addition, concentration data may be acquired by measuring concentration values from blood collected from an evaluation object by, for example, the following measurement method (A), (B), or (C). Here, the unit of the concentration value may be, for example, a molar concentration, a weight concentration, or an enzyme activity, and may be obtained by adding / subtracting / dividing an arbitrary constant to / from these concentrations.
(A) The collected blood sample is collected in a tube treated with EDTA-2Na, and plasma is separated from the blood by centrifuging the tube. All plasma samples are stored frozen at −80 ° C. until the concentration value is measured. At the time of concentration value measurement, sulfosalicylic acid is added and protein removal treatment is performed by adjusting the concentration to 3%, and then the concentration value is analyzed with an amino acid analyzer based on the principle of high-performance liquid chromatography (HPCL) using a ninhydrin reaction in a post column. To do.
(B) Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are stored frozen at −80 ° C. until the concentration value is measured. When measuring the concentration value, sulfosalicylic acid is added to remove the protein, and then the concentration value is analyzed by an amino acid analyzer based on the post-column derivatization method using a ninhydrin reagent.
(C) The collected blood sample is subjected to blood cell separation using a membrane, MEMS technology, or the principle of centrifugation to separate plasma or serum from the blood. Plasma or serum samples that are not measured immediately after plasma or serum are obtained are stored frozen at −80 ° C. until the concentration is measured. At the time of measuring the concentration value, the concentration value is analyzed by quantifying a substance that increases or decreases by substrate recognition or a spectroscopic value using a molecule that reacts with or binds to a target blood substance such as an enzyme or an aptamer.
　つぎに、ステップＳ１１で取得した濃度データに含まれている、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を用いて、評価対象について膵臓癌の状態を評価する（ステップＳ１２）。なお、ステップＳ１２を実行する前に、ステップＳ１１で取得した濃度データから欠損値や外れ値などのデータを除去してもよい。ここで、状態を評価するとは、例えば、現在の状態を検査することである。 Next, the state of pancreatic cancer is evaluated for the evaluation target using the concentration values of at least two of the 19 kinds of amino acids contained in the concentration data acquired in step S11 (step S12). Note that before executing step S12, data such as missing values and outliers may be removed from the density data acquired in step S11. Here, evaluating the state means, for example, examining the current state.
　以上、第１実施形態によれば、ステップＳ１１では評価対象の濃度データを取得し、ステップＳ１２では、ステップＳ１１で取得した評価対象の濃度データに含まれている、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を用いて、評価対象について膵臓癌の状態を評価する（要するに、評価対象における膵臓癌の状態を評価するための情報または評価対象における膵臓癌の状態を知る上で参考となり得る信頼性の高い情報を取得する）。これにより、糖尿病を有する評価対象における膵臓癌の状態を評価するための情報または糖尿病を有する評価対象における膵臓癌の状態を知る上で参考となり得る信頼性の高い情報を提供することができる。 As described above, according to the first embodiment, the concentration data of the evaluation target is acquired in step S11, and in step S12, the concentration data of the evaluation target acquired in step S11 is included in the 19 kinds of amino acids. Use at least two concentration values to evaluate the status of pancreatic cancer for the evaluation target (in short, it can be useful for knowing information for evaluating the status of pancreatic cancer in the evaluation target or the status of pancreatic cancer in the evaluation target) Get reliable information). Accordingly, it is possible to provide information for evaluating the state of pancreatic cancer in an evaluation subject having diabetes or highly reliable information that can be used as a reference in knowing the state of pancreatic cancer in an evaluation subject having diabetes.
　また、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値が評価対象における膵臓癌の状態を反映したものであると決定してもよく、さらに、濃度値を例えば以下に挙げた手法などで変換し、変換後の値が評価対象における膵臓癌の状態を反映したものであると決定してもよい。換言すると、濃度値又は変換後の値そのものを、評価対象における膵臓癌の状態に関する評価結果として扱ってもよい。
　濃度値の取り得る範囲が所定範囲（例えば０．０から１．０までの範囲、０．０から１０．０までの範囲、０．０から１００．０までの範囲、又は－１０．０から１０．０までの範囲、など）に収まるようにするためなどに、例えば、濃度値に対して任意の値を加減乗除したり、濃度値を所定の変換手法（例えば、指数変換、対数変換、角変換、平方根変換、プロビット変換、逆数変換、Ｂｏｘ－Ｃｏｘ変換、又はべき乗変換など）で変換したり、また、濃度値に対してこれらの計算を組み合わせて行ったりすることで、濃度値を変換してもよい。例えば、濃度値を指数としネイピア数を底とする指数関数の値（具体的には、膵臓癌の状態が所定の状態（例えば、基準値を超えた、膵臓癌に罹患している可能性が高い状態、など）である確率ｐを定義したときの自然対数ｌｎ（ｐ／（１－ｐ））が濃度値と等しいとした場合におけるｐ／（１－ｐ）の値）をさらに算出してもよく、また、算出した指数関数の値を１と当該値との和で割った値（具体的には、確率ｐの値）をさらに算出してもよい。
　また、特定の条件のときの変換後の値が特定の値となるように、濃度値を変換してもよい。例えば、特異度が８０％のときの変換後の値が５．０となり且つ特異度が９５％のときの変換後の値が８．０となるように濃度値を変換してもよい。
　また、各代謝物および各アミノ酸ごとに、濃度分布を正規分布化した後、平均５０、標準偏差１０となるように偏差値化してもよい。
　なお、これらの変換は、男女別や年齢別に行ってもよい。
　なお、本明細書における濃度値は、濃度値そのものであってもよく、濃度値を変換した後の値であってもよい。 Further, it may be determined that the concentration value of at least two of the 19 kinds of amino acids reflects the state of pancreatic cancer in the evaluation target, and the concentration value is converted by, for example, the following methods Then, the converted value may be determined to reflect the state of pancreatic cancer in the evaluation target. In other words, the concentration value or the converted value itself may be treated as an evaluation result regarding the state of pancreatic cancer in the evaluation target.
The possible range of the density value is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0 to For example, an arbitrary value is added / subtracted / multiplied / divided from / to the density value, or the density value is converted into a predetermined conversion method (for example, exponential conversion, logarithmic conversion, Conversion by angle conversion, square root conversion, probit conversion, reciprocal conversion, Box-Cox conversion, power conversion, etc., and by combining these calculations for density values, the density values are converted. May be. For example, the value of an exponential function with the concentration value as an index and the Napier number as the base (specifically, the pancreatic cancer state may exceed a predetermined state (for example, there is a possibility of suffering from pancreatic cancer) (The value of p / (1-p) when the natural logarithm ln (p / (1-p)) is equal to the concentration value) when the probability p is defined to be high) Alternatively, a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, the value of probability p) may be further calculated.
Further, the density value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the density value may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.
Further, for each metabolite and each amino acid, the concentration distribution may be converted into a normal distribution and then converted into a deviation value so that the average is 50 and the standard deviation is 10.
These conversions may be performed by gender or age.
The density value in the present specification may be the density value itself or a value after the density value is converted.
　また、モニタ等の表示装置又は紙等の物理媒体に視認可能に示される所定の物差し上における所定の目印の位置に関する位置情報を、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値又は当該濃度値を変換した場合にはその変換後の値を用いて生成し、生成した位置情報が評価対象における膵臓癌の状態を反映したものであると決定してもよい。なお、所定の物差しとは、膵臓癌の状態を評価するためのものであり、例えば、目盛りが示された物差しであって、「濃度値又は変換後の値の取り得る範囲、又は、当該範囲の一部分」における上限値と下限値に対応する目盛りが少なくとも示されたもの、などである。また、所定の目印とは、濃度値又は変換後の値に対応するものであり、例えば、丸印又は星印などである。 In addition, position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper is the concentration value of at least two of the 19 kinds of amino acids or the concentration When the value is converted, it may be generated using the converted value, and the generated position information may be determined to reflect the state of pancreatic cancer in the evaluation target. The predetermined ruler is for evaluating the state of pancreatic cancer. For example, the ruler is a ruler with a scale, and the “concentration value or a range that can be obtained after conversion, or the range. , A scale corresponding to the upper limit value and the lower limit value in “part of” is shown at least. The predetermined mark corresponds to the density value or the value after conversion, and is, for example, a circle mark or a star mark.
　また、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値が、所定値（平均値±１ＳＤ、２ＳＤ、３ＳＤ、Ｎ分位点、Ｎパーセンタイル又は臨床的意義の認められたカットオフ値など）より低い若しくは所定値以下の場合又は所定値以上若しくは所定値より高い場合に、評価対象について膵臓癌の状態を評価してもよい。その際、濃度値そのものではなく、濃度偏差値（各代謝物および各アミノ酸ごとに、男女別に濃度分布を正規分布化した後、平均５０、標準偏差１０となるように偏差値化した値）を用いてもよい。例えば、濃度偏差値が平均値－２ＳＤ未満の場合（濃度偏差値＜３０の場合）又は濃度偏差値が平均値＋２ＳＤより高い場合（濃度偏差値＞７０の場合）に、評価対象について膵臓癌の状態を評価してもよい。 In addition, the concentration value of at least two of the 19 kinds of amino acids is more than a predetermined value (average value ± 1SD, 2SD, 3SD, N quantile, N percentile, or a cutoff value with clinical significance). When it is low or below a predetermined value, or above a predetermined value or higher than a predetermined value, the state of pancreatic cancer may be evaluated for the evaluation target. At that time, instead of the concentration value itself, a concentration deviation value (a value obtained by normalizing the concentration distribution by gender for each metabolite and each amino acid and then making the deviation value so that the average is 50 and the standard deviation is 10) It may be used. For example, when the concentration deviation value is less than the average value −2SD (when the concentration deviation value <30) or when the concentration deviation value is higher than the average value + 2SD (when the concentration deviation value> 70), pancreatic cancer is evaluated for the evaluation target. The state may be evaluated.
　また、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値および前記１９種類のアミノ酸のうちの少なくとも２つの濃度値が代入される変数を含む式を用いて式の値を算出することで、評価対象について膵臓癌の状態を評価してもよい。 In addition, by calculating the value of the expression using an expression including a variable into which at least two concentration values of the 19 kinds of amino acids and at least two concentration values of the 19 kinds of amino acids are substituted, evaluation is performed. A subject may be evaluated for pancreatic cancer status.
　また、算出した式の値が評価対象における膵臓癌の状態を反映したものであると決定してもよく、さらに、式の値を例えば以下に挙げた手法などで変換し、変換後の値が評価対象における膵臓癌の状態を反映したものであると決定してもよい。換言すると、式の値又は変換後の値そのものを、評価対象における膵臓癌の状態に関する評価結果として扱ってもよい。
　式の値の取り得る範囲が所定範囲（例えば０．０から１．０までの範囲、０．０から１０．０までの範囲、０．０から１００．０までの範囲、又は－１０．０から１０．０までの範囲、など）に収まるようにするためなどに、例えば、式の値に対して任意の値を加減乗除したり、式の値を所定の変換手法（例えば、指数変換、対数変換、角変換、平方根変換、プロビット変換、逆数変換、Ｂｏｘ－Ｃｏｘ変換、又はべき乗変換など）で変換したり、また、式の値に対してこれらの計算を組み合わせて行ったりすることで、式の値を変換してもよい。例えば、式の値を指数としネイピア数を底とする指数関数の値（具体的には、膵臓癌の状態が所定の状態（例えば、基準値を超えた、膵臓癌に罹患している可能性が高い状態、など）である確率ｐを定義したときの自然対数ｌｎ（ｐ／（１－ｐ））が式の値と等しいとした場合におけるｐ／（１－ｐ）の値）をさらに算出してもよく、また、算出した指数関数の値を１と当該値との和で割った値（具体的には、確率ｐの値）をさらに算出してもよい。
　また、特定の条件のときの変換後の値が特定の値となるように、式の値を変換してもよい。例えば、特異度が８０％のときの変換後の値が５．０となり且つ特異度が９５％のときの変換後の値が８．０となるように式の値を変換してもよい。
　また、平均５０、標準偏差１０となるように偏差値化してもよい。
　なお、これらの変換は、男女別や年齢別に行ってもよい。
　なお、本明細書における式の値は、式の値そのものであってもよく、式の値を変換した後の値であってもよい。 Further, it may be determined that the calculated value of the expression reflects the state of pancreatic cancer in the evaluation target, and further, the value of the expression is converted by, for example, the method described below, and the converted value is You may determine that it reflects the state of the pancreatic cancer in an evaluation object. In other words, the value of the expression or the converted value itself may be handled as the evaluation result regarding the state of pancreatic cancer in the evaluation target.
The possible range of the value of the expression is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0 In order to fit within the range from 1 to 10.0, etc., for example, an arbitrary value is added / subtracted / divided / divided from / to the value of the expression, or the value of the expression is converted into a predetermined conversion method (for example, exponential conversion, Logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by combining these calculations on the value of the expression, The value of the expression may be converted. For example, the value of an exponential function with the value of the expression as the index and the Napier number as the base (specifically, the pancreatic cancer state may be suffering from pancreatic cancer with a predetermined state (for example, exceeding a reference value) Is further calculated as the natural logarithm ln (p / (1-p)) is equal to the value of the equation) when the probability p is defined to be high) Alternatively, a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, the value of probability p) may be further calculated.
Further, the value of the expression may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the value of the equation may be converted so that the value after conversion when the specificity is 80% is 5.0 and the value after conversion when the specificity is 95% is 8.0.
Further, the deviation value may be converted to an average of 50 and a standard deviation of 10.
These conversions may be performed by gender or age.
Note that the value of the expression in this specification may be the value of the expression itself, or may be a value after converting the value of the expression.
　また、モニタ等の表示装置又は紙等の物理媒体に視認可能に示される所定の物差し上における所定の目印の位置に関する位置情報を、式の値又は当該式の値を変換した場合にはその変換後の値を用いて生成し、生成した位置情報が評価対象における膵臓癌の状態を反映したものであると決定してもよい。なお、所定の物差しとは、膵臓癌の状態を評価するためのものであり、例えば、目盛りが示された物差しであって、「式の値又は変換後の値の取り得る範囲、又は、当該範囲の一部分」における上限値と下限値に対応する目盛りが少なくとも示されたもの、などである。また、所定の目印とは、式の値又は変換後の値に対応するものであり、例えば、丸印又は星印などである。 In addition, if the position information on the position of a predetermined mark on a predetermined ruler that is visible on a display device such as a monitor or a physical medium such as paper is converted into an expression value or the value of the expression It may be generated using a later value, and it may be determined that the generated position information reflects the state of pancreatic cancer in the evaluation target. Note that the predetermined ruler is for evaluating the state of pancreatic cancer, for example, a ruler with a scale, and “the range of the value of the formula or the value after conversion, or the That is, at least a scale corresponding to the upper limit value and the lower limit value in “part of range” is shown. The predetermined mark corresponds to the value of the expression or the value after conversion, and is, for example, a circle mark or a star mark.
　また、評価対象が膵臓癌に罹患している可能性の程度を定性的に評価してもよい。具体的には、「前記１９種類のアミノ酸のうちの少なくとも２つの濃度値および予め設定された１つまたは複数の閾値」または「前記１９種類のアミノ酸のうちの少なくとも２つの濃度値、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値が代入される変数を含む式、および予め設定された１つまたは複数の閾値」を用いて、評価対象を、膵臓癌に罹患している可能性の程度を少なくとも考慮して定義された複数の区分のうちのどれか１つに分類してもよい。なお、複数の区分には、膵臓癌に罹患している可能性の程度が高い対象（例えば、膵臓癌に罹患していると見做す対象）を属させるための区分（例えば、実施例に記載したランクＣなど）、膵臓癌に罹患している可能性の程度が低い対象（例えば、膵臓癌に罹患していないと見做す対象）を属させるための区分（例えば、実施例に記載したランクＡなど）、および膵臓癌に罹患している可能性の程度が中程度である対象を属させるための区分（例えば、実施例に記載したランクＢなど）が含まれていてもよい。また、複数の区分には、膵臓癌に罹患している可能性の程度が高い対象を属させるための区分（例えば、実施例に記載した膵臓癌区分など）、および、膵臓癌に罹患している可能性の程度が低い対象を属させるための区分（例えば、実施例に記載した、健常である可能性が高い対象（例えば健常であると見做す対象）を属させるための健常区分など）が含まれていてもよい。また、濃度値又は式の値を所定の手法で変換し、変換後の値を用いて評価対象を複数の区分のうちのどれか１つに分類してもよい。 Also, the degree of possibility that the evaluation target is suffering from pancreatic cancer may be qualitatively evaluated. Specifically, “at least two concentration values of the 19 amino acids and one or more preset threshold values” or “at least two concentration values of the 19 amino acids, the 19 types The degree of possibility that the subject to be evaluated suffers from pancreatic cancer using an expression including a variable into which the concentration value of at least two of the amino acids is substituted and one or more preset threshold values ” May be classified into any one of a plurality of categories defined in consideration of at least. In addition, in a plurality of categories, categories (for example, in the examples) for belonging to subjects that are highly likely to have pancreatic cancer (for example, subjects considered to be suffering from pancreatic cancer). Rank C described), classification for belonging to a subject having a low possibility of suffering from pancreatic cancer (for example, a subject regarded as not suffering from pancreatic cancer) (for example, described in the Examples) And a category (for example, rank B described in the examples) for belonging to a subject having a medium possibility of suffering from pancreatic cancer may be included. In addition, the plurality of categories include a category for belonging to a subject having a high possibility of suffering from pancreatic cancer (for example, the pancreatic cancer category described in Examples), and the like. Category for assigning a subject having a low possibility of being belonging (for example, healthy category for assigning a subject having a high possibility of being healthy (for example, a subject considered to be healthy) described in the examples) ) May be included. Alternatively, the density value or the expression value may be converted by a predetermined method, and the evaluation target may be classified into any one of a plurality of categories using the converted value.
　また、評価の際に用いる式について、その形式は特に問わないが、例えば、以下に示す形式のものでもよい。
・最小二乗法に基づく重回帰式、線形判別式、主成分分析、正準判別分析などの線形モデル
・最尤法に基づくロジスティック回帰、Ｃｏｘ回帰などの一般化線形モデル
・一般化線形モデルに加えて個体間差、施設間差などの変量効果を考慮した一般化線形混合モデル
・Ｋ－ｍｅａｎｓ法、階層的クラスタ解析などクラスタ解析で作成された式
・ＭＣＭＣ(マルコフ連鎖モンテカルロ法)、ベイジアンネットワーク、階層ベイズ法などベイズ統計に基づき作成された式
・サポートベクターマシンや決定木などクラス分類により作成された式
・分数式など上記のカテゴリに属さない手法により作成された式
・異なる形式の式の和で示されるような式 In addition, the form used for the evaluation is not particularly limited, but for example, the following form may be used.
・ Linear models such as multiple regression, linear discriminant, principal component analysis, canonical discriminant analysis based on least square method ・ Generalized linear model such as logistic regression based on maximum likelihood method, Cox regression ・ In addition to generalized linear model Generalized linear mixed models that take into account random effects such as inter-individual differences, inter-facility differences, formulas created by cluster analysis such as K-means method, hierarchical cluster analysis, MCMC (Markov chain Monte Carlo method), Bayesian network, Formulas created based on Bayesian statistics such as Hierarchical Bayes method, formulas created by class classification such as support vector machines and decision trees, formulas created by methods not belonging to the above categories such as fractional formulas, sums of formulas of different formats Formula as shown in
　また、評価の際に用いる式を、例えば、本出願人による国際出願である国際公開第２００４／０５２１９１号に記載の方法又は本出願人による国際出願である国際公開第２００６／０９８１９２号に記載の方法で作成してもよい。なお、これらの方法で得られた式であれば、入力データとしての濃度データにおけるアミノ酸の濃度値の単位に因らず、当該式を膵臓癌の状態を評価するのに好適に用いることができる。 In addition, the formula used in the evaluation is described in, for example, the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. You may create by the method. In addition, if it is a formula obtained by these methods, the formula can be suitably used to evaluate the state of pancreatic cancer regardless of the unit of the amino acid concentration value in the concentration data as input data. .
　ここで、重回帰式、多重ロジスティック回帰式、正準判別関数などにおいては各変数に係数及び定数項が付加されるが、この係数及び定数項は、好ましくは実数であれば構わず、より好ましくは、データから前記の各種分類を行うために得られた係数及び定数項の９９％信頼区間の範囲に属する値であれば構わず、さらに好ましくは、データから前記の各種分類を行うために得られた係数及び定数項の９５％信頼区間の範囲に属する値であれば構わない。また、各係数の値及びその信頼区間は、それを実数倍したものでもよく、定数項の値及びその信頼区間は、それに任意の実定数を加減乗除したものでもよい。ロジスティック回帰式、線形判別式、重回帰式などを評価の際に用いる場合、線形変換（定数の加算、定数倍）及び単調増加（減少）の変換（例えばｌｏｇｉｔ変換など）は評価性能を変えるものではなく変換前と同等であるので、これらの変換が行われた後のものを用いてもよい。 Here, in the multiple regression equation, multiple logistic regression equation, canonical discriminant function, etc., a coefficient and a constant term are added to each variable. The coefficient and the constant term are preferably real numbers, and more preferably May be any value belonging to the range of the 99% confidence interval of the coefficient and constant term obtained for performing the various classifications from the data, and more preferably, the value obtained for performing the various classifications from the data. Any value may be used as long as it falls within the 95% confidence interval of the obtained coefficient and constant term. Further, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding / subtracting / multiplying / subtracting an arbitrary real constant thereto. When logistic regression, linear discriminant, multiple regression, etc. are used for evaluation, linear conversion (addition of constants, multiple of constants) and monotonic increase (decrease) conversion (for example, logit conversion) change evaluation performance. However, since it is equivalent to before conversion, the one after these conversions may be used.
　また、分数式とは、当該分数式の分子が変数Ａ，Ｂ，Ｃ，・・・の和で表わされ及び／又は当該分数式の分母が変数ａ，ｂ，ｃ，・・・の和で表わされるものである。また、分数式には、このような構成の分数式α，β，γ，・・・の和（例えばα＋βのようなもの）も含まれる。また、分数式には、分割された分数式も含まれる。なお、分子や分母に用いられる変数にはそれぞれ適当な係数がついても構わない。また、分子や分母に用いられる変数は重複しても構わない。また、各分数式に適当な係数がついても構わない。また、各変数の係数の値や定数項の値は、実数であれば構わない。ある分数式と、当該分数式において分子の変数と分母の変数が入れ替えられたものとでは、目的変数との相関の正負の符号が概して逆転するものの、それらの相関性は保たれるが故に、評価性能も同等と見做せるので、分数式には、分子の変数と分母の変数が入れ替えられたものも含まれる。 Further, the fractional expression means that the numerator of the fractional expression is represented by the sum of the variables A, B, C,... And / or the denominator of the fractional expression is the sum of the variables a, b, c,. It is represented by In addition, the fractional expression includes a sum of fractional expressions α, β, γ,. The fractional expression also includes a divided fractional expression. Note that each variable used in the numerator and denominator may have an appropriate coefficient. The variables used for the numerator and denominator may overlap. Further, an appropriate coefficient may be attached to each fractional expression. Further, the value of the coefficient of each variable and the value of the constant term may be real numbers. In some fractional expressions and those in which the numerator and denominator variables are interchanged, the sign of the correlation with the target variable is generally reversed, but their correlation is maintained. Since the evaluation performance can be regarded as equivalent, the fractional expression includes one in which the numerator variable and the denominator variable are interchanged.
　そして、膵臓癌の状態を評価する際、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値以外に、他の生体情報に関する値（例えば、以下に挙げた値など）をさらに用いても構わない。また、評価の際に用いる式には、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値が代入される変数以外に、他の生体情報に関する値（例えば、以下に挙げた値など）が代入される１つ又は複数の変数がさらに含まれていてもよい。
１．アミノ酸以外の他の血中の代謝物（アミノ酸代謝物・糖類・脂質等）、タンパク質、ペプチド、ミネラル、ホルモン等の濃度値
２．アルブミン、総蛋白、トリグリセリド（中性脂肪）、ＨｂＡ１ｃ、糖化アルブミン、インスリン抵抗性指数、総コレステロール、ＬＤＬコレステロール、ＨＤＬコレステロール、アミラーゼ、総ビリルビン、クレアチニン、推算糸球体濾過量(ｅＧＦＲ)、尿酸、ＧＯＴ（ＡＳＴ）、ＧＰＴ（ＡＬＴ）、ＧＧＴＰ（γ－ＧＴＰ）、グルコース（血糖値）、ＣＲＰ（Ｃ反応性蛋白）、赤血球、ヘモグロビン、ヘマトクリット、ＭＣＶ、ＭＣＨ、ＭＣＨＣ、白血球、血小板数等の血液検査値
３．超音波エコー、Ｘ線、ＣＴ、ＭＲＩ、内視鏡像等の画像情報から得られる値
４．年齢、身長、体重、ＢＭＩ、腹囲、収縮期血圧、拡張期血圧、性別、喫煙情報、食事情報、飲酒情報、運動情報、ストレス情報、睡眠情報、家族の既往歴情報、疾患歴情報（糖尿病等）等の生体指標に関する値
５．タンパク質の発現量 And when evaluating the state of pancreatic cancer, in addition to the concentration values of at least two of the 19 kinds of amino acids, other values related to biological information (for example, the values listed below) may be further used. . In addition to the variables into which the concentration values of at least two of the 19 kinds of amino acids are substituted, values related to other biological information (for example, the values listed below) are substituted into the formula used for evaluation. One or more variables may be further included.
1. 1. Concentration values of blood metabolites other than amino acids (amino acid metabolites, sugars, lipids, etc.), proteins, peptides, minerals, hormones, etc. Albumin, total protein, triglyceride (neutral fat), HbA1c, glycated albumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid, GOT (AST), GPT (ALT), GGTP (γ-GTP), glucose (blood glucose level), CRP (C-reactive protein), red blood cell, hemoglobin, hematocrit, MCV, MCH, MCHC, white blood cell, platelet count, etc. Value 3. 3. Value obtained from image information such as ultrasonic echo, X-ray, CT, MRI, endoscopic image, etc. Age, height, weight, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, gender, smoking information, meal information, drinking information, exercise information, stress information, sleep information, family history information, disease history information (diabetes, etc.) 4. Values related to biological indices such as Protein expression level
［第２実施形態］
［２－１．第２実施形態の概要］
　ここでは、第２実施形態の概要について図２を参照して説明する。図２は第２実施形態の基本原理を示す原理構成図である。なお、本第２実施形態の説明では、上述した第１実施形態と重複する説明を省略する場合がある。特に、ここでは、膵臓癌の状態を評価する際に、式の値又はその変換後の値を用いるケースを一例として記載しているが、例えば、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値又はその変換後の値（例えば濃度偏差値など）を用いてもよい。 [Second Embodiment]
[2-1. Outline of Second Embodiment]
Here, an overview of the second embodiment will be described with reference to FIG. FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. In the description of the second embodiment, the description overlapping the first embodiment described above may be omitted. In particular, here, the case of using the value of the formula or the value after the conversion when evaluating the state of pancreatic cancer is described as an example. For example, the concentration of at least two of the 19 kinds of amino acids is described. A value or a value after the conversion (for example, a density deviation value) may be used.
　制御部は、糖尿病を有する評価対象（例えば動物やヒトなどの個体）の血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値に関する予め取得した濃度データに含まれている、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値、および、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値が代入される変数を含む予め記憶部に記憶された式を用いて、式の値を算出することで、評価対象について膵臓癌の状態を評価する（ステップＳ２１）。これにより、糖尿病を有する評価対象における膵臓癌の状態を知る上で参考となり得る信頼性の高い情報を提供することができる。 The control unit includes the 19 types included in the concentration data acquired in advance regarding the concentration values of at least two of the 19 types of amino acids in the blood of an evaluation target (for example, an individual such as an animal or a human) having diabetes. The value of the expression is calculated using the expression stored in advance in the storage unit including the concentration value to which at least two concentration values of the amino acids of the above and the concentration value of at least two of the 19 kinds of amino acids are substituted. Thus, the state of pancreatic cancer is evaluated for the evaluation target (step S21). Thereby, it is possible to provide highly reliable information that can serve as a reference in knowing the state of pancreatic cancer in an evaluation subject having diabetes.
　なお、ステップＳ２１で用いられる式は、以下に説明する式作成処理（工程１～工程４）に基づいて作成されたものでもよい。ここで、式作成処理の概要について説明する。なお、ここで説明する処理はあくまでも一例であり、式の作成方法はこれに限定されない。 It should be noted that the formula used in step S21 may be created based on formula creation processing (step 1 to step 4) described below. Here, an overview of the formula creation process will be described. Note that the processing described here is merely an example, and the method of creating an expression is not limited to this.
　まず、制御部は、濃度データと膵臓癌の状態を表す指標に関する指標データとを含む予め記憶部に記憶された指標状態情報（欠損値や外れ値などを持つデータが事前に除去されているものでもよい）から所定の式作成手法に基づいて、候補式（例えば、ｙ＝ａ１ｘ１＋ａ２ｘ２＋・・・＋ａｎｘｎ、ｙ：指標データ、ｘｉ：濃度データ、ａｉ：定数、ｉ＝１，２，・・・，ｎ）を作成する（工程１）。 First, the control unit previously stores index state information (data having missing values, outliers, etc., previously stored in the storage unit, including concentration data and index data relating to an index representing the state of pancreatic cancer). Or a candidate expression (for example, y = a1x1 + a2x2 +... + Anxn, y: index data, xi: concentration data, ai: constant, i = 1, 2,... n) is created (step 1).
　なお、工程１において、指標状態情報から、複数の異なる式作成手法（主成分分析や判別分析、サポートベクターマシン、重回帰分析、Ｃｏｘ回帰分析、ロジスティック回帰分析、ｋ－ｍｅａｎｓ法、クラスター解析、決定木などの多変量解析に関するものを含む。）を併用して複数の候補式を作成してもよい。具体的には、膵臓癌に罹患していない多数の糖尿病群および糖尿病に罹患している多数の膵臓癌群から得た血液を分析して得た濃度データおよび指標データから構成される多変量データである指標状態情報に対して、複数の異なるアルゴリズムを利用して複数群の候補式を同時並行的に作成してもよい。例えば、異なるアルゴリズムを利用して判別分析およびロジスティック回帰分析を同時に行い、２つの異なる候補式を作成してもよい。また、主成分分析を行って作成した候補式を利用して指標状態情報を変換し、変換した指標状態情報に対して判別分析を行うことで候補式を作成してもよい。これにより、最終的に、評価に最適な式を作成することができる。 In step 1, multiple different formula creation methods (principal component analysis and discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, determination from index state information A plurality of candidate expressions may be created using a combination of multivariate analysis such as trees). Specifically, multivariate data composed of concentration data and index data obtained by analyzing blood obtained from a large number of diabetic groups not suffering from pancreatic cancer and a large number of pancreatic cancer groups suffering from diabetes A plurality of groups of candidate formulas may be created simultaneously in parallel using a plurality of different algorithms. For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate formulas. Alternatively, the candidate formulas may be created by converting index state information using candidate formulas created by performing principal component analysis and performing discriminant analysis on the converted index status information. As a result, it is possible to finally create an optimum expression for evaluation.
　ここで、主成分分析を用いて作成した候補式は、全ての濃度データの分散を最大にするような各変数を含む一次式である。また、判別分析を用いて作成した候補式は、各群内の分散の和の全ての濃度データの分散に対する比を最小にするような各変数を含む高次式（指数や対数を含む）である。また、サポートベクターマシンを用いて作成した候補式は、群間の境界を最大にするような各変数を含む高次式（カーネル関数を含む）である。また、重回帰分析を用いて作成した候補式は、全ての濃度データからの距離の和を最小にするような各変数を含む高次式である。また、Ｃｏｘ回帰分析を用いて作成した候補式は、対数ハザード比を含む線形モデルで、そのモデルの尤度を最大とするような各変数とその係数を含む１次式である。また、ロジスティック回帰分析を用いて作成した候補式は、確率の対数オッズを表す線形モデルであり、その確率の尤度を最大にするような各変数を含む一次式である。また、ｋ－ｍｅａｎｓ法とは、各濃度データのｋ個近傍を探索し、近傍点の属する群の中で一番多いものをそのデータの所属群と定義し、入力された濃度データの属する群と定義された群とが最も合致するような変数を選択する手法である。また、クラスター解析とは、全ての濃度データの中で最も近い距離にある点同士をクラスタリング（群化）する手法である。また、決定木とは、変数に序列をつけて、序列が上位である変数の取りうるパターンから濃度データの群を予測する手法である。 Here, the candidate formula created using the principal component analysis is a linear formula including each variable that maximizes the variance of all density data. Candidate formulas created using discriminant analysis are high-order formulas (including exponents and logarithms) that contain variables that minimize the ratio of the sum of variances within each group to the variance of all concentration data. is there. The candidate formula created using the support vector machine is a high-order formula (including a kernel function) including variables that maximize the boundary between groups. Moreover, the candidate formula created using the multiple regression analysis is a high-order formula including each variable that minimizes the sum of the distances from all density data. The candidate formula created using Cox regression analysis is a linear model including a log hazard ratio, and is a linear expression including each variable and its coefficient that maximize the likelihood of the model. The candidate formula created using logistic regression analysis is a linear model that represents log odds of probability, and is a linear formula that includes each variable that maximizes the likelihood of the probability. In the k-means method, k neighborhoods of each density data are searched, the largest group among the groups to which the neighboring points belong is defined as the group to which the data belongs, and the group to which the input density data belongs. This is a method for selecting a variable that best matches the group defined as. Cluster analysis is a technique for clustering (grouping) points that are closest to each other in all density data. Further, the decision tree is a technique for predicting a group of density data from patterns that can be taken by variables with higher ranks by adding ranks to the variables.
　式作成処理の説明に戻り、制御部は、工程１で作成した候補式を、所定の検証手法に基づいて検証（相互検証）する（工程２）。候補式の検証は、工程１で作成した各候補式に対して行う。なお、工程２において、ブートストラップ法やホールドアウト法、Ｎ－フォールド法、リーブワンアウト法などのうち少なくとも１つに基づいて、候補式の判別率や感度、特異度、情報量基準、ＲＯＣ＿ＡＵＣ（受信者特性曲線の曲線下面積）などのうち少なくとも１つに関して検証してもよい。これにより、指標状態情報や評価条件を考慮した予測性または頑健性の高い候補式を作成することができる。 Returning to the description of the formula creation process, the control unit verifies (mutually verifies) the candidate formula created in step 1 based on a predetermined verification method (step 2). Candidate expressions are verified for each candidate expression created in step 1. In step 2, the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (candidate expression of candidate formulas are determined based on at least one of the bootstrap method, holdout method, N-fold method, leave one-out method, and the like. It may be verified with respect to at least one of the area under the receiver characteristic curve). Thereby, a candidate formula having high predictability or robustness in consideration of the index state information and the evaluation conditions can be created.
　ここで、判別率とは、本実施形態にかかる評価手法で、真の状態が陰性である評価対象（例えば、膵臓癌に罹患していない評価対象など）を正しく陰性と評価し、真の状態が陽性である評価対象（例えば、膵臓癌に罹患している評価対象など）を正しく陽性と評価している割合である。また、感度とは、本実施形態にかかる評価手法で、真の状態が陽性である評価対象を正しく陽性と評価している割合である。また、特異度とは、本実施形態にかかる評価手法で、真の状態が陰性である評価対象を正しく陰性と評価している割合である。また、赤池情報量規準とは、回帰分析などの場合に，観測データが統計モデルにどの程度一致するかを表す基準であり、「－２×（統計モデルの最大対数尤度）＋２×（統計モデルの自由パラメータ数）」で定義される値が最小となるモデルを最もよいと判断する。また、ＲＯＣ＿ＡＵＣは、２次元座標上に（ｘ，ｙ）＝（１－特異度，感度）をプロットして作成される曲線である受信者特性曲線（ＲＯＣ）の曲線下面積として定義され、ＲＯＣ＿ＡＵＣの値は完全な判別では１となり、この値が１に近いほど判別性が高いことを示す。また、予測性とは、候補式の検証を繰り返すことで得られた判別率や感度、特異性を平均したものである。また、頑健性とは、候補式の検証を繰り返すことで得られた判別率や感度、特異性の分散である。 Here, the discrimination rate is an evaluation method according to the present embodiment, and an evaluation object whose true state is negative (for example, an evaluation object not suffering from pancreatic cancer) is correctly evaluated as negative, and the true state Is a rate at which an evaluation target (for example, an evaluation target suffering from pancreatic cancer) is correctly evaluated as positive. Sensitivity is the rate at which an evaluation object whose true state is positive is correctly evaluated as positive in the evaluation method according to the present embodiment. Further, the specificity is a rate at which an evaluation object whose true state is negative is correctly evaluated as negative in the evaluation method according to the present embodiment. The Akaike Information Criterion is a standard that expresses how closely the observed data matches the statistical model in the case of regression analysis, etc., and is expressed as “−2 × (maximum log likelihood of statistical model) + 2 × (statistics). The model having the smallest value defined by “the number of free parameters of the model)” is determined to be the best. ROC_AUC is defined as the area under the curve of the receiver characteristic curve (ROC), which is a curve created by plotting (x, y) = (1-specificity, sensitivity) on two-dimensional coordinates, and ROC_AUC The value of 1 is 1 in complete discrimination, and the closer this value is to 1, the higher the discriminability. The predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of candidate formulas. Robustness is the variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate formulas.
　式作成処理の説明に戻り、制御部は、所定の変数選択手法に基づいて候補式の変数を選択することで、候補式を作成する際に用いる指標状態情報に含まれる濃度データの組み合わせを選択する（工程３）。なお、工程３において、変数の選択は、工程１で作成した各候補式に対して行ってもよい。これにより、候補式の変数を適切に選択することができる。そして、工程３で選択した濃度データを含む指標状態情報を用いて再び工程１を実行する。また、工程３において、工程２での検証結果からステップワイズ法、ベストパス法、近傍探索法、遺伝的アルゴリズムのうち少なくとも１つに基づいて候補式の変数を選択してもよい。なお、ベストパス法とは、候補式に含まれる変数を１つずつ順次減らしていき、候補式が与える評価指標を最適化することで変数を選択する方法である。 Returning to the description of the formula creation process, the control unit selects a combination of density data included in the index state information used when creating a candidate formula by selecting a variable of the candidate formula based on a predetermined variable selection method. (Step 3). In step 3, the selection of variables may be performed for each candidate formula created in step 1. Thereby, the variable of a candidate formula can be selected appropriately. Then, Step 1 is executed again using the index state information including the density data selected in Step 3. In step 3, the candidate expression variable may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result in step 2. The best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index given by the candidate formula.
　式作成処理の説明に戻り、制御部は、上述した工程１、工程２および工程３を繰り返し実行し、これにより蓄積した検証結果に基づいて、複数の候補式の中から評価の際に用いる候補式を選出することで、評価の際に用いる式を作成する（工程４）。なお、候補式の選出には、例えば、同じ式作成手法で作成した候補式の中から最適なものを選出する場合と、すべての候補式の中から最適なものを選出する場合とがある。 Returning to the description of the formula creation process, the control unit repeatedly executes the above-described step 1, step 2, and step 3, and based on the verification results accumulated thereby, candidates to be used for evaluation from a plurality of candidate formulas By selecting an expression, an expression used for evaluation is created (step 4). The selection of candidate formulas includes, for example, selecting an optimal formula from candidate formulas created by the same formula creation method and selecting an optimal formula from all candidate formulas.
　以上、説明したように、式作成処理では、指標状態情報に基づいて、候補式の作成、候補式の検証および候補式の変数の選択に関する処理を一連の流れで体系化（システム化）して実行することにより、膵臓癌の評価に最適な式を作成することができる。換言すると、式作成処理では、前記１９種類のアミノ酸のうちの少なくとも１つの濃度を多変量の統計解析に用い、最適でロバストな変数の組を選択するために変数選択法とクロスバリデーションとを組み合わせて、評価性能の高い式を抽出する。 As described above, in the formula creation process, processing related to creation of candidate formulas, verification of candidate formulas and selection of variables of candidate formulas is organized (systemized) in a series of flows based on index state information. By executing it, it is possible to create an optimal expression for the evaluation of pancreatic cancer. In other words, in the formula creation process, the concentration of at least one of the 19 types of amino acids is used for multivariate statistical analysis, and the variable selection method and cross-validation are combined to select the optimal and robust variable set. Thus, an expression with high evaluation performance is extracted.
［２－２．第２実施形態の構成］
　ここでは、第２実施形態にかかる評価システム（以下では本システムと記す場合がある。）の構成について、図３から図１４を参照して説明する。なお、本システムはあくまでも一例であり、本発明はこれに限定されない。特に、ここでは、膵臓癌の状態を評価する際に、式の値又はその変換後の値を用いるケースを一例として記載しているが、例えば、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値又はその変換後の値（例えば濃度偏差値など）を用いてもよい。 [2-2. Configuration of Second Embodiment]
Here, the configuration of an evaluation system according to the second embodiment (hereinafter may be referred to as the present system) will be described with reference to FIGS. 3 to 14. This system is merely an example, and the present invention is not limited to this. In particular, here, the case of using the value of the formula or the value after the conversion when evaluating the state of pancreatic cancer is described as an example. For example, the concentration of at least two of the 19 kinds of amino acids is described. A value or a value after the conversion (for example, a density deviation value) may be used.
　まず、本システムの全体構成について図３および図４を参照して説明する。図３は本システムの全体構成の一例を示す図である。また、図４は本システムの全体構成の他の一例を示す図である。本システムは、図３に示すように、評価対象である個体について膵臓癌の状態を評価する評価装置１００と、血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値に関する個体の濃度データを提供するクライアント装置２００（本発明の端末装置に相当）とを、ネットワーク３００を介して通信可能に接続して構成されている。 First, the overall configuration of this system will be described with reference to FIG. 3 and FIG. FIG. 3 is a diagram showing an example of the overall configuration of the present system. FIG. 4 is a diagram showing another example of the overall configuration of the present system. As shown in FIG. 3, the present system includes an evaluation apparatus 100 that evaluates the state of pancreatic cancer for an individual to be evaluated, and individual concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood. And a client device 200 (corresponding to a terminal device of the present invention) that provides communication via a network 300.
　なお、本システムにおいて、評価に用いられるデータの提供元となるクライアント装置２００と評価結果の提供先となるクライアント装置２００は別々のものであってもよい。本システムは、図４に示すように、評価装置１００やクライアント装置２００の他に、評価装置１００で式を作成する際に用いる指標状態情報や、評価の際に用いる式などを格納したデータベース装置４００を、ネットワーク３００を介して通信可能に接続して構成されてもよい。これにより、ネットワーク３００を介して、評価装置１００からクライアント装置２００やデータベース装置４００へ、あるいはクライアント装置２００やデータベース装置４００から評価装置１００へ、膵臓癌の状態を知る上で参考となる情報などが提供される。ここで、膵臓癌の状態を知る上で参考となる情報とは、例えば、ヒトを含む生物の膵臓癌の状態に関する特定の項目について測定した値に関する情報などである。また、膵臓癌の状態を知る上で参考となる情報は、評価装置１００やクライアント装置２００や他の装置（例えば各種の計測装置等）で生成され、主にデータベース装置４００に蓄積される。 In this system, the client device 200 that is a provider of data used for evaluation and the client device 200 that is a provider of evaluation results may be different. As shown in FIG. 4, this system stores a database apparatus that stores index state information used when creating an expression in the evaluation apparatus 100, an expression used during evaluation, and the like in addition to the evaluation apparatus 100 and the client apparatus 200. 400 may be configured to be communicably connected via the network 300. As a result, information that is useful for knowing the state of pancreatic cancer from the evaluation apparatus 100 to the client apparatus 200 or the database apparatus 400, or from the client apparatus 200 or the database apparatus 400 to the evaluation apparatus 100 via the network 300, or the like. Provided. Here, the information that is useful for knowing the state of pancreatic cancer is, for example, information about values measured for specific items related to the state of pancreatic cancer in organisms including humans. In addition, information that is useful for knowing the state of pancreatic cancer is generated by the evaluation apparatus 100, the client apparatus 200, and other apparatuses (for example, various measuring apparatuses) and is mainly stored in the database apparatus 400.
　つぎに、本システムの評価装置１００の構成について図５から図１１を参照して説明する。図５は、本システムの評価装置１００の構成の一例を示すブロック図であり、該構成のうち本発明に関係する部分のみを概念的に示している。 Next, the configuration of the evaluation apparatus 100 of this system will be described with reference to FIGS. FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
　評価装置１００は、当該評価装置を統括的に制御するＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）等の制御部１０２と、ルータ等の通信装置および専用線等の有線または無線の通信回線を介して当該評価装置をネットワーク３００に通信可能に接続する通信インターフェース部１０４と、各種のデータベースやテーブルやファイルなどを格納する記憶部１０６と、入力装置１１２や出力装置１１４に接続する入出力インターフェース部１０８と、で構成されており、これら各部は任意の通信路を介して通信可能に接続されている。ここで、評価装置１００は、各種の分析装置（例えばアミノ酸分析装置等）と同一筐体で構成されてもよい。例えば、血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を算出（測定）し、算出した値を出力（印刷やモニタ表示など）する構成（ハードウェアおよびソフトウェア）を備えた小型分析装置において、後述する評価部１０２ｄをさらに備え、当該評価部１０２ｄで得られた結果を前記構成を用いて出力すること、を特徴とするものでもよい。 The evaluation device 100 includes a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in an integrated manner, a communication device such as a router, and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 that is communicably connected to the network 300, the storage unit 106 that stores various databases, tables, and files, and the input / output interface unit 108 that is connected to the input device 112 and the output device 114 are configured. These units are communicably connected via an arbitrary communication path. Here, the evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analysis apparatus). For example, a small analysis having a configuration (hardware and software) that calculates (measures) concentration values of at least two of the 19 amino acids in blood and outputs the calculated values (printing, monitor display, etc.) The apparatus may further include an evaluation unit 102d to be described later, and output a result obtained by the evaluation unit 102d using the above configuration.
　通信インターフェース部１０４は、評価装置１００とネットワーク３００（またはルータ等の通信装置）との間における通信を媒介する。すなわち、通信インターフェース部１０４は、他の端末と通信回線を介してデータを通信する機能を有する。 The communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with other terminals via a communication line.
　入出力インターフェース部１０８は、入力装置１１２や出力装置１１４に接続する。ここで、出力装置１１４には、モニタ（家庭用テレビを含む）の他、スピーカやプリンタを用いることができる（なお、以下では、出力装置１１４をモニタ１１４として記載する場合がある。）。入力装置１１２には、キーボードやマウスやマイクの他、マウスと協働してポインティングデバイス機能を実現するモニタを用いることができる。 The input / output interface unit 108 is connected to the input device 112 and the output device 114. Here, in addition to a monitor (including a home television), a speaker or a printer can be used as the output device 114 (hereinafter, the output device 114 may be described as the monitor 114). As the input device 112, a monitor that realizes a pointing device function in cooperation with a mouse can be used in addition to a keyboard, a mouse, and a microphone.
　記憶部１０６は、ストレージ手段であり、例えば、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）・ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）等のメモリ装置や、ハードディスクのような固定ディスク装置、フレキシブルディスク、光ディスク等を用いることができる。記憶部１０６には、ＯＳ（Ｏｐｅｒａｔｉｎｇ　Ｓｙｓｔｅｍ）と協働してＣＰＵに命令を与え各種処理を行うためのコンピュータプログラムが記録されている。記憶部１０６は、図示の如く、濃度データファイル１０６ａと、指標状態情報ファイル１０６ｂと、指定指標状態情報ファイル１０６ｃと、式関連情報データベース１０６ｄと、評価結果ファイル１０６ｅと、を格納する。 The storage unit 106 is a storage unit, and for example, a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used. The storage unit 106 stores a computer program for giving instructions to the CPU and performing various processes in cooperation with an OS (Operating System). As illustrated, the storage unit 106 stores a density data file 106a, an index state information file 106b, a designated index state information file 106c, an expression related information database 106d, and an evaluation result file 106e.
　濃度データファイル１０６ａは、血液中の前記１９種類のアミノ酸のうちの少なくとも２つの濃度値に関する濃度データを格納する。図６は、濃度データファイル１０６ａに格納される情報の一例を示す図である。濃度データファイル１０６ａに格納される情報は、図６に示すように、評価対象である個体（サンプル）を一意に識別するための個体番号と、濃度データとを相互に関連付けて構成されている。ここで、図６では、濃度データを数値、すなわち連続尺度として扱っているが、濃度データは名義尺度や順序尺度でもよい。なお、名義尺度や順序尺度の場合は、それぞれの状態に対して任意の数値を与えることで解析してもよい。また、濃度データに、他の生体情報に関する値（上記参照）を組み合わせてもよい。 The concentration data file 106a stores concentration data regarding at least two concentration values of the 19 kinds of amino acids in blood. FIG. 6 is a diagram showing an example of information stored in the density data file 106a. As shown in FIG. 6, the information stored in the density data file 106a is configured by associating an individual number for uniquely identifying an individual (sample) to be evaluated with density data. Here, in FIG. 6, the density data is handled as a numerical value, that is, a continuous scale, but the density data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state. In addition, values related to other biological information (see above) may be combined with the density data.
　図５に戻り、指標状態情報ファイル１０６ｂは、式を作成する際に用いる指標状態情報を格納する。図７は、指標状態情報ファイル１０６ｂに格納される情報の一例を示す図である。指標状態情報ファイル１０６ｂに格納される情報は、図７に示すように、個体番号と、膵臓癌の状態を表す指標（指標Ｔ１、指標Ｔ２、指標Ｔ３・・・）に関する指標データ（Ｔ）と、濃度データと、を相互に関連付けて構成されている。ここで、図７では、指標データおよび濃度データを数値（すなわち連続尺度）として扱っているが、指標データおよび濃度データは名義尺度や順序尺度でもよい。なお、名義尺度や順序尺度の場合は、それぞれの状態に対して任意の数値を与えることで解析してもよい。また、指標データは、膵臓癌の状態のマーカーとなる既知の指標などであり、数値データを用いてもよい。 Returning to FIG. 5, the index state information file 106b stores the index state information used when creating the formula. FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b. As shown in FIG. 7, the information stored in the index state information file 106b includes an individual number and index data (T) related to an index (index T1, index T2, index T3,...) Indicating the state of pancreatic cancer. The density data is associated with each other. Here, in FIG. 7, the index data and the density data are handled as numerical values (that is, continuous scales), but the index data and the density data may be nominal scales or order scales. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state. The index data is a known index that serves as a marker of pancreatic cancer status, and numerical data may be used.
　図５に戻り、指定指標状態情報ファイル１０６ｃは、後述する指定部１０２ｂで指定した指標状態情報を格納する。図８は、指定指標状態情報ファイル１０６ｃに格納される情報の一例を示す図である。指定指標状態情報ファイル１０６ｃに格納される情報は、図８に示すように、個体番号と、指定した指標データと、指定した濃度データと、を相互に関連付けて構成されている。 Returning to FIG. 5, the designated index state information file 106c stores the index state information designated by the designation unit 102b described later. FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. As shown in FIG. 8, the information stored in the designated index state information file 106c is configured by associating an individual number, designated index data, and designated density data with each other.
　図５に戻り、式関連情報データベース１０６ｄは、後述する式作成部１０２ｃで作成した式を格納する式ファイル１０６ｄ１で構成される。式ファイル１０６ｄ１は、評価の際に用いる式を格納する。図９は、式ファイル１０６ｄ１に格納される情報の一例を示す図である。式ファイル１０６ｄ１に格納される情報は、図９に示すように、ランクと、式（図９では、Ｆｐ（Ｐｈｅ，・・・）やＦｐ（Ｇｌｙ，Ｌｅｕ，Ｐｈｅ）、Ｆｋ（Ｇｌｙ，Ｌｅｕ，Ｐｈｅ，・・・）など）と、各式作成手法に対応する閾値と、各式の検証結果（例えば各式の値）と、を相互に関連付けて構成されている。 Referring back to FIG. 5, the formula related information database 106d includes a formula file 106d1 that stores formulas created by a formula creation unit 102c described later. The expression file 106d1 stores expressions used for evaluation. FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. As shown in FIG. 9, the information stored in the expression file 106d1 includes the rank, the expression (in FIG. 9, Fp (Phe,...), Fp (Gly, Leu, Phe), Fk (Gly, Leu, Phe,...)), A threshold value corresponding to each formula creation method, and a verification result of each formula (for example, the value of each formula) are associated with each other.
　図５に戻り、評価結果ファイル１０６ｅは、後述する評価部１０２ｄで得られた評価結果を格納する。図１０は、評価結果ファイル１０６ｄに格納される情報の一例を示す図である。評価結果ファイル１０６ｄに格納される情報は、評価対象である個体（サンプル）を一意に識別するための個体番号と、予め取得した個体の濃度データと、膵臓癌の状態に関する評価結果（例えば、後述する算出部１０２ｄ１で算出した式の値、後述する変換部１０２ｄ２で式の値を変換した後の値、後述する生成部１０２ｄ３で生成した位置情報、又は、後述する分類部１０２ｄ４で得られた分類結果、など）と、を相互に関連付けて構成されている。 5, the evaluation result file 106e stores the evaluation result obtained by the evaluation unit 102d described later. FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106d. Information stored in the evaluation result file 106d includes an individual number for uniquely identifying an individual (sample) to be evaluated, concentration data of the individual acquired in advance, and an evaluation result regarding the state of pancreatic cancer (for example, described later) The value of the formula calculated by the calculation unit 102d1, the value after converting the value of the formula by the conversion unit 102d2 described later, the position information generated by the generation unit 102d3 described later, or the classification obtained by the classification unit 102d4 described later Results) and the like.
　図５に戻り、制御部１０２は、ＯＳ等の制御プログラム・各種の処理手順等を規定したプログラム・所要データなどを格納するための内部メモリを有し、これらのプログラムに基づいて種々の情報処理を実行する。制御部１０２は、図示の如く、大別して、受信部１０２ａと指定部１０２ｂと式作成部１０２ｃと評価部１０２ｄと結果出力部１０２ｅと送信部１０２ｆとを備えている。制御部１０２は、データベース装置４００から送信された指標状態情報やクライアント装置２００から送信された濃度データに対して、欠損値のあるデータの除去・外れ値の多いデータの除去・欠損値のあるデータの多い変数の除去などのデータ処理も行う。 Returning to FIG. 5, the control unit 102 has an internal memory for storing a control program such as an OS, a program that defines various processing procedures, and necessary data, and various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a reception unit 102a, a specification unit 102b, an expression creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f. The control unit 102 removes data with missing values, removes data with many outliers, and has data with missing values from the index state information sent from the database device 400 and the density data sent from the client device 200. Data processing such as removal of many variables is also performed.
　受信部１０２ａは、クライアント装置２００やデータベース装置４００から送信された情報（具体的には、濃度データや指標状態情報、式など）を、ネットワーク３００などを介して受信してもよい。なお、受信部１０２ａは、評価結果の送信先のクライアント装置２００とは異なるクライアント装置２００から送信された評価に用いられるデータを受信してもよい。指定部１０２ｂは、式を作成するにあたり対象とする指標データおよび濃度データを指定する。 The receiving unit 102a may receive information (specifically, concentration data, index state information, formulas, etc.) transmitted from the client device 200 or the database device 400 via the network 300 or the like. The receiving unit 102a may receive data used for evaluation transmitted from a client device 200 different from the client device 200 that is the transmission destination of the evaluation result. The designating unit 102b designates index data and density data that are targets for creating an expression.
　式作成部１０２ｃは、受信部１０２ａで受信した指標状態情報や指定部１０２ｂで指定した指標状態情報に基づいて式を作成する。なお、式が予め記憶部１０６の所定の記憶領域に格納されている場合には、式作成部１０２ｃは、記憶部１０６から所望の式を選択することで、式を作成してもよい。また、式作成部１０２ｃは、式を予め格納した他のコンピュータ装置（例えばデータベース装置４００）から所望の式を選択しダウンロードすることで、式を作成してもよい。 The formula creating unit 102c creates a formula based on the index state information received by the receiving unit 102a and the index state information specified by the specifying unit 102b. Note that if the formula is stored in a predetermined storage area of the storage unit 106 in advance, the formula creation unit 102 c may create the formula by selecting a desired formula from the storage unit 106. The formula creation unit 102c may create a formula by selecting and downloading a desired formula from another computer device (for example, the database device 400) that stores the formula in advance.
　評価部１０２ｄは、事前に得られた式（例えば、式作成部１０２ｃで作成した式、又は、受信部１０２ａで受信した式など）、及び、受信部１０２ａで受信した糖尿病を有する個体の濃度データに含まれる、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を用いて式の値を算出することで、個体について膵臓癌の状態を評価する。なお、評価部１０２ｄは、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値又は当該濃度値の変換後の値（例えば濃度偏差値）を用いて、個体について膵臓癌の状態を評価してもよい。 The evaluation unit 102d is a formula obtained in advance (for example, a formula created by the formula creation unit 102c or a formula received by the reception unit 102a), and concentration data of an individual having diabetes received by the reception unit 102a. The state of pancreatic cancer is evaluated for an individual by calculating the value of the equation using the concentration values of at least two of the 19 kinds of amino acids included in the above. The evaluation unit 102d may evaluate the state of pancreatic cancer for an individual using at least two concentration values of the 19 kinds of amino acids or a converted value of the concentration values (for example, concentration deviation value). Good.
　ここで、評価部１０２ｄの構成について図１１を参照して説明する。図１１は、評価部１０２ｄの構成を示すブロック図であり、該構成のうち本発明に関係する部分のみを概念的に示している。評価部１０２ｄは、算出部１０２ｄ１と、変換部１０２ｄ２と、生成部１０２ｄ３と、分類部１０２ｄ４と、をさらに備えている。 Here, the configuration of the evaluation unit 102d will be described with reference to FIG. FIG. 11 is a block diagram showing a configuration of the evaluation unit 102d, and conceptually shows only a portion related to the present invention. The evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.
　算出部１０２ｄ１は、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値、および、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値が代入される変数を少なくとも含む式を用いて、式の値を算出する。なお、評価部１０２ｄは、算出部１０２ｄ１で算出した式の値を評価結果として評価結果ファイル１０６ｅの所定の記憶領域に格納してもよい。 The calculation unit 102d1 uses an expression including at least two concentration values of the 19 kinds of amino acids and a variable into which at least two concentration values of the 19 kinds of amino acids are substituted. Is calculated. Note that the evaluation unit 102d may store the value of the expression calculated by the calculation unit 102d1 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
　変換部１０２ｄ２は、算出部１０２ｄ１で算出した式の値を例えば上述した変換手法などで変換する。なお、評価部１０２ｄは、変換部１０２ｄ２で変換した後の値を評価結果として評価結果ファイル１０６ｅの所定の記憶領域に格納してもよい。また、変換部１０２ｄ２は、濃度データに含まれている、前記１９種類のアミノ酸のうちの少なくとも２つの濃度値を、例えば上述した変換手法などで変換してもよい。 The conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1 using, for example, the conversion method described above. Note that the evaluation unit 102d may store the value after the conversion by the conversion unit 102d2 as an evaluation result in a predetermined storage area of the evaluation result file 106e. Further, the conversion unit 102d2 may convert at least two concentration values of the 19 kinds of amino acids included in the concentration data by, for example, the conversion method described above.
　生成部１０２ｄ３は、モニタ等の表示装置又は紙等の物理媒体に視認可能に示される所定の物差し上における所定の目印の位置に関する位置情報を、算出部１０２ｄ１で算出した式の値又は変換部１０２ｄ２で変換した後の値（濃度値又は当該濃度値の変換後の値でもよい）を用いて生成する。なお、評価部１０２ｄは、生成部１０２ｄ３で生成した位置情報を評価結果として評価結果ファイル１０６ｅの所定の記憶領域に格納してもよい。 The generation unit 102d3 uses the value of the expression calculated by the calculation unit 102d1 or the conversion unit 102d2 for the position information related to the position of the predetermined mark on the predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper. It is generated using the value after conversion in (which may be a density value or a value after conversion of the density value). The evaluation unit 102d may store the position information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of the evaluation result file 106e.
　分類部１０２ｄ４は、算出部１０２ｄ１で算出した式の値又は変換部１０２ｄ２で変換した後の値（濃度値又は当該濃度値の変換後の値でもよい）を用いて、個体を、膵臓癌に罹患している可能性の程度を少なくとも考慮して定義された複数の区分のうちのどれか１つに分類する。 The classification unit 102d4 uses an expression value calculated by the calculation unit 102d1 or a value after conversion by the conversion unit 102d2 (which may be a concentration value or a value after conversion of the concentration value) to cause an individual to suffer from pancreatic cancer. And classifying it into any one of a plurality of categories defined in consideration of at least the degree of the possibility of being performed.
　結果出力部１０２ｅは、制御部１０２の各処理部での処理結果（評価部１０２ｄで得られた評価結果を含む）等を出力装置１１４に出力する。 The result output unit 102e outputs the processing result (including the evaluation result obtained by the evaluation unit 102d) in each processing unit of the control unit 102 to the output device 114.
　送信部１０２ｆは、個体の濃度データの送信元のクライアント装置２００に対して評価結果を送信したり、データベース装置４００に対して、評価装置１００で作成した式や評価結果を送信したりする。なお、送信部１０２ｆは、評価に用いられるデータの送信元のクライアント装置２００とは異なるクライアント装置２００に対して評価結果を送信してもよい。 The transmission unit 102f transmits the evaluation result to the client device 200 that is the transmission source of the individual concentration data, or transmits the formula or evaluation result created by the evaluation device 100 to the database device 400. Note that the transmission unit 102f may transmit the evaluation result to a client device 200 different from the client device 200 that is a transmission source of data used for evaluation.
　つぎに、本システムのクライアント装置２００の構成について図１２を参照して説明する。図１２は、本システムのクライアント装置２００の構成の一例を示すブロック図であり、該構成のうち本発明に関係する部分のみを概念的に示している。 Next, the configuration of the client device 200 of this system will be described with reference to FIG. FIG. 12 is a block diagram showing an example of the configuration of the client apparatus 200 of the present system, and conceptually shows only the portion related to the present invention in the configuration.
　クライアント装置２００は、制御部２１０とＲＯＭ２２０とＨＤ（Ｈａｒｄ　Ｄｉｓｋ）２３０とＲＡＭ２４０と入力装置２５０と出力装置２６０と入出力ＩＦ２７０と通信ＩＦ２８０とで構成されており、これら各部は任意の通信路を介して通信可能に接続されている。クライアント装置２００は、プリンタ・モニタ・イメージスキャナ等の周辺装置を必要に応じて接続した情報処理装置（例えば、既知のパーソナルコンピュータ・ワークステーション・家庭用ゲーム装置・インターネットＴＶ・ＰＨＳ（Ｐｅｒｓｏｎａｌ　Ｈａｎｄｙｐｈｏｎｅ　Ｓｙｓｔｅｍ）端末・携帯端末・移動体通信端末・ＰＤＡ（Ｐｅｒｓｏｎａｌ　Ｄｉｇｉｔａｌ　Ａｓｓｉｓｔａｎｔ）等の情報処理端末など）を基にしたものであってもよい。 The client device 200 includes a control unit 210, a ROM 220, an HD (Hard Disk) 230, a RAM 240, an input device 250, an output device 260, an input / output IF 270, and a communication IF 280. These units are connected via an arbitrary communication path. Are connected to communicate. The client device 200 is an information processing device in which peripheral devices such as a printer, a monitor, and an image scanner are connected as necessary (for example, a known personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone System) It may be based on a terminal, a portable terminal, a mobile communication terminal, an information processing terminal such as PDA (Personal Digital Assistant), or the like.
　入力装置２５０はキーボードやマウスやマイク等である。なお、後述するモニタ２６１もマウスと協働してポインティングデバイス機能を実現する。出力装置２６０は、通信ＩＦ２８０を介して受信した情報を出力する出力手段であり、モニタ（家庭用テレビを含む）２６１およびプリンタ２６２を含む。この他、出力装置２６０にスピーカ等を設けてもよい。入出力ＩＦ２７０は入力装置２５０や出力装置２６０に接続する。 The input device 250 is a keyboard, a mouse, a microphone, or the like. A monitor 261, which will be described later, also realizes a pointing device function in cooperation with the mouse. The output device 260 is an output unit that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker or the like. The input / output IF 270 is connected to the input device 250 and the output device 260.
　通信ＩＦ２８０は、クライアント装置２００とネットワーク３００（またはルータ等の通信装置）とを通信可能に接続する。換言すると、クライアント装置２００は、モデムやＴＡ（Ｔｅｒｍｉｎａｌ　Ａｄａｐｔｅｒ）やルータなどの通信装置および電話回線を介して、または専用線を介してネットワーク３００に接続される。これにより、クライアント装置２００は、所定の通信規約に従って評価装置１００にアクセスすることができる。 The communication IF 280 connects the client device 200 and the network 300 (or a communication device such as a router) so that they can communicate with each other. In other words, the client device 200 is connected to the network 300 via a communication device such as a modem, a TA (Terminal Adapter), or a router, and a telephone line, or via a dedicated line. Thereby, the client apparatus 200 can access the evaluation apparatus 100 according to a predetermined communication protocol.
　制御部２１０は、受信部２１１および送信部２１２を備えている。受信部２１１は、通信ＩＦ２８０を介して、評価装置１００から送信された評価結果などの各種情報を受信する。送信部２１２は、通信ＩＦ２８０を介して、個体の濃度データなどの各種情報を評価装置１００へ送信する。 The control unit 210 includes a reception unit 211 and a transmission unit 212. The receiving unit 211 receives various types of information such as an evaluation result transmitted from the evaluation device 100 via the communication IF 280. The transmission unit 212 transmits various types of information such as individual concentration data to the evaluation apparatus 100 via the communication IF 280.
　制御部２１０は、当該制御部で行う処理の全部または任意の一部を、ＣＰＵおよび当該ＣＰＵにて解釈して実行するプログラムで実現してもよい。ＲＯＭ２２０またはＨＤ２３０には、ＯＳと協働してＣＰＵに命令を与え、各種処理を行うためのコンピュータプログラムが記録されている。当該コンピュータプログラムは、ＲＡＭ２４０にロードされることで実行され、ＣＰＵと協働して制御部２１０を構成する。また、当該コンピュータプログラムは、クライアント装置２００と任意のネットワークを介して接続されるアプリケーションプログラムサーバに記録されてもよく、クライアント装置２００は、必要に応じてその全部または一部をダウンロードしてもよい。また、制御部２１０で行う処理の全部または任意の一部を、ワイヤードロジック等によるハードウェアで実現してもよい。 The control unit 210 may be realized by a CPU and a program that is interpreted and executed by the CPU and all or any part of the processing performed by the control unit. The ROM 220 or the HD 230 stores computer programs for giving instructions to the CPU in cooperation with the OS and performing various processes. The computer program is executed by being loaded into the RAM 240, and constitutes the control unit 210 in cooperation with the CPU. Further, the computer program may be recorded in an application program server connected to the client apparatus 200 via an arbitrary network, and the client apparatus 200 may download all or a part thereof as necessary. . In addition, all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.
　ここで、制御部２１０は、評価装置１００に備えられている評価部１０２ｄが有する機能と同様の機能を有する評価部２１０ａ（算出部２１０ａ１、変換部２１０ａ２、生成部２１０ａ３、及び分類部２１０ａ４を含む）を備えていてもよい。そして、制御部２１０に評価部２１０ａが備えられている場合には、評価部２１０ａは、評価装置１００から送信された評価結果に含まれている情報に応じて、変換部２１０ａ２で式の値（濃度値でもよい）を変換したり、生成部２１０ａ３で式の値又は変換後の値（濃度値又は当該濃度値の変換後の値でもよい）に対応する位置情報を生成したり、分類部２１０ａ４で式の値又は変換後の値（濃度値又は当該濃度値の変換後の値でもよい）を用いて個体を複数の区分のうちのどれか１つに分類したりしてもよい。 Here, the control unit 210 includes an evaluation unit 210a (a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100. ) May be provided. And when the evaluation part 210a is provided in the control part 210, the evaluation part 210a is based on the information contained in the evaluation result transmitted from the evaluation apparatus 100, and the value of a formula (in the conversion part 210a2) ( A density value), or position information corresponding to an expression value or a converted value (which may be a density value or a value after conversion of the density value) is generated by the generation unit 210a3, or a classification unit 210a4 The individual may be classified into any one of a plurality of categories using the value of the expression or the value after conversion (which may be the density value or the value after conversion of the density value).
　つぎに、本システムのネットワーク３００について図３、図４を参照して説明する。ネットワーク３００は、評価装置１００とクライアント装置２００とデータベース装置４００とを相互に通信可能に接続する機能を有し、例えばインターネットやイントラネットやＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）（有線／無線の双方を含む）等である。なお、ネットワーク３００は、ＶＡＮ（Ｖａｌｕｅ－Ａｄｄｅｄ　Ｎｅｔｗｏｒｋ）や、パソコン通信網や、公衆電話網（アナログ／デジタルの双方を含む）や、専用回線網（アナログ／デジタルの双方を含む）や、ＣＡＴＶ（Ｃｏｍｍｕｎｉｔｙ　Ａｎｔｅｎｎａ　ＴｅｌｅＶｉｓｉｏｎ）網や、携帯回線交換網または携帯パケット交換網（ＩＭＴ（Ｉｎｔｅｒｎａｔｉｏｎａｌ　Ｍｏｂｉｌｅ　Ｔｅｌｅｃｏｍｍｕｎｉｃａｔｉｏｎ）２０００方式、ＧＳＭ（登録商標）（Ｇｌｏｂａｌ　Ｓｙｓｔｅｍ　ｆｏｒ　Ｍｏｂｉｌｅ　Ｃｏｍｍｕｎｉｃａｔｉｏｎｓ）方式またはＰＤＣ（Ｐｅｒｓｏｎａｌ　Ｄｉｇｉｔａｌ　Ｃｅｌｌｕｌａｒ）／ＰＤＣ－Ｐ方式等を含む）や、無線呼出網や、Ｂｌｕｅｔｏｏｔｈ（登録商標）等の局所無線網や、ＰＨＳ網や、衛星通信網（ＣＳ（Ｃｏｍｍｕｎｉｃａｔｉｏｎ　Ｓａｔｅｌｌｉｔｅ）、ＢＳ（Ｂｒｏａｄｃａｓｔｉｎｇ　Ｓａｔｅｌｌｉｔｅ）またはＩＳＤＢ（Ｉｎｔｅｇｒａｔｅｄ　Ｓｅｒｖｉｃｅｓ　Ｄｉｇｉｔａｌ　Ｂｒｏａｄｃａｓｔｉｎｇ）等を含む）等でもよい。 Next, the network 300 of this system will be described with reference to FIGS. The network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 so that they can communicate with each other. For example, the Internet, an intranet, a LAN (Local Area Network) (including both wired and wireless), and the like It is. The network 300 includes a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog / digital), a dedicated line network (including both analog / digital), CATV ( Community Antenna Television (PD) network, mobile circuit switching network or mobile packet switching network (IMT (International Mobile Telecommunication) 2000 system, GSM (Registered Trademark) Mobile Communications-PDC (PDC)) System), wireless paging networks, and local wireless networks such as Bluetooth (registered trademark) , Or PHS network, satellite communication network (CS (Communication Satellite), BS (Broadcasting Satellite) or ISDB (including Integrated Services Digital Broadcasting), etc.) may be like.
　つぎに、本システムのデータベース装置４００の構成について図１３を参照して説明する。図１３は、本システムのデータベース装置４００の構成の一例を示すブロック図であり、該構成のうち本発明に関係する部分のみを概念的に示している。 Next, the configuration of the database apparatus 400 of this system will be described with reference to FIG. FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system, and conceptually shows only the portion related to the present invention in the configuration.
　データベース装置４００は、評価装置１００または当該データベース装置で式を作成する際に用いる指標状態情報や、評価装置１００で作成した式、評価装置１００での評価結果などを格納する機能を有する。図１３に示すように、データベース装置４００は、当該データベース装置を統括的に制御するＣＰＵ等の制御部４０２と、ルータ等の通信装置および専用線等の有線または無線の通信回路を介して当該データベース装置をネットワーク３００に通信可能に接続する通信インターフェース部４０４と、各種のデータベースやテーブルやファイル（例えばＷｅｂページ用ファイル）などを格納する記憶部４０６と、入力装置４１２や出力装置４１４に接続する入出力インターフェース部４０８と、で構成されており、これら各部は任意の通信路を介して通信可能に接続されている。 The database apparatus 400 has a function of storing index state information used when creating an expression in the evaluation apparatus 100 or the database apparatus, an expression created in the evaluation apparatus 100, an evaluation result in the evaluation apparatus 100, and the like. As shown in FIG. 13, the database apparatus 400 includes a control unit 402 such as a CPU that controls the database apparatus in an integrated manner, a communication apparatus such as a router, and a wired or wireless communication circuit such as a dedicated line. A communication interface unit 404 that connects the apparatus to the network 300 to be communicable, a storage unit 406 that stores various databases, tables, and files (for example, files for Web pages), and an input unit that connects to the input unit 412 and the output unit 414. The output interface unit 408 is configured to be communicable via an arbitrary communication path.
　記憶部４０６は、ストレージ手段であり、例えば、ＲＡＭ・ＲＯＭ等のメモリ装置や、ハードディスクのような固定ディスク装置や、フレキシブルディスクや、光ディスク等を用いることができる。記憶部４０６には、各種処理に用いる各種プログラム等を格納する。通信インターフェース部４０４は、データベース装置４００とネットワーク３００（またはルータ等の通信装置）との間における通信を媒介する。すなわち、通信インターフェース部４０４は、他の端末と通信回線を介してデータを通信する機能を有する。入出力インターフェース部４０８は、入力装置４１２や出力装置４１４に接続する。ここで、出力装置４１４には、モニタ（家庭用テレビを含む）の他、スピーカやプリンタを用いることができる。また、入力装置４１２には、キーボードやマウスやマイクの他、マウスと協働してポインティングデバイス機能を実現するモニタを用いることができる。 The storage unit 406 is a storage means, and for example, a memory device such as a RAM / ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used. The storage unit 406 stores various programs used for various processes. The communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with other terminals via a communication line. The input / output interface unit 408 is connected to the input device 412 and the output device 414. Here, in addition to a monitor (including a home television), a speaker or a printer can be used as the output device 414. In addition to the keyboard, mouse, and microphone, the input device 412 can be a monitor that realizes a pointing device function in cooperation with the mouse.
　制御部４０２は、ＯＳ等の制御プログラム・各種の処理手順等を規定したプログラム・所要データなどを格納するための内部メモリを有し、これらのプログラムに基づいて種々の情報処理を実行する。制御部４０２は、図示の如く、大別して、送信部４０２ａと受信部４０２ｂを備えている。送信部４０２ａは、指標状態情報や式などの各種情報を、評価装置１００へ送信する。受信部４０２ｂは、評価装置１００から送信された、式や評価結果などの各種情報を受信する。 The control unit 402 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, required data, and the like, and executes various information processing based on these programs. As shown in the figure, the control unit 402 is roughly divided into a transmission unit 402a and a reception unit 402b. The transmission unit 402a transmits various types of information such as index state information and formulas to the evaluation apparatus 100. The receiving unit 402b receives various types of information such as expressions and evaluation results transmitted from the evaluation device 100.
　なお、本説明では、評価装置１００が、濃度データの受信から、式の値の算出、個体の区分への分類、そして評価結果の送信までを実行し、クライアント装置２００が評価結果の受信を実行するケースを例として挙げたが、クライアント装置２００に評価部２１０ａが備えられている場合は、評価装置１００は式の値の算出を実行すれば十分であり、例えば式の値の変換、位置情報の生成、及び、個体の区分への分類などは、評価装置１００とクライアント装置２００とで適宜分担して実行してもよい。
　例えば、クライアント装置２００は、評価装置１００から式の値を受信した場合には、評価部２１０ａは、変換部２１０ａ２で式の値を変換したり、生成部２１０ａ３で式の値又は変換後の値に対応する位置情報を生成したり、分類部２１０ａ４で式の値又は変換後の値を用いて個体を複数の区分のうちのどれか１つに分類したりしてもよい。
　また、クライアント装置２００は、評価装置１００から変換後の値を受信した場合には、評価部２１０ａは、生成部２１０ａ３で変換後の値に対応する位置情報を生成したり、分類部２１０ａ４で変換後の値を用いて個体を複数の区分のうちのどれか１つに分類したりしてもよい。
　また、クライアント装置２００は、評価装置１００から式の値又は変換後の値と位置情報とを受信した場合には、評価部２１０ａは、分類部２１０ａ４で式の値又は変換後の値を用いて個体を複数の区分のうちのどれか１つに分類してもよい。 In this description, the evaluation apparatus 100 executes from the reception of the concentration data to the calculation of the value of the expression, the classification into the individual categories, and the transmission of the evaluation result, and the client apparatus 200 receives the evaluation result. In the case where the client device 200 includes the evaluation unit 210a, it is sufficient for the evaluation device 100 to calculate the value of the expression. For example, conversion of the value of the expression, position information The generation and the classification into individual sections may be appropriately shared by the evaluation apparatus 100 and the client apparatus 200.
For example, when the client device 200 receives the value of the expression from the evaluation device 100, the evaluation unit 210a converts the value of the expression in the conversion unit 210a2, or the value of the expression or the value after conversion in the generation unit 210a3. Or the classification unit 210a4 may classify the individual into one of a plurality of categories using the value of the expression or the value after conversion.
Further, when the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a generates position information corresponding to the converted value in the generation unit 210a3, or converts it in the classification unit 210a4. An individual may be classified into any one of a plurality of divisions using a later value.
When the client device 200 receives the value of the expression or the value after conversion and the position information from the evaluation device 100, the evaluation unit 210a uses the value of the expression or the value after conversion in the classification unit 210a4. The individual may be classified into any one of a plurality of sections.
［２－３．他の実施形態］
　本発明にかかる評価装置、算出装置、評価方法、算出方法、評価プログラム、算出プログラム、評価システム、および端末装置は、上述した第２実施形態以外にも、請求の範囲に記載した技術的思想の範囲内において種々の異なる実施形態にて実施されてよいものである。 [2-3. Other Embodiments]
The evaluation device, the calculation device, the evaluation method, the calculation method, the evaluation program, the calculation program, the evaluation system, and the terminal device according to the present invention have the technical idea described in the claims in addition to the second embodiment described above. It may be implemented in a variety of different embodiments within the scope.
　また、第２実施形態において説明した各処理のうち、自動的に行われるものとして説明した処理の全部または一部を手動的に行うこともでき、あるいは、手動的に行われるものとして説明した処理の全部または一部を公知の方法で自動的に行うこともできる。 In addition, among the processes described in the second embodiment, all or part of the processes described as being performed automatically can be performed manually, or the processes described as being performed manually All or a part of the above can be automatically performed by a known method.
　このほか、上記文献中や図面中で示した処理手順、制御手順、具体的名称、各処理の登録データや検索条件等のパラメータを含む情報、画面例、データベース構成については、特記する場合を除いて任意に変更することができる。 In addition, unless otherwise specified, the processing procedures, control procedures, specific names, information including registration data for each processing, parameters such as search conditions, screen examples, and database configurations shown in the above documents and drawings Can be changed arbitrarily.
　また、評価装置１００に関して、図示の各構成要素は機能概念的なものであり、必ずしも物理的に図示の如く構成されていることを要しない。 In addition, regarding the evaluation apparatus 100, each illustrated component is functionally conceptual and does not necessarily need to be physically configured as illustrated.
　例えば、評価装置１００が備える処理機能、特に制御部１０２にて行われる各処理機能については、その全部または任意の一部を、ＣＰＵおよび当該ＣＰＵにて解釈実行されるプログラムにて実現してもよく、また、ワイヤードロジックによるハードウェアとして実現してもよい。尚、プログラムは、情報処理装置に本発明にかかる評価方法を実行させるためのプログラム化された命令を含む一時的でないコンピュータ読み取り可能な記録媒体に記録されており、必要に応じて評価装置１００に機械的に読み取られる。すなわち、ＲＯＭまたはＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）などの記憶部１０６などには、ＯＳと協働してＣＰＵに命令を与え、各種処理を行うためのコンピュータプログラムが記録されている。このコンピュータプログラムは、ＲＡＭにロードされることによって実行され、ＣＰＵと協働して制御部を構成する。 For example, all or some of the processing functions provided in the evaluation apparatus 100, particularly the processing functions performed by the control unit 102, may be realized by the CPU and a program interpreted and executed by the CPU. Alternatively, it may be realized as hardware by wired logic. The program is recorded on a non-transitory computer-readable recording medium including programmed instructions for causing the information processing apparatus to execute the evaluation method according to the present invention, and is stored in the evaluation apparatus 100 as necessary. Read mechanically. That is, a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded in the storage unit 106 such as a ROM or HDD (Hard Disk Drive). This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU.
　また、このコンピュータプログラムは評価装置１００に対して任意のネットワークを介して接続されたアプリケーションプログラムサーバに記憶されていてもよく、必要に応じてその全部または一部をダウンロードすることも可能である。 Further, this computer program may be stored in an application program server connected to the evaluation apparatus 100 via an arbitrary network, and the whole or a part of the computer program can be downloaded as necessary.
　また、本発明にかかる評価プログラムを、一時的でないコンピュータ読み取り可能な記録媒体に格納してもよく、また、プログラム製品として構成することもできる。ここで、この「記録媒体」とは、メモリーカード、ＵＳＢ（Ｕｎｉｖｅｒｓａｌ　Ｓｅｒｉａｌ　Ｂｕｓ）メモリ、ＳＤ（Ｓｅｃｕｒｅ　Ｄｉｇｉｔａｌ）カード、フレキシブルディスク、光磁気ディスク、ＲＯＭ、ＥＰＲＯＭ（Ｅｒａｓａｂｌｅ　Ｐｒｏｇｒａｍｍａｂｌｅ　Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＥＥＰＲＯＭ（Ｅｌｅｃｔｒｉｃａｌｌｙ　Ｅｒａｓａｂｌｅ　ａｎｄ　Ｐｒｏｇｒａｍｍａｂｌｅ　Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）（登録商標）、ＣＤ－ＲＯＭ（Ｃｏｍｐａｃｔ　Ｄｉｓｃ　Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＭＯ（Ｍａｇｎｅｔｏ－Ｏｐｔｉｃａｌ　ｄｉｓｋ）、ＤＶＤ（Ｄｉｇｉｔａｌ　Ｖｅｒｓａｔｉｌｅ　Ｄｉｓｋ）、および、Ｂｌｕ－ｒａｙ（登録商標）　Ｄｉｓｃ等の任意の「可搬用の物理媒体」を含むものとする。 Further, the evaluation program according to the present invention may be stored in a computer-readable recording medium that is not temporary, and may be configured as a program product. Here, the “recording medium” refers to a memory card, USB (Universal Serial Bus) memory, SD (Secure Digital) card, flexible disk, magneto-optical disk, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electric Electric). Erasable and Programmable Read Only Memory (registered trademark), CD-ROM (Compact Disc Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Register, etc.) Any “possible It is intended to include physical medium "of use.
　また、「プログラム」とは、任意の言語または記述方法にて記述されたデータ処理方法であり、ソースコードまたはバイナリコード等の形式を問わない。なお、「プログラム」は必ずしも単一的に構成されるものに限られず、複数のモジュールやライブラリとして分散構成されるものや、ＯＳに代表される別個のプログラムと協働してその機能を達成するものをも含む。なお、実施形態に示した各装置において記録媒体を読み取るための具体的な構成および読み取り手順ならびに読み取り後のインストール手順等については、周知の構成や手順を用いることができる。 Also, the “program” is a data processing method described in an arbitrary language or description method, and may be in the form of source code or binary code. Note that the “program” is not necessarily limited to a single configuration, and functions are achieved in cooperation with a separate configuration such as a plurality of modules and libraries or a separate program represented by the OS. Including things. In addition, a well-known structure and procedure can be used about the specific structure and reading procedure for reading a recording medium in each apparatus shown to embodiment, the installation procedure after reading, etc.
　記憶部１０６に格納される各種のデータベース等は、ＲＡＭ、ＲＯＭ等のメモリ装置、ハードディスク等の固定ディスク装置、フレキシブルディスク、および、光ディスク等のストレージ手段であり、各種処理やウェブサイト提供に用いる各種のプログラム、テーブル、データベース、および、ウェブページ用ファイル等を格納する。 Various databases and the like stored in the storage unit 106 are storage devices such as a memory device such as a RAM and a ROM, a fixed disk device such as a hard disk, a flexible disk, and an optical disk. Programs, tables, databases, web page files, and the like.
　また、評価装置１００は、既知のパーソナルコンピュータまたはワークステーション等の情報処理装置として構成してもよく、また、任意の周辺装置が接続された当該情報処理装置として構成してもよい。また、評価装置１００は、当該情報処理装置に本発明の評価方法を実現させるソフトウェア（プログラムまたはデータ等を含む）を実装することにより実現してもよい。 Further, the evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured as the information processing apparatus connected to an arbitrary peripheral device. The evaluation apparatus 100 may be realized by installing software (including a program or data) that causes the information processing apparatus to realize the evaluation method of the present invention.
　更に、装置の分散・統合の具体的形態は図示するものに限られず、その全部または一部を、各種の付加等に応じてまたは機能負荷に応じて、任意の単位で機能的または物理的に分散・統合して構成することができる。すなわち、上述した実施形態を任意に組み合わせて実施してもよく、実施形態を選択的に実施してもよい。 Furthermore, the specific form of distribution / integration of the devices is not limited to that shown in the figure, and all or a part of them may be functionally or physically in arbitrary units according to various additions or according to functional loads. It can be configured to be distributed and integrated. That is, the above-described embodiments may be arbitrarily combined and may be selectively implemented.
　膵臓癌と確定診断された患者のうち糖尿病を合併する患者７５例の血液サンプル、及び、癌を罹患していない糖尿病患者７５例の血液サンプルから、前述のアミノ酸分析法（Ａ）により、Ｔｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇの１９種のアミノ酸の血中濃度を測定した。 From the blood samples of 75 patients with diabetes diagnosed among patients diagnosed with pancreatic cancer and the blood samples of 75 patients with diabetes who do not suffer from cancer, Tyr, Serum concentrations of 19 amino acids, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, and Arg, were measured.
　前記１９種のアミノ酸から抽出した、２種のアミノ酸から６種のアミノ酸までの全ての組み合わせに対し、多重ロジスティック回帰を実施した。そして、糖尿病患者を対象とした膵臓癌群と非膵臓癌群の２群判別に関する判別性能を、ＲＯＣ曲線下面積で評価した。 Multiple logistic regression was performed on all combinations from 2 amino acids to 6 amino acids extracted from the 19 amino acids. And the discrimination performance regarding 2 group discrimination | determination of the pancreatic cancer group and non-pancreatic cancer group which aimed at the diabetic patient was evaluated by the area under a ROC curve.
　図１４は、２種のアミノ酸の組み合わせに対して得られた上位１００個のＲＯＣ曲線下面積から６種のアミノ酸の組み合わせに対して得られた上位１００個のＲＯＣ曲線下面積までと、特許文献１（国際公開第２０１４／０８４２９０号）に記載された２００個の多変量判別式からなる既存式群に対して得られたＲＯＣ曲線下面積と、を比較した結果を示す図である。 FIG. 14 shows from the area under the top 100 ROC curves obtained for a combination of two amino acids to the area under the top 100 ROC curves obtained for a combination of six amino acids. It is a figure which shows the result of having compared the area under the ROC curve obtained with respect to the existing formula group which consists of 200 multivariate discriminants described in 1 (International Publication 2014/084290).
　本実施例で測定した血中アミノ酸濃度データを、既存式群を用いて解析して、ＲＯＣ曲線下面積を得たところ、最大値は０．８７３であった。そして、ＲＯＣ曲線下面積が当該最大値を上回る、２種のアミノ酸の組み合わせを用いた新式を、前記上位１００に対応する２種のアミノ酸の組み合わせを基に検討した。その結果、ＲＯＣ曲線下面積が０．８７７である、ＳｅｒおよびＨｉｓの組み合わせを用いた新式が検出された。 The blood amino acid concentration data measured in this example was analyzed using the existing formula group to obtain the area under the ROC curve, and the maximum value was 0.873. Then, a new formula using a combination of two amino acids whose area under the ROC curve exceeds the maximum value was examined based on a combination of two amino acids corresponding to the top 100. As a result, a new formula using a combination of Ser and His having an area under the ROC curve of 0.877 was detected.
　以上の結果から、検査対象者を糖尿病患者に限定することにより、ＳｅｒおよびＨｉｓのアミノ酸の組み合わせを用いた多変量判別式が既存式群と比較して有用な指標となることが判明した。 From the above results, it was found that a multivariate discriminant using a combination of Ser and His amino acids is a useful index compared to the existing formula group by limiting the test subject to diabetic patients.
　また、前記上位１００に対応する３種のアミノ酸から６種のアミノ酸までの組み合わせについても、ＲＯＣ曲線下面積が前記最大値０．８７３を上回る新式の数を集計した。その結果、３種のアミノ酸の組み合わせを用いた新式の数は１８、４種のアミノ酸の組み合わせを用いた新式の数は１０２、５種のアミノ酸の組み合わせを用いた新式の数及び６種のアミノ酸の組み合わせを用いた新式の数はそれぞれ１００であった。これらの新式の一覧を図１５から図１８に示す。 In addition, for combinations from 3 amino acids to 6 amino acids corresponding to the top 100, the number of new formulas where the area under the ROC curve exceeds the maximum value 0.873 was counted. As a result, the number of new formulas using a combination of three amino acids is 18, the number of new formulas using a combination of four amino acids is 102, the number of new formulas using a combination of five amino acids, and the six amino acids The number of new formulas using the combination was 100 for each. A list of these new types is shown in FIGS.
　以上の結果から、検査対象者を糖尿病患者に限定することにより、図１５から図１８に示した各アミノ酸の組み合わせを用いた多変量判別式が既存式群と比較して有用な指標となることが判明した。 Based on the above results, by limiting the test subject to diabetic patients, the multivariate discriminant using the combination of each amino acid shown in FIGS. 15 to 18 becomes a useful index compared to the existing formula group. There was found.
　実施例１で得た、前記最大値０．８７３を上回る、３種のアミノ酸の組み合わせを用いた１８個の多変量判別式を対象として、これらの式に含まれる２種のアミノ酸の組み合わせを探索した。その結果、図１９に示す２種のアミノ酸の組み合わせが確認された。 Searching for 18 multivariate discriminants using combinations of 3 amino acids exceeding the maximum value of 0.873 obtained in Example 1, and searching for combinations of 2 amino acids included in these equations did. As a result, a combination of two amino acids shown in FIG. 19 was confirmed.
　検査対象者を糖尿病患者に限定することにより得られた、既存式群と比較して有用な多変量判別式に、図１９に示した２種のアミノ酸の組み合わせが含まれていることが判明した。 It turned out that the combination of the two types of amino acids shown in FIG. 19 is included in the useful multivariate discriminant compared with the existing formula group obtained by limiting the test subject to diabetic patients. .
　実施例１で得られた、４種のアミノ酸の組み合わせを用いた新式、５種のアミノ酸の組み合わせを用いた新式及び６種のアミノ酸の組み合わせを用いた新式について、図１６から図１８に示す通り、ＲＯＣ曲線下面積が前記最大値０．８７３を上回った。そこで、ＲＯＣ曲線下面積が上位１００の新式ではなく、ＲＯＣ曲線下面積が前記０．８７３を上回る、４種のアミノ酸の組み合わせを用いた新式、５種のアミノ酸の組み合わせを用いた新式及び６種のアミノ酸の組み合わせを用いた新式に検討対象を広げ、これらの新式それぞれに対し、式に含まれる２種のアミノ酸の組み合わせの数を集計した。 As shown in FIG. 16 to FIG. 18, the new formula using the combination of four amino acids, the new formula using a combination of five amino acids, and the new formula using a combination of six amino acids obtained in Example 1. The area under the ROC curve exceeded the maximum value 0.873. Therefore, the area under the ROC curve is not the top 100 new formula, the area under the ROC curve exceeds 0.873 above, a new formula using a combination of four amino acids, a new formula using a combination of five amino acids, and six types The scope of study was expanded to new formulas using combinations of amino acids, and for each of these new formulas, the number of combinations of two types of amino acids contained in the formula was counted.
　前記１９種のアミノ酸から選択可能な全１７１通りの２種のアミノ酸の組み合わせのうち、式に多く含まれる組み合わせを上位３４位（上位２０％）まで特定した。その特定結果を図２０に示す。 Among the total of 171 combinations of two types of amino acids that can be selected from the 19 types of amino acids, the combinations that are included in the formula were identified in the top 34 (top 20%). The identification result is shown in FIG.
　検査対象者を糖尿病患者に限定することにより得られた、既存式群と比較して有用な多変量判別式に、図２０に示した２種のアミノ酸の組み合わせが高頻度で含まれていることが判明した。 The combination of the two types of amino acids shown in FIG. 20 is frequently included in the multivariate discriminant useful compared to the existing formula group obtained by limiting the test subject to diabetic patients. There was found.
　実施例１で用いたサンプルデータを用いた。実施例１で抽出した、３種のアミノ酸から６種のアミノ酸までの全ての組み合わせに対し、多重ロジスティック回帰を実施した。そして、糖尿病患者を対象とした膵臓癌群と非膵臓癌群の２群判別に関する判別性能を、ＲＯＣ曲線下面積で評価した。 The sample data used in Example 1 was used. Multiple logistic regression was performed on all combinations from 3 amino acids to 6 amino acids extracted in Example 1. And the discrimination performance regarding 2 group discrimination | determination of the pancreatic cancer group and non-pancreatic cancer group which aimed at the diabetic patient was evaluated by the area under a ROC curve.
　ＲＯＣ曲線下面積が前記最大値０．８７３を上回る、３種のアミノ酸の組み合わせを用いた新式、４種のアミノ酸の組み合わせを用いた新式、５種のアミノ酸の組み合わせを用いた新式及び６種のアミノ酸の組み合わせを用いた新式それぞれに対し、式に含まれる３種のアミノ酸の組み合わせの数を集計した。 The new area using the combination of three amino acids, the new expression using a combination of four amino acids, the new expression using a combination of five kinds of amino acids, and the six kinds of areas under the ROC curve exceeding the maximum value 0.873. For each new formula using combinations of amino acids, the number of combinations of three amino acids contained in the formula was counted.
　前記１９種のアミノ酸から選択可能な全９６９通りの３種のアミノ酸の組み合わせのうち、式に多く含まれる組み合わせを上位９７位（上位１０％）まで特定した。３種のアミノ酸の組み合わせを用いた新式および４種のアミノ酸の組み合わせを用いた新式に対し得られた特定結果を図２１に示し、５種のアミノ酸の組み合わせを用いた新式および６種のアミノ酸の組み合わせを用いた新式に対し得られた特定結果を図２２に示す。 Among the total of 969 combinations of 3 types of amino acids that can be selected from the 19 types of amino acids, the combinations that are included in the formula were identified in the top 97 (top 10%). The specific results obtained for the new formula using a combination of three amino acids and the new formula using a combination of four amino acids are shown in FIG. 21, and the new formula using a combination of five amino acids and six amino acids The specific results obtained for the new formula using the combination are shown in FIG.
　検査対象者を糖尿病患者に限定することにより得られた、既存式群と比較して有用な多変量判別式に、図２１および図２２に示した３種のアミノ酸の組み合わせが高頻度で含まれていることが判明した。 A useful multivariate discriminant compared to the existing formula group obtained by limiting the test subject to diabetic patients frequently contains the combinations of the three amino acids shown in FIG. 21 and FIG. Turned out to be.
　以上のように、本発明は、産業上の多くの分野、特に医薬品や食品、医療などの分野で広く実施することができ、特に、糖尿病患者における膵臓癌の状態の進行予測や疾病リスク予測やプロテオームやメタボローム解析などを行うバイオインフォマティクス分野において極めて有用である。 As described above, the present invention can be widely implemented in many industrial fields, in particular, in fields such as pharmaceuticals, foods, and medical care, and in particular, prediction of progression of pancreatic cancer status and disease risk prediction in diabetic patients. It is extremely useful in the field of bioinformatics for proteome and metabolome analysis.
　１００　評価装置
　１０２　制御部
　１０２ａ　受信部
　１０２ｂ　指定部
　１０２ｃ　式作成部
　１０２ｄ　評価部
　１０２ｄ１　算出部
　１０２ｄ２　変換部
　１０２ｄ３　生成部
　１０２ｄ４　分類部
　１０２ｅ　結果出力部
　１０２ｆ　送信部
　１０４　通信インターフェース部
　１０６　記憶部
　１０６ａ　濃度データファイル
　１０６ｂ　指標状態情報ファイル
　１０６ｃ　指定指標状態情報ファイル
　１０６ｄ　式関連情報データベース
　１０６ｄ１　式ファイル
　１０６ｅ　評価結果ファイル
　１０８　入出力インターフェース部
　１１２　入力装置
　１１４　出力装置
　２００　クライアント装置（端末装置（情報通信端末装置））
　３００　ネットワーク
　４００　データベース装置 DESCRIPTION OF SYMBOLS 100 Evaluation apparatus 102 Control part 102a Receiving part 102b Specification part 102c Formula creation part 102d Evaluation part 102d1 Calculation part 102d2 Conversion part 102d3 Generation part 102d4 Classification part 102e Result output part 102f Transmission part 104 Communication interface part 106 Storage part 106a Concentration data file 106b Index state information file 106c Designated index state information file 106d Expression related information database 106d1 Expression file 106e Evaluation result file 108 Input / output interface unit 112 Input device 114 Output device 200 Client device (terminal device (information communication terminal device))
300 network 400 database device

Claims (11)

  1. 　糖尿病を有する評価対象の血液中のＴｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇのうちの少なくとも２つの濃度値、または、前記濃度値が代入される変数を含む式および前記濃度値を用いて算出された前記式の値を用いて、前記評価対象について、膵臓癌の状態を評価する評価ステップを含むこと、
    　を特徴とする評価方法。     At least one of Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg in the blood to be evaluated having diabetes An evaluation step for evaluating the state of pancreatic cancer for the evaluation object using two concentration values or an equation including a variable to which the concentration value is substituted and the value of the equation calculated using the concentration value Including
    Evaluation method characterized by
  2. 　前記評価ステップは、制御部を備えた情報処理装置の前記制御部において実行されること、
    　を特徴とする請求項１に記載の評価方法。     The evaluation step is executed in the control unit of the information processing apparatus including the control unit;
    The evaluation method according to claim 1, wherein:
  3. 　糖尿病を有する評価対象の血液中のＴｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇのうちの少なくとも２つの濃度値、および、前記濃度値が代入される変数を含む膵臓癌の状態を評価するための式を用いて、前記式の値を算出すること、
    　を特徴とする算出方法。     At least one of Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg in the blood to be evaluated having diabetes Calculating the value of the equation using an equation for assessing pancreatic cancer status comprising two concentration values and a variable into which the concentration value is substituted;
    A calculation method characterized by
  4. 　前記算出ステップは、制御部を備えた情報処理装置の前記制御部において実行されること、
    　を特徴とする請求項３に記載の算出方法。     The calculation step is executed in the control unit of the information processing apparatus including the control unit;
    The calculation method according to claim 3.
  5. 　制御部を備えた評価装置であって、
    　前記制御部は、
    　糖尿病を有する評価対象の血液中のＴｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇのうちの少なくとも２つの濃度値、または、前記濃度値が代入される変数を含む式および前記濃度値を用いて算出された前記式の値を用いて、前記評価対象について、膵臓癌の状態を評価する評価手段
    　を備えたこと、
    　を特徴とする評価装置。     An evaluation device including a control unit,
    The controller is
    At least one of Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg in the blood to be evaluated having diabetes Evaluation means for evaluating the state of pancreatic cancer for the evaluation object using two concentration values or an expression including a variable to which the concentration value is substituted and the value of the expression calculated using the concentration value Having
    An evaluation apparatus characterized by.
  6. 　前記濃度値に関する濃度データまたは前記式の値を提供する端末装置とネットワークを介して通信可能に接続され、
    　前記制御部は、
    　前記端末装置から送信された前記評価対象の前記濃度データまたは前記式の値を受信するデータ受信手段と、
    　前記評価手段で得られた評価結果を前記端末装置へ送信する結果送信手段と、
    　をさらに備え、
    　前記評価手段は、前記データ受信手段で受信した前記濃度データに含まれている前記濃度値または前記式の値を用いること、
    　を特徴とする請求項５に記載の評価装置。     The terminal device that provides the density data related to the density value or the value of the formula is connected to be communicable via a network,
    The controller is
    Data receiving means for receiving the concentration data of the evaluation object or the value of the expression transmitted from the terminal device;
    A result transmitting means for transmitting the evaluation result obtained by the evaluating means to the terminal device;
    Further comprising
    The evaluation means uses the density value or the value of the formula included in the density data received by the data receiving means;
    The evaluation apparatus according to claim 5.
  7. 　制御部を備えた算出装置であって、
    　前記制御部は、
    　糖尿病を有する評価対象の血液中のＴｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇのうちの少なくとも２つの濃度値、および、前記濃度値が代入される変数を含む膵臓癌の状態を評価するための式を用いて、前記式の値を算出する算出手段
    　を備えたこと、
    　を特徴とする算出装置。     A calculation device including a control unit,
    The controller is
    At least one of Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg in the blood to be evaluated having diabetes A calculation means for calculating a value of the formula using two formulas for evaluating the state of pancreatic cancer including two concentration values and a variable into which the concentration values are substituted;
    A calculation device characterized by the above.
  8. 　制御部を備えた情報処理装置において実行させるための評価プログラムであって、
    　前記制御部において実行させるための、
    　糖尿病を有する評価対象の血液中のＴｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇのうちの少なくとも２つの濃度値、または、前記濃度値が代入される変数を含む式および前記濃度値を用いて算出された前記式の値を用いて、前記評価対象について、膵臓癌の状態を評価する評価ステップ
    　を含むこと、
    　を特徴とする評価プログラム。     An evaluation program for execution in an information processing apparatus provided with a control unit,
    For executing in the control unit,
    At least one of Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg in the blood to be evaluated having diabetes An evaluation step for evaluating the state of pancreatic cancer for the evaluation object using two concentration values or an equation including a variable to which the concentration value is substituted and the value of the equation calculated using the concentration value Including
    An evaluation program characterized by
  9. 　制御部を備えた情報処理装置において実行させるための算出プログラムであって、
    　前記制御部において実行させるための、
    　糖尿病を有する評価対象の血液中のＴｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇのうちの少なくとも２つの濃度値、および、前記濃度値が代入される変数を含む膵臓癌の状態を評価するための式を用いて、前記式の値を算出する算出ステップ
    　を含むこと、
    　を特徴とする算出プログラム。     A calculation program for execution in an information processing apparatus including a control unit,
    For executing in the control unit,
    At least one of Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, Arg in the blood to be evaluated having diabetes Using a formula for evaluating a state of pancreatic cancer that includes two concentration values and a variable into which the concentration value is substituted, including a calculation step of calculating a value of the equation,
    A calculation program characterized by
  10. 　制御部を備えた評価装置と、制御部を備え、糖尿病を有する評価対象の血液中のＴｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇのうちの少なくとも２つの濃度値に関する濃度データ、または、前記濃度値が代入される変数を含む式および前記濃度値を用いて算出された前記式の値を提供する端末装置とを、ネットワークを介して通信可能に接続して構成された評価システムであって、
    　前記端末装置の前記制御部は、
    　前記濃度データまたは前記式の値を前記評価装置へ送信するデータ送信手段と、
    　前記評価装置から送信された、前記評価対象における膵臓癌の状態に関する評価結果を受信する結果受信手段と、
    　を備え、
    　前記評価装置の前記制御部は、
    　前記端末装置から送信された前記濃度データまたは前記式の値を受信するデータ受信手段と、
    　前記データ受信手段で受信した前記濃度データに含まれている前記濃度値または前記式の値を用いて、前記評価対象について、膵臓癌の状態を評価する評価手段と、
    　前記評価手段で得られた前記評価結果を前記端末装置へ送信する結果送信手段と、
    　を備えたこと、
    　を特徴とする評価システム。     An evaluation apparatus including a control unit, and a control unit including Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, and Phe in blood to be evaluated having diabetes , His, Trp, Orn, Lys, Arg, concentration data regarding at least two concentration values, or an equation including a variable to which the concentration value is substituted and the value of the equation calculated using the concentration value. An evaluation system configured by connecting a terminal device to be provided to be communicable via a network,
    The control unit of the terminal device is
    Data transmission means for transmitting the concentration data or the value of the equation to the evaluation device;
    A result receiving means for receiving an evaluation result relating to a state of pancreatic cancer in the evaluation target transmitted from the evaluation device;
    With
    The control unit of the evaluation apparatus includes:
    Data receiving means for receiving the concentration data or the value of the equation transmitted from the terminal device;
    Using the concentration value or the value of the formula included in the concentration data received by the data receiving means, evaluation means for evaluating the state of pancreatic cancer for the evaluation object;
    A result transmitting means for transmitting the evaluation result obtained by the evaluating means to the terminal device;
    Having
    An evaluation system characterized by
  11. 　制御部を備えた端末装置であって、
    　前記制御部は、
    　糖尿病を有する評価対象における膵臓癌の状態に関する評価結果を取得する結果取得手段
    　を備え、
    　前記評価結果は、前記評価対象の血液中のＴｙｒ，Ｓｅｒ，Ａｓｎ，Ｇｌｎ，Ｐｒｏ，Ｇｌｙ，Ａｌａ，Ｃｉｔ，Ｖａｌ，Ｍｅｔ，Ｉｌｅ，Ｌｅｕ，Ｔｈｒ，Ｐｈｅ，Ｈｉｓ，Ｔｒｐ，Ｏｒｎ，Ｌｙｓ，Ａｒｇのうちの少なくとも２つの濃度値、または、前記濃度値が代入される変数を含む式および前記濃度値を用いて算出された前記式の値を用いて、前記評価対象について、膵臓癌の状態を評価した結果であること、
    　を特徴とする端末装置。     A terminal device comprising a control unit,
    The controller is
    A result acquisition means for acquiring an evaluation result relating to a state of pancreatic cancer in an evaluation subject having diabetes;
    The evaluation results include Tyr, Ser, Asn, Gln, Pro, Gly, Ala, Cit, Val, Met, Ile, Leu, Thr, Phe, His, Trp, Orn, Lys, and Arg in the blood to be evaluated. Evaluating the state of pancreatic cancer for the evaluation object using at least two of these concentration values, or an expression including a variable to which the concentration value is substituted and the value of the expression calculated using the concentration value The result of
    A terminal device characterized by the above.
PCT/JP2018/003477 2017-02-02 2018-02-01 Method for evaluating pancreatic cancer in diabetes patient, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device WO2018143369A1 (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010038796A (en) * 2008-08-06 2010-02-18 Human Metabolome Technologies Inc Disease marker and measurement method therefor
WO2014084290A1 (en) * 2012-11-27 2014-06-05 味の素株式会社 Method for evaluating pancreatic cancer, pancreatic cancer evaluation device, pancreatic cancer evaluation method, pancreatic cancer evaluation program, pancreatic cancer evaluation system and information communication terminal unit
JP2014106114A (en) * 2012-11-27 2014-06-09 Ajinomoto Co Inc Pancreas cancer risk disease evaluation method, pancreas cancer risk disease evaluation device, pancreas cancer risk disease evaluation method, pancreas cancer risk disease evaluation program, pancreas cancer risk disease evaluation system and information communication terminal device

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101542037B1 (en) * 2006-12-21 2015-08-05 아지노모토 가부시키가이샤 Method of evaluating of cancer state cancer-evaluating apparatus cancer-evaluating method cancer-evaluating system cancer-evaluating program and recording medium
CN104316701B (en) * 2008-06-20 2018-05-15 味之素株式会社 The evaluation method of female genital cancer
JP2014084290A (en) 2012-10-23 2014-05-12 Asahi Kasei Chemicals Corp Butadiene containing composition
KR102204498B1 (en) * 2013-04-09 2021-01-19 아지노모토 가부시키가이샤 Method of evaluating lifestyle-related disease indicator, lifestyle-related disease indicator-evaluating apparatus, lifestyle-related disease indicator-evaluating method, lifestyle-related disease indicator-evaluating program product, lifestyle-related disease indicator-evaluating system, and information communication terminal apparatus

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010038796A (en) * 2008-08-06 2010-02-18 Human Metabolome Technologies Inc Disease marker and measurement method therefor
WO2014084290A1 (en) * 2012-11-27 2014-06-05 味の素株式会社 Method for evaluating pancreatic cancer, pancreatic cancer evaluation device, pancreatic cancer evaluation method, pancreatic cancer evaluation program, pancreatic cancer evaluation system and information communication terminal unit
JP2014106114A (en) * 2012-11-27 2014-06-09 Ajinomoto Co Inc Pancreas cancer risk disease evaluation method, pancreas cancer risk disease evaluation device, pancreas cancer risk disease evaluation method, pancreas cancer risk disease evaluation program, pancreas cancer risk disease evaluation system and information communication terminal device

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
SHI, W. ET AL.: "Osteoprotegerin is up-regulated in pancreatic cancers and correlates with cancer-associated new-onset diabetes", BIOSCIENCE TRENDS, vol. 8, no. 6, 2014, pages 322 - 326, XP055529465 *

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