KR102477319B1 - Cancer monitoring method, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device - Google Patents

Abstract

The task is to provide an evaluation method that can provide highly reliable information that can be used as a reference for determining the possibility of cancer recurrence. In the present embodiment, at least one of Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, Trp, His, Orn, and Pro, or Glu, Arg in the blood of an evaluation target in whom cancer has been found. , Orn, Cit, His, Val, Phe, Tyr, Met, Pro, Trp, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly, using concentration values of at least two of Regarding the evaluation target, the possibility of cancer recurrence is evaluated.

Description

Cancer monitoring method, calculation method, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device

The present invention relates to a cancer monitoring method, a calculation method, an evaluation device, a calculation device, an evaluation program, a calculation program, an evaluation system, and a terminal device.

BACKGROUND OF THE INVENTION [0002] Surgery therapy is a treatment for removing cancer lesions, and is said to be the first treatment to be selected as a solid cancer treatment for removing leukemia or the like. Since cancer can be cured if it can be completely removed, surgery is the most direct method of treatment.

For example, in the case of early gastric cancer without metastasis, surgery can cure almost 100% of the cancer. However, surgery is performed not only for early cancer but also for cancer that has progressed to some extent. In normal cancer surgery, not only the original dark cow (primary cow) is removed, but also the surrounding lymph nodes assuming metastasis. In the case of metastasis to the lymph node, if the lymph node is left without being removed, the cancer will recur regardless of whether the remaining part is visible or not, so the lymph node is resected slightly wider than the range in which metastasis is likely to occur. Thus, in surgical therapy, cancer can be completely cured if it remains in the primary site or if it remains in a relatively small number of lymph nodes even if there is metastasis. However, in reality, recurrence occurs after surgery because fine cancer cells may remain after surgery.

For the purpose of improving the prognosis by early detection and treatment of recurrence, postoperative follow-up (or survival) is performed. In the postoperative follow-up, a medical examination and medical examination, a blood test such as a tumor marker, and an image test are performed in combination. These examinations need to be continuously taken after surgery in order to confirm the presence or absence of cancer recurrence, which is a burden for the patient. If the risk of recurrence due to surgical therapy can be discriminated, predicted, or evaluated before and during a certain period after surgery, there is an advantage in reducing the burden of examination. In addition, the result of the discrimination, prediction, or evaluation can also serve as a judgment material when a patient selects a treatment method. In addition, the discrimination, prediction, or evaluation result can be expected to be of great help in formulating a treatment plan for a patient, improving treatment results for cancer, and preventing recurrence of metastatic cancer and managing prognosis.

On the other hand, it is known that the concentration of amino acids in blood is changed by surgical treatment for cancer. For example, it has been reported that the concentrations of seline and glutamic acid in the blood are normalized by surgery in breast cancer patients (Non-Patent Document 1). In addition, there is a report that blood concentrations of glutamine, tryptophan, alanine, glycine and arginine are decreased immediately after chest surgery in patients with esophageal cancer and lung cancer (Non-Patent Document 2). In gastric cancer patients and breast cancer patients, it has been reported that alanine and aspartic acid increase after surgery (Non-Patent Document 3).

In addition, as prior patents, Patent Documents 1, 2 and 3 relating to a method for correlating amino acid concentration and living body state are disclosed. In addition, Patent Document 4 regarding a method for evaluating the state of lung cancer using amino acid concentration, Patent Document 5 relating to a method for evaluating the state of breast cancer using amino acid concentration, and Evaluating the state of colon cancer using amino acid concentration Patent Document 6 regarding a method, Patent Document 7 regarding a method for evaluating cancer status using amino acid concentration, Patent Document 8 regarding a method for evaluating gastric cancer status using amino acid concentration, and cancer type using amino acid concentration Patent Document 9 concerning an evaluation method, Patent Document 10 for evaluating the state of female genital cancer using amino acid concentration, Patent Document 11 for evaluating the state of prostate disease including prostate cancer using amino acid concentration, and amino acid concentration Patent Document 12 on a method for evaluating the state of pancreatic cancer using amino acid concentration, Patent Document 13 on a method for evaluating the state of pancreatic cancer risk disease using amino acid concentration, and evaluating the therapeutic effect of cancer immunotherapy using amino acid concentration Patent Literature 14 related to a method is disclosed.

International Publication No. 2004/052191 International Publication No. 2006/098192 International Publication No. 2009/054351 International Publication No. 2008/016111 International Publication No. 2008/075662 International Publication No. 2008/075663 International Publication No. 2008/075664 International Publication No. 2009/099005 International Publication No. 2009/110517 International Publication No. 2009/154296 International Publication No. 2009/154297 International Publication No. 2014/084290 Japanese Unexamined Patent Publication No. 2014-106114 International Publication No. 2013/168550

Cancer Invest. 2004;22(2):203-10. Cancer. 1988;62(2):355-60. J Transl Med. 2015; 13:35

However, Non-Patent Documents 1, 2, and 3 are all short-term reports after surgical operations, and there is no report of changes in blood amino acid concentrations associated with recurrence after cancer surgery therapy. In addition, when the index formula group disclosed in Patent Literatures 1 to 14 is used to evaluate the possibility of cancer recurrence, the subject of evaluation is different.

That is, there is no biomarker using amino acids in the blood that predicts, discriminates or evaluates the possibility of cancer recurrence.

The present invention has been made in view of the above problems, and an evaluation method, evaluation device, evaluation program, evaluation system, and terminal that provide highly reliable information that can be used as a reference for determining the possibility of cancer recurrence using blood amino acid concentrations It aims to provide a device.

As a result of intensive studies to solve the above problems, the inventors of the present invention have found that the possibility of cancer recurrence can be accurately evaluated using amino acids in blood, and have completed the present invention.

That is, in order to solve the above problems and achieve the object, the evaluation method according to the present invention is based on 12 amino acids (Glu, Ser, a-ABA, Val, At least one of Met, Lys, Ile, Leu, Trp, His, Orn and Pro) or 21 amino acids (Glu, Arg, Orn, Cit, His, Val, Phe, Tyr, Met, Pro, Trp, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly), using at least two concentration values, to evaluate the possibility of cancer recurrence for the evaluation target. do.

In this specification, various amino acids are mainly abbreviated, but their official names are as follows.

(abbreviation) (full name)

Ala Alanine

Arg Arginine

Asn Asparagine

a-ABA α-Aminobutyric acid

Cit Citrulline

Gln Glutamine

Glu Glutamic acid

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

In addition, in the evaluation method according to the present invention, in the evaluation step, in the evaluation step, a formula including a variable to which a concentration value of at least one of the 12 amino acids or at least two of the 21 amino acids is substituted. Further, by calculating the value of the above formula using , the possibility of recurrence of cancer in the evaluation target is evaluated.

Further, an evaluation device according to the present invention is an evaluation device having a control unit, wherein the control unit selects at least one of the 12 types of amino acids or one of the 21 types of amino acids in the blood of an evaluation target in which cancer has been found. It is characterized by having an evaluation means for evaluating the possibility of recurrence of cancer in the evaluation target using at least two concentration values.

Further, the evaluation method according to the present invention is an evaluation method executed in an information processing device having a control unit, and among the above 12 amino acids in the blood of an evaluation target in which cancer is found, which is executed in the control unit. and an evaluation step of evaluating the possibility of recurrence of cancer in the evaluation target using concentration values of at least one or at least two of the above 21 types of amino acids.

Further, the evaluation program according to the present invention is an evaluation program to be executed in an information processing device having a control unit, and to be executed in the control unit, in the blood of an evaluation device in which cancer has been found. and an evaluation step of evaluating the possibility of recurrence of cancer in the evaluation target using concentration values of at least one of the amino acids or at least two of the above 21 amino acids.

In addition, a recording medium according to the present invention is a non-transitory computer-readable recording medium, and is characterized in that it includes programmed instructions for executing the evaluation method in an information processing device.

Further, the evaluation system according to the present invention includes an evaluation device including a control unit and a control unit, wherein at least one of the above 12 amino acids or the above 21 kinds of amino acids in the blood of an evaluation target in which cancer is found is selected. An evaluation system configured by communicably connecting terminal devices providing concentration data relating to at least two concentration values of the terminal device through a network, wherein the control unit of the terminal device transmits the concentration data to the evaluation device. data transmission means and result reception means for receiving an evaluation result regarding the possibility of recurrence of cancer transmitted from the evaluation device, wherein the control unit of the evaluation device receives the concentration data transmitted from the terminal device using concentration data receiving means and the concentration values of at least one of the 12 amino acids or at least two of the 21 amino acids included in the concentration data received by the concentration data receiving means, It is characterized by comprising evaluation means for evaluating the possibility of recurrence of cancer in a subject, and result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device.

Further, a terminal device according to the present invention is a terminal device having a control unit, wherein the control unit has result acquisition means for obtaining an evaluation result regarding the possibility of recurrence of cancer, and the evaluation result is determined when cancer is found. Characterized in that the result of evaluating the possibility of recurrence of cancer in the evaluation target using concentration values of at least one of the 12 amino acids or at least two of the 21 amino acids in the blood of the evaluation target having .

Further, according to the present invention, the terminal device is configured to be communicatively connected to an evaluation device for evaluating the possibility of recurrence of cancer via a network, and the control unit controls the blood of the evaluation target. Concentration data transmission means for transmitting concentration data relating to concentration values of at least one of the 12 amino acids or at least two of the 21 amino acids to the evaluation device, wherein the result obtaining means is configured to perform the evaluation and receiving the evaluation result transmitted from the device.

In addition, the evaluation device according to the present invention provides concentration data regarding concentration values of at least one of the 12 amino acids or at least two of the 21 amino acids in the blood of an evaluation target in which cancer has been found. An evaluation apparatus comprising a control unit communicatively connected to a terminal apparatus via a network, wherein the control unit comprises: density data receiving means for receiving the density data transmitted from the terminal apparatus; Evaluation means for evaluating the possibility of recurrence of cancer in the evaluation target using the concentration values of at least one of the 12 amino acids or at least two of the 21 amino acids included in the concentration data; and , result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device.

According to the present invention, using blood amino acid concentrations, an effect of providing highly reliable information that can be used as a reference for knowing the possibility of cancer recurrence is shown.

1 is a principle configuration diagram showing the basic principle of a first embodiment.
Fig. 2 is a principle configuration diagram showing the basic principle of a second embodiment.
Fig. 3 is a diagram showing an example of the overall configuration of this system;
Fig. 4 is a diagram showing another example of the overall configuration of this system;
Fig. 5 is a block diagram showing an example of the configuration of an evaluation apparatus 100 of the present system.
6 is a diagram showing an example of information stored in the density data file 106a.
Fig. 7 is a diagram showing an example of information stored in the indicator state information file 106b;
Fig. 8 is a diagram showing an example of information stored in a designated indicator state information file 106c;
Fig. 9 is a diagram showing an example of information stored in the expression file 106d1;
Fig. 10 is a diagram showing an example of information stored in an evaluation result file 106e;
Fig. 11 is a block diagram showing the configuration of an evaluation unit 102d;
Fig. 12 is a block diagram showing an example of the configuration of a client device 200 of this system.
Fig. 13 is a block diagram showing an example of the configuration of a database device 400 of this system.
Fig. 14 shows concentration data of 21 types of amino acids without recurrence.
Fig. 15 is a diagram showing concentration data of 21 types of amino acids in a recurrence example.
Fig. 16 is a diagram showing a radar chart showing "the distribution of each amino acid after surgery when each amino acid before surgery is 100%" related to recurrence-free cases.
Fig. 17 is a radar chart showing the "distribution of each amino acid after surgery when each amino acid before surgery is 100%" in a case of recurrence.
18 is a diagram showing the results of logistic regression analysis.
Fig. 19 is a diagram showing combinations of two variables and ROC_AUC for each combination;
Fig. 20 is a diagram showing combinations of three variables and ROC_AUC for each combination;
Fig. 21 is a diagram showing combinations of 4 variables and ROC_AUC for each combination;
22 is a diagram showing combinations of two variables and ROC_AUC for each combination.
23 is a diagram showing combinations of three variables and ROC_AUC for each combination.
24 is a diagram showing combinations of 4 variables and ROC_AUC for each combination;

Hereinafter, an embodiment of an evaluation method according to the present invention (first embodiment), and an embodiment of an evaluation device, evaluation method, evaluation program, recording medium, evaluation system, and terminal device according to the present invention (second embodiment) will be described in detail based on the drawings. In addition, this invention is not limited by these embodiments.

[First Embodiment]

[1-1. Outline of First Embodiment]

Here, it demonstrates with reference to FIG. 1 which concerns the outline of 1st Embodiment. 1 is a principle configuration diagram showing the basic principle of the first embodiment.

First, at least one of the above 12 types of amino acids or at least one of the above 21 types of amino acids in blood (including plasma, serum, etc.) of an evaluation target (for example, an individual such as animal or human) in which cancer has been found. Concentration data relating to the concentration values of at least two of the amino acids in are obtained (step S11).

Here, "cancer was found" includes, for example, that a definitive diagnosis of cancer was made. In Step S11, for example, before treatment (for example, treatment by surgery, chemotherapy, radiotherapy, cancer immunotherapy, etc.) of the discovered cancer is started for an evaluation target in which cancer has been found. Either or both of concentration data derived from the collected blood (concentration data before the start of treatment) and concentration data derived from the blood collected after the start of the treatment (concentration data after the start of the treatment) may be acquired. In addition, "before treatment is started" includes, for example, before treatment in the first narrow sense of treatment in a broad sense over a certain period of time is performed. In addition, "after the treatment is started", for example, after the initial treatment in the broad sense of treatment over a certain period of time is performed and before the final treatment in the narrow sense is performed (e.g., generally speaking "during treatment", etc.), or after treatment in the broad sense of the term has been performed (for example, commonly referred to as "after treatment", etc.), etc. are included.

In step S11, for example, concentration data measured by a company or the like that measures concentration values may be acquired. Alternatively, the concentration data may be obtained by measuring the concentration value from blood collected from the evaluation target by, for example, the following measuring method (A), (B), or (C). Here, the unit of the concentration value may be, for example, molar concentration, weight concentration, or enzyme activity, and may be obtained by multiplying or subtracting these concentrations by an arbitrary constant.

(A) Plasma is separated from blood by centrifuging the collected blood sample. All plasma samples are cryopreserved at -80°C until determination of concentration values. In the case of concentration value measurement, deproteinization is performed by adding acetonitrile, followed by pre-column derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidylcarbamate), and The concentration value is analyzed by liquid chromatograph mass spectrometry (LC/MS) (see International Publication No. 2003/069328 and International Publication No. 2005/116629).

(B) Separating plasma from blood by centrifuging the collected blood sample. All plasma samples are cryopreserved at -80°C until determination of concentration values. In measuring the concentration value, after deproteinization by adding sulfosalicylic acid, the concentration value is analyzed by an amino acid analyzer based on the principle of a 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 whose concentration values were not measured immediately after obtaining the plasma or serum are cryopreserved at -80°C until the concentration values are measured. In measuring the concentration value, the concentration value is analyzed by quantifying or the like a substance that increases or decreases by substrate recognition or a spectroscopic value using a molecule that reacts or binds to the target amino acid, such as enzyme or afterma.

Next, using the concentration values included in the concentration data obtained in step S11, the possibility of cancer recurrence is evaluated for the evaluation target (step S12). In addition, before executing step S12, data such as missing values or deviation values may be removed from the concentration data obtained in step S11. In addition, "recurrence" includes, for example, local recurrence, regional recurrence, and distant recurrence (metastasis). Further, "evaluating the possibility of cancer recurrence" includes, for example, evaluating the degree of possibility of cancer recurrence. In addition, when both the concentration data before the start of treatment and the concentration data after the start of treatment are used in step S12, for example, by calculating the ratio or difference between the concentration value before the start of treatment and the concentration value after start of treatment, You may perform evaluation using the value of a ratio or a difference.

As described above, in the first embodiment, in step S11, the concentration data of the evaluation target is acquired, and in step S12, the cancer recurrence in the evaluation target is performed using the concentration value included in the concentration data of the evaluation target acquired in step S11. Evaluate the likelihood (in other words, obtain information to evaluate the likelihood of cancer recurrence or reliable information that can be used as a reference for determining the likelihood of cancer recurrence). As a result, it is possible to precisely evaluate the possibility of cancer recurrence using blood amino acid concentration (in other words, information for evaluating the possibility of cancer recurrence or highly reliable information that can be used as a reference for knowing the possibility of cancer recurrence) can be provided). In particular, in the case of using only the concentration data before the start of treatment in step S12, the evaluation results obtained in the present embodiment can be utilized as reference information when determining a treatment method. In addition, when the concentration data after the start of treatment or after treatment is used in step S12, the evaluation results obtained in the present embodiment are used for recurrence monitoring, used as reference information when determining another treatment method, or postoperative follow-up. It can be used as reference information when selecting the period and means of

In addition, the concentration values of at least one of the above 12 amino acids or at least two of the above 21 amino acids (the above ratio or difference value may be sufficient) reflect the possibility of recurrence of cancer in the evaluation target or the evaluation target It may be determined that it is an index of the possibility of recurrence of cancer in It may be determined that the possibility of recurrence of cancer in the evaluation target is reflected or the possibility of cancer recurrence in the evaluation target serves as an index. In other words, the concentration value or the value after conversion itself may be treated as an evaluation result regarding the recurrence possibility of cancer in the evaluation target.

To make the range that the concentration value can take fall within a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or the range from -10.0 to 10.0, etc.) For example, adding or subtracting an arbitrary value for the concentration value, or converting the concentration value into a predetermined conversion method (eg, exponential conversion, logarithmic conversion, angular conversion, square root conversion, plobit conversion, reciprocal conversion, Box-Cox transformation, exponential transformation, etc.) or concentration values may be converted by combining these calculations. For example, the value of the exponential function based on the Napier number with the concentration value as the exponent (specifically, the natural logarithm when the probability of cancer recurrence (p) is defined (ln)(p/(1-p) )) may be additionally calculated (the value of p/(1-p)) when it is assumed that the concentration value is the same, or the value obtained by dividing the value of the calculated exponential function by the sum of 1 and the value (specifically As , the value of the probability p) may be additionally calculated.

Further, the concentration value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the concentration 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.

Alternatively, after normalizing the concentration distribution for each amino acid, the deviation values may be set to an average of 50 and a standard deviation of 10.

In addition, these conversions may be performed according to gender or age.

In addition, the concentration value in this specification may be the concentration value itself, or may be a value after conversion of the concentration value.

In addition, positional information regarding the position of a predetermined mark on a predetermined character appearing visibly on a display device such as a monitor or a physical medium such as paper is selected from at least one of the above 12 types of amino acids or the above 21 types of amino acids. At least two concentration values (the above-mentioned ratio or difference values may be used) among the amino acids of , or when the concentration values are converted, the values after the conversion are used to generate, and the generated positional information is used to determine the level of cancer in the evaluation target. You may decide that it reflects the possibility of recurrence or becomes an index of the possibility of cancer recurrence in the evaluation target. In addition, it is for evaluating the possibility of recurrence of cancer at a predetermined growth rate, and, for example, as a ruler marked with a scale, the upper limit value and The scale corresponding to the lower limit value appeared at least, and the like. In addition, the predetermined display corresponds to a concentration value or a value after conversion, and is, for example, a circle mark or an asterisk mark.

In addition, the concentration values of at least one of the above 12 amino acids or at least two of the above 21 kinds of amino acids (the above ratio or difference value may be sufficient) are predetermined values (average value ± 1SD, 2SD, 3SD, N quartile , the N percentile, or a cutoff value for which clinical significance has been confirmed), the possibility of cancer recurrence in the evaluation target may be evaluated when it is lower than or equal to or less than a predetermined value, or when it is greater than or equal to a predetermined value or higher than a predetermined value. In this case, instead of the concentration value itself, a concentration deviation value (a value obtained by normalizing the concentration distribution for each metabolite and each amino acid for each sex, and then converting the concentration distribution to an average of 50 and a standard deviation of 10) 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 is > 70), the possibility of cancer recurrence in the evaluation target is determined. may be evaluated

In addition, the concentration value of at least one of the above 12 amino acids or at least two of the above 21 kinds of amino acids (the above ratio or difference value may be sufficient) and the concentration value (the above ratio or difference value may be sufficient) You may evaluate the possibility of cancer recurrence in an evaluation target by calculating the value of an expression using an expression containing the variable to be substituted.

In addition, it may be determined that the value of the calculated formula reflects the possibility of recurrence of cancer in the evaluation target or becomes an index of the possibility of cancer recurrence in the evaluation target. It may be converted by the methods listed above, etc., and it may be determined that the value after conversion reflects the possibility of cancer recurrence in the evaluation target or serves as an index of the possibility of cancer recurrence in the evaluation target. In other words, the value of the formula or the value itself after conversion may be treated as an evaluation result regarding the possibility of recurrence of cancer in the evaluation target.

The range that the value of the expression can take falls within a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or the range from -10.0 to 10.0, etc.) In order to do so, for example, addition or subtraction multiplication of an arbitrary value for the value of an expression, or a predetermined conversion method (e.g., exponential conversion, logarithmic conversion, angular conversion, square root conversion, flobit conversion, reciprocal of the value of the expression) Transformation, Box-Cox transformation, power transformation, etc.), or the value of the expression may be converted by performing a combination of these calculations with respect to the value of the expression. For example, the value of the exponential function based on the Napier number with the value of the equation as the exponent (specifically, the natural logarithm when the probability of cancer recurrence (p) is defined (ln)(p/(1- The value of p/(1-p) in the case where p)) is equal to the value of the formula) may be additionally calculated, and the value obtained by dividing the value of the calculated exponential function by the sum of 1 and the value (Specifically, the value of probability p) may be additionally 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 expression 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.

Alternatively, the values of the formula may be converted into deviation values such that the average is 50 and the standard deviation is 10.

In addition, these conversions may be performed according to gender or age.

In addition, 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.

Further, when the positional information regarding the position of a predetermined display on a display device such as a monitor or a physical medium such as paper is visually displayed on a predetermined ruler, the value of an expression or the value of the expression is converted, the conversion It may be determined that the generated positional information reflects the possibility of cancer recurrence in the evaluation target or serves as an index of the possibility of cancer recurrence in the evaluation target. In addition, it is for evaluating the possibility of recurrence of cancer at a predetermined growth rate, for example, as a scaled ruler, the upper limit value in "the range that the value of the formula or the value after conversion can take, or a part of the range" and that at least the scale corresponding to the lower limit appears. In addition, a predetermined mark corresponds to a value of an expression or a value after conversion, and is, for example, a circle mark or an asterisk.

In addition, you may qualitatively evaluate the degree of possibility of cancer recurrence in the evaluation target. Specifically, "the concentration value of at least one of the above 12 amino acids or at least two of the above 21 kinds of amino acids (the above ratio or difference value may be sufficient) and one or more preset threshold values" or " Concentration values of at least one of the 12 types of amino acids or at least two of the 21 types of amino acids (the above ratio or difference value may be sufficient), and the variable to which the corresponding concentration value (the above ratio or difference value may be used) is substituted The evaluation target may be classified into any one of a plurality of categories defined in consideration of at least the degree of possibility of cancer recurrence, using a formula including, and one or a plurality of threshold values set in advance. In addition, a plurality of classifications include a classification for belonging to a subject with a high degree of cancer recurrence (for example, a subject considered to have cancer recurrence), and a subject with a low degree of cancer recurrence (for example, , a subject for whom the cancer is considered not to recur), and a category for including a subject with a moderate degree of possibility of cancer recurrence may be included. In addition, the plurality of categories may include a category for assigning subjects with a high degree of possibility of cancer recurrence, and a category for assigning subjects with a low degree of possibility of cancer recurrence. Alternatively, the concentration values (which may be the values of the ratio or difference described above) or the values of the equations may be converted by a predetermined method, and the evaluation target may be classified into any of a plurality of categories using the converted values.

In addition, regarding the formula used at the time of evaluation, the format is not particularly limited, but may be of the format shown below, for example.

・Linear models such as multiple regression, linear discriminant, principal component analysis, and canonical discriminant analysis based on the least squares method

・Generalized linear models such as logistic regression and Cox regression based on the maximum likelihood method

In addition to the generalized linear model, a generalized linear mixed model that considers random effects such as inter-individual and facility differences

Equation created by cluster analysis such as K-means method and hierarchical cluster analysis

Formulas created based on Bayesian statistics, such as MCMC (Markov Chain Monte Carlo method), Bayesian network, and 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 classification formulas

Expression expressed as the sum of expressions of different forms

In addition, even if the formula used for evaluation is created by the method described in, for example, International Publication No. 2004/052191 filed by the present applicant or International Publication No. 2006/098192 filed by the present applicant good night. In addition, as long as it is an expression obtained by these methods, the expression can be suitably used for evaluating the possibility of cancer recurrence regardless of the unit of the amino acid concentration value in the concentration data as input data.

Here, coefficients and constant terms are added to each variable in the multiple regression equation, multiple logistic regression equation, canonical discriminant function, etc., but these coefficients and constant terms are preferably real numbers, and more preferably, the above various classifications from data Any value that falls within the range of the 99% confidence interval of the coefficients and constant terms obtained to perform the above, more preferably, values that fall within the range of the 95% confidence interval of the coefficients and constant terms obtained for performing the various classifications from the data. It doesn't matter if Further, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of a constant term and its confidence interval may be multiplied by an arbitrary real constant. When logistic regression, linear discriminant, multiple regression, etc. are used for evaluation, linear transformation (constant addition, constant multiplication) and monotonically increasing (decreasing) transformation (e.g., logit transformation) improve evaluation performance. It is not replaced, but since it is equivalent to before conversion, you may use the thing after these conversions at the time of evaluation.

In addition, a fractional expression means that the numerators of the fractional expression are variables A, B, C, . . . and/or the denominator of the corresponding fractional expression is the variable a, b, c... is expressed as the sum of Further, in the fractional expression, the fractional expression α, β, γ, . . . The sum of (eg α + β) is also included. Also, the fractional expression includes a divided fractional expression. In addition, it does not matter if an appropriate coefficient is attached to each variable used for a numerator or a denominator. In addition, it does not matter even if the variable used for a numerator or a denominator overlaps. In addition, an appropriate coefficient may be attached to each fractional expression. Further, as long as the value of the coefficient of each variable or the value of the constant term is a real number, it does not matter. In addition, in a certain fractional expression and in the case where the numerator variable and the denominator variable in the fractional expression are exchanged, the positive and negative signs of the correlation with the target variable are generally reversed, but since these correlations are maintained, the evaluation performance can be regarded as equivalent. In the fractional expression, the variable of the numerator and the variable of the denominator are exchanged.

In addition, when evaluating the possibility of cancer recurrence, biometric information other than amino acids (e.g., blood metabolites other than amino acids (amino acid metabolites, sugars, lipids, etc.), concentrations of proteins, peptides, minerals or hormones, tumor Marker values, blood test values such as albumin, total protein, triglyceride, HbA1c, LDL cholesterol, HDL cholesterol, amylase, total bilirubin or uric acid, blood cytokines, number of immune cells, intracellular cytokines or delayed excess Immune-related test values such as response (DTH), image information such as ultrasound echo, upper and lower endoscopy, X-ray, CT or MRI, or age, height, weight, BMI, blood pressure, gender, smoking information, diet information, alcohol consumption Biomarkers such as information, exercise information, stress information, sleep information, family history information or disease history information (diabetes, pancreatitis, etc.) may be additionally used. In addition, biometric information other than amino acids (for example, blood metabolites other than amino acids (amino acid metabolites, saccharides, lipids, etc.) , concentrations of proteins, peptides, minerals or hormones, values of tumor markers, blood test values such as albumin, total protein, triglycerides, HbA1c, LDL cholesterol, HDL cholesterol, amylase, total bilirubin or uric acid, blood cytokines, immune function Immunity-related test values such as cell count, intracellular cytokine in charge of immunity, or delayed hypertonic response (DTH), image information such as ultrasound echo, upper and lower endoscopy, X-ray, CT, or MRI, or age, height, and weight , biomarkers such as BMI, blood pressure, gender, smoking information, meal information, drinking information, exercise information, stress information, sleep information, family history information or disease history information (diabetes, pancreatitis, etc.) are additionally included. may be

[Second Embodiment]

[2-1. Outline of Second Embodiment]

Here, the outline of the second embodiment will be described with reference to FIG. 2 . Fig. 2 is a principle configuration diagram showing the basic principle of the second embodiment. Note that in the description of the second embodiment, the description overlapping with that of the first embodiment described above is omitted in some cases. In particular, here, when evaluating the possibility of cancer recurrence, a case in which the value of the formula or the value after conversion thereof is used is described as an example, but, for example, a concentration value, a ratio of concentration values, or a difference between concentration values or these Values after conversion (for example, concentration deviation values, etc.) may be used.

The control unit controls concentration values of at least one of the above 12 amino acids or at least two of the above 21 amino acids in the blood of an evaluation target (for example, an individual such as an animal or a human) in which cancer has been found, in advance. Evaluate the possibility of cancer recurrence in the evaluation target by calculating the value of the expression using an expression previously stored in a storage unit including the corresponding concentration value included in the obtained concentration data and a variable to which the concentration value is substituted. (step S21). In addition, when both the concentration data before the start of treatment and the concentration data after the start of treatment are used in step S21, the control unit calculates, for example, a ratio or difference between the concentration value before the start of treatment and the concentration value after start of treatment , You may evaluate the possibility of recurrence of cancer about an evaluation target by substituting the computed ratio or difference value into a variable, and calculating the value of an expression.

In addition, the expression used in step S21 may be created based on the expression creation processing (steps 1 to 4) described below. Here, an outline of the expression creation process will be described. Incidentally, the processing described here is only an example, and the formula creation method is not limited to this.

First, the control unit selects a candidate expression (e.g., , y=a1×1+a2×2+…+an×n, y: index data, xi: concentration data, ai: constant, i = 1, 2, …, n) are created (step 1). In addition, the indicator state information includes concentration data (for example, data on amino acid concentration before surgery, data on amino acid concentration after surgery, or data on changes in amino acid concentration before and after surgery, etc.) and an index related to cancer recurrence. It includes data (for example, binary data on the presence or absence of recurrence, etc.).

Further, in step 1, from the index state information, a plurality of different expression creation techniques (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, decision ) may be used together to create a plurality of candidate equations. Specifically, for index status information, which is multivariate data composed of concentration data and index data obtained by analyzing blood obtained preoperatively and/or postoperatively from a plurality of recurrence and non-recurrence groups, a plurality of different algorithms are used to determine multiple groups. Candidate formulas of may be created simultaneously and in parallel. For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate expressions. Alternatively, a candidate formula may be prepared by converting index state information using a candidate expression created by performing principal component analysis and performing discriminant analysis on the converted index state information. In this way, finally, an expression optimal for evaluation can be created.

Here, the candidate equation created using principal component analysis is a linear equation including each variable that maximizes the variance of all concentration data. In addition, the candidate expression created using discriminant analysis is a high-order expression (including exponential or logarithmic) that includes each variable that minimizes the ratio of the sum of variances in each group to the variance of the total concentration data. In addition, the candidate expression created using the support vector machine is a high-order expression (including a kernel function) including each variable that maximizes the boundary between groups. In addition, the candidate expression created using the multiple regression analysis is a high-order expression including each variable that minimizes the sum of distances from all concentration data. In addition, a candidate expression created using Cox regression analysis is a linear model including a logarithmic hazard ratio, and is a linear expression including each variable that maximizes the likelihood of the model and its coefficient. In addition, the candidate expression created using logistic regression analysis is a linear model representing the logarithmic odds of probability, and is a linear expression including each variable that maximizes its probability likelihood. In addition, the k-means method searches for k neighborhoods of each density data, defines the largest number of groups among the groups to which the nearby points belong, as the group to which the data belongs, and the group defined as the group to which the input density data belongs has the highest agreement. It is a method for selecting variables that Note that the cluster analysis is a method of clustering (grouping) points that are closest to each other among all the density data. In addition, the decision tree is a method of categorizing variables and predicting a group of concentration data from patterns that variables with higher sequences can take.

Returning to the description of the expression creation process, the control unit verifies (mutually verifies) the candidate expression created in step 1 based on a predetermined verification method (step 2). Verification of the candidate formula is performed for each candidate formula created in Step 1. Further, in step 2, based on at least one of the bootstrap method, the hold-out method, the N-fold method, and the leave-one-out method, the discriminant rate, sensitivity, specificity, information amount standard, ROC_AUC (receiver area under the curve of the characteristic curve), etc. may be verified. In this way, it is possible to create candidate formulas with high predictability or robustness in consideration of index state information and evaluation conditions.

Here, the discrimination rate is the evaluation method according to the present embodiment, in which an evaluation target whose true state is negative (eg, recurrence-free evaluation target, etc.) is accurately evaluated as negative, and an evaluation target whose true state is positive (eg, recurrence-free evaluation target) It is the ratio of accurately evaluating the evaluation target, etc.) as positive. In addition, the sensitivity is the rate at which an evaluation target whose real state is positive is accurately evaluated as positive by the evaluation method according to the present embodiment. In addition, specificity is the ratio at which the evaluation target whose real state is negative is accurately evaluated as negative by the evaluation method according to the present embodiment. In addition, the Akaike information quantity criterion is a criterion indicating the degree to which observed data agrees with the statistical model in the case of regression analysis, etc. number)” is determined as the best model. In addition, 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 the value of ROC_AUC is a perfect discrimination becomes 1, and the closer this value is to 1, the higher the discrimination. In addition, predictability is the average of discriminant rates, sensitivities, and specificities obtained by repeating verification of candidate formulas. Robustness is the discriminant rate, sensitivity, and variance of specificity obtained by repeating verification of candidate formulas.

Returning to the description of the equation creation process, the control unit selects a variable of the candidate equation based on a predetermined variable selection method, thereby selecting a combination of concentration data included in the indicator state information used when creating the candidate equation (step 3). ). In step 3, selection of variables may be performed for each candidate expression created in step 1. This makes it possible to appropriately select the variables of the candidate expression. Then, process 1 is executed again using the indicator state information including the concentration data selected in process 3. Further, in step 3, the variable of the candidate expression may be selected based on at least one of the step-wise method, best-pass method, neighborhood search method, and genetic algorithm from the verification result in step 2. In addition, the best-pass method is a method of selecting variables by sequentially reducing the variables included in the candidate expression one by one and optimizing the evaluation index given by the candidate expression.

Returning to the description of the expression creation process, the control unit repeatedly executes steps 1, 2, and 3 described above, and based on the verification results accumulated thereby, the candidate expression to be used in evaluation from among a plurality of candidate expressions. By selecting , the expression used for evaluation is created (step 4). Further, in the selection of candidate formulas, there are cases in which, for example, an optimal candidate formula is selected from among candidate formulas created by the same formula creation method, and an optimal one is selected from all candidate formulas.

As described above, in the expression creation process, based on the index state information, processes related to creation of candidate expressions, verification of candidate expressions, and selection of variables of candidate expressions are systematized (systematized) in a series of flows and executed, An expression optimal for evaluating the possibility of recurrence of cancer can be created. In other words, in the formula creation process, the concentration of at least one of the above 21 amino acids is used for multivariate statistical analysis, and a variable selection method and cross-validation are combined to select an optimally robust variable combination for evaluation. Extract expressions with high performance.

[2-2. Configuration of the second embodiment]

Here, the configuration of the evaluation system (hereinafter sometimes referred to as this system) according to the second embodiment will be described with reference to FIGS. 3 to 14 . In addition, this system is only an example, and this invention is not limited to this. In particular, here, when evaluating the possibility of cancer recurrence, a case in which the value of the formula or the value after conversion thereof is used is described as an example, but, for example, a concentration value, a ratio of concentration values, or a difference between concentration values or these Values after conversion (for example, concentration deviation values, etc.) may be used.

First, the overall configuration of this system will be described with reference to FIGS. 3 and 4 . Fig. 3 is a diagram showing an example of the overall configuration of this system. 4 is a diagram showing another example of the overall configuration of this system. As shown in Fig. 3, this system comprises an evaluation apparatus 100 for evaluating the possibility of recurrence of cancer in an individual subject to evaluation, and at least one of the 12 types of amino acids or at least one of the 21 types of amino acids in the blood. It is constituted by connecting a client device 200 (corresponding to a terminal device of the present invention), which provides concentration data of an individual related to two concentration values, via a network 300 to enable communication. In this system, the client device 200 serving as a source of data used for evaluation and the client device 200 serving as a provider of evaluation results may be different. As shown in FIG. 4, this system stores, in addition to the evaluation device 100 and the client device 200, index state information used when creating expressions used for evaluation, expressions used during evaluation, and the like. It may be configured by connecting the database device 400 so that communication is possible via the network 300 .

Next, the configuration of the evaluation apparatus 100 of this system will be described with reference to FIGS. 5 to 11 . Fig. 5 is a block diagram showing an example of the configuration of the evaluation device 100 of the present system, conceptually showing only the part related to the present invention in the configuration.

The evaluation device 100 performs the evaluation through a control unit 102 such as a central processing unit (CPU) that collectively controls the evaluation device, a communication device such as a router, and a wired or wireless communication line such as a dedicated line. Connection to the communication interface unit 104 for communicatively connecting the device to the network 300, the storage unit 106 for storing various databases, tables, files, etc., and the input device 112 or output device 114 It is composed of an input/output interface unit 108, and each of these units is communicatively connected via an arbitrary communication path. Here, the evaluation device 100 may be configured with the same housing as various analysis devices (for example, an amino acid analysis device). For example, a configuration (hardware) of calculating (measuring) concentration values of at least one of the above 12 amino acids or at least two of the above 21 amino acids in blood, and outputting (printing, displaying on a monitor, etc.) the calculated values and software) may further include an evaluation unit 102d described later, and output results obtained by the evaluation unit 102d using the configuration described above.

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 an input device 112 or an output device 114 . Here, as the output device 114, in addition to a monitor (including a home television), a speaker or a printer can be used. . For the input device 112, in addition to a keyboard, mouse, or microphone, a monitor that realizes a pointing device function in cooperation with a mouse can be used.

The storage unit 106 is a storage means, and for example, memory devices such as RMA (Random Access Memory) and ROM (Read Only Memory), fixed disk devices such as hard disks, flexible disks, optical disks, and the like can be used. there is. In the storage unit 106, computer programs for giving commands to the CPU in cooperation with an OS (Operating System) to perform various processes are recorded. As shown in the figure, the storage unit 106 includes a concentration data file 106a, a ground indicator state information file 106b, a designated index state information file 106c, a food-related information database 106d, and an evaluation result file ( 106e) is stored.

The concentration data file 106a includes concentration data relating to concentration values of at least one of the 12 amino acids or at least two of the 21 amino acids in the blood (for example, concentration data before the start of treatment and concentration data after the start of treatment). either or both). 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 concentration data file 106a is configured by correlating an individual number for identifying an individual (sample) to be evaluated at once and concentration 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 light scale or an ordinal scale. In addition, in the case of a name scale or an order scale, it may be analyzed by assigning an arbitrary numerical value to each state. In addition, concentration data may be combined with values related to the above biometric information other than amino acids.

Returning to Fig. 5, the index state information file 106b stores index state information used when creating an expression. Fig. 7 is a diagram showing an example of information stored in the indicator state information file 106b. As shown in Fig. 7, the information stored in the indicator state information file 106b is configured by correlating the individual number with index data and concentration data related to cancer recurrence. Here, in FIG. 7, the index data and concentration data are treated as numerical values (ie continuous scale), but the index data and concentration data may be a light scale or an ordinal scale. In addition, in the case of a name scale or an order scale, it may be analyzed by assigning an arbitrary numerical value to each state.

Returning to Fig. 5, the specified indicator state information file 106c stores the indicator state information specified by the designation unit 102b described later. Fig. 8 is a diagram showing an example of information stored in the designated indicator state information file 106c. As shown in Fig. 8, the information stored in the designated indicator state information file 106c is configured by correlating an individual number, designated index data, and designated concentration data.

Returning to Fig. 5, the expression-related information database 106d is composed of an expression file 106d1 that stores expressions created by an expression creation unit 102c described later. The expression file 106d1 stores expressions used in evaluation. Fig. 9 is a diagram showing an example of information stored in the expression file 106d1. As shown in Fig. 9, the information stored in the expression file 106d1 includes ranks, equations (in Fig. 9, Fp(Phe,...), Fp(Gly, Leu, Phe), Fk(Gly, Leu, Phe, ...), etc.), the threshold value corresponding to each expression creation method, and the verification result of each expression (for example, the value of each expression) are mutually correlated.

Returning to Fig. 5, the evaluation result file 106e stores evaluation results obtained by the evaluation unit 102d described later. Fig. 10 is a diagram showing an example of information stored in the evaluation result file 106d. The information stored in the evaluation result file 106d includes an individual number for identifying an individual (sample) to be evaluated at once, concentration data of the individual obtained in advance, and evaluation results regarding the possibility of cancer recurrence (e.g., as described below). The value of the expression calculated by the calculation unit 102d1, the value after converting the value of the expression by the conversion unit 102d2 described later, the location information generated by the generation unit 102d3 described later, or the classification unit 102d4 described later The classification results obtained from, etc.) are correlated and configured.

Returning to Fig. 5, the control unit 102 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, etc., required data, etc., and executes various information processes based on these programs. As shown in the figure, the control unit 102 roughly classifies the receiving unit 102a, the designating unit 102b, the expression creating unit 102c, the evaluation unit 102d, the result outputting unit 102e, and the transmitting unit 102f. are equipped The control unit 102 removes data with missing values, removes data with many deviated values, and determines missing values with respect to the indicator state information transmitted from the database device 400 or the concentration data transmitted from the client device 200. Data processing such as removal of variables with a lot of data is also performed.

The receiver 102a may receive information transmitted from the client device 200 or the database device 400 (specifically, concentration data, indicator state information, equations, etc.) via the network 300 or the like. Also, the receiving unit 102a may receive data used for evaluation transmitted from a client device 200 different from the client device 200 to which the evaluation result is transmitted. The designation unit 102b designates target index data and concentration data when creating an expression.

The expression creation unit 102c creates an expression based on the indicator state information received by the reception unit 102a or the indicator state information designated by the designation unit 102b. In the case where an expression is previously stored in a predetermined storage area of the storage unit 106, the expression creation unit 102c may create an expression by selecting a desired expression from the storage unit 106. Alternatively, the expression creation unit 102c may create an expression by selecting and downloading a desired expression from another computer device (for example, the database device 400) that stores the expression in advance.

The evaluation unit 102d is included in the formula obtained in advance (for example, the formula created by the formula creation unit 102c or the formula received by the receiver 102a, etc.) and the concentration data of the individual received by the receiver 102a. By calculating the value of the formula using the concentration value, the likelihood of cancer recurrence is evaluated for the individual. In addition, the evaluation unit 102d determines the concentration value of at least one of the 12 amino acids or at least two of the 21 amino acids, the ratio of the concentration values, or the difference between the concentration values, or the value after conversion (for example, the concentration value). deviation value) may be used to evaluate the possibility of cancer recurrence in an individual.

Here, the configuration of the evaluation unit 102d will be described with reference to FIG. 11 . Fig. 11 is a block diagram showing the configuration of the evaluation unit 102d, conceptually showing only the part related to the present invention in the configuration. The evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.

The calculator 102d1 includes at least one concentration value of at least one of the 12 types of amino acids or at least two of the 21 types of amino acids (the above ratio or difference value may be used) and at least a variable to which the concentration value is substituted. Using the expression to calculate the value of the expression. Further, 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 expression calculated by the calculation unit 102d1 by, for example, the conversion method described above. Further, the evaluation unit 102d may store the value converted by the conversion unit 102d2 as an evaluation result in a predetermined storage area of the evaluation result file 106e. In addition, the conversion unit 102d2 may convert the concentration values included in the concentration data or the ratio or difference between the concentration values by, for example, the conversion method described above.

The generation unit 102d3 calculates positional information regarding the position of a predetermined mark on a predetermined ruler that appears visually on a display device such as a monitor or on a physical medium such as paper, in the formula calculated by the calculation unit 102d1. It is generated using a value or a value after conversion by the conversion unit 102d2 (a concentration value, a ratio of concentration values, a difference between concentration values, or a value after these conversions may be used). Further, the evaluation unit 102d may store the positional 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 determines the value of the equation calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (a concentration value, a ratio of concentration values, or a difference between concentration values or a value after these conversions may be used). is used to classify the individual into one of a plurality of categories defined by at least considering the degree of likelihood that the cancer will recur.)

The result output unit 102e outputs processing results (including evaluation results obtained by the evaluation unit 102d) and the like in each processing unit of the control unit 102 to the output device 114 .

The transmitter 102f transmits the evaluation result to the client device 200, which is the source of the individual concentration data, and transmits the expression or evaluation result created by the evaluation device 100 to the database device 400. In addition, the transmission unit 102f may transmit the evaluation result to a client device 200 that is different from the client device 200 from which the data used for evaluation is transmitted.

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 device 200 of the present system, conceptually showing only the part related to the present invention in the configuration.

The client device 200 communicates with the control unit 210, the ROM 220, the HD (Hard Disk) 230, the RAM 240, the input device 250, the output device 260, and the input/output IF 270 It is composed of the IF 280, and each of these units is communicatively connected via an arbitrary communication path. The client device 200 is an information processing device (e.g., a known personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone)) to which peripheral devices such as a printer, monitor, and image scanner are connected as necessary. System) terminal, portable terminal, mobile communication terminal, information processing terminal such as PDA (Personal Digital Assistant), etc.).

The input device 250 is a keyboard, mouse, or microphone. In addition, a monitor 261 described later also realizes a pointing device function in cooperation with a mouse. The output device 260 is output means for outputting received information via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, a speaker or the like may be installed in the output device 260 . Input/output IF 270 connects to input device 250 or 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 communication is possible. In other words, the client device 200 is connected to the network 300 via a communication device such as a modem, TA (Terminal Adapter) or router, and a telephone line, or via a dedicated line. As a result, the client device 200 can access the evaluation device 100 according to a predetermined communication protocol.

The control unit 210 includes a communication unit 211 and a transmission unit 212 . The receiver 211 receives various types of information such as evaluation results 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 device 100 via the communication IF 280 .

The control unit 210 may realize all or an arbitrary part of the processing performed by the control unit with a CPU and a program that is interpreted and executed by the CPU. In the ROM 220 or the HD 230, a computer program for giving commands to the CPU in cooperation with the OS and performing various processes is recorded. The computer program is executed by being loaded into the RAM 240 and constitutes the control unit 210 in cooperation with the CPU. In addition, the computer program may be recorded in an application program server connected to the client device 200 via an arbitrary network, and the client device 200 may download all or part of it as needed. In addition, all or an arbitrary part of the processing performed by the control unit 210 may be realized by hardware using wired logic or the like.

Here, the controller 210 includes an evaluation unit 210a having the same function as the function of the evaluation unit 102d provided in the evaluation device 100 (calculation unit 210a1, conversion unit 210a2, and generation unit). 210a3, and a classification unit 210a4) may be provided. Then, when the control unit 210 includes the evaluation unit 210a, the evaluation unit 210a converts the conversion unit 210a2 according to the information included in the evaluation result transmitted from the evaluation device 100. The value of the expression (which may be the concentration value, the ratio of the concentration values, or the difference between the concentration values) is converted, or the value of the expression or the converted value (the concentration value, the ratio of the concentration values, or the difference between the concentration values) is converted in the generator 210a3. The positional information corresponding to these conversion values may be generated), or the value after conversion in the classification unit 210a4 or the value after conversion (concentration value, ratio of concentration values, difference between concentration values, or values after conversion thereof) Good) may be used to classify the object into one of a plurality of divisions.

Next, the network 300 of this system will be described with reference to FIGS. 3 and 4 . The network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 in a mutually communicative manner, and is, for example, the Internet, an intranet, or a LAN (Local Area Network) (wired/ including both radios), etc. In addition, the network 300 is a value-added network (VAN), a personal computer communication network, a public switched telephone network (including both analog and digital), a dedicated line network (including both analog and digital), or a CATV ( Community Antenna TeleVision) network, portable circuit switching network or portable packet switching network (IMT (International Mobile Telecommunication) 2000 method, GSM (registered trademark) (Global System for Mobile Communications) method, PDC (Personal Digital Cellular)/PDC-P method, etc. including), wireless paging network, local wireless network such as Blue tooth (registered trademark), PHS network, satellite communication network (CS (Communication Satellite), BS (Broadcasting Satellite) or ISDB (Integrated Serviced Digital Broadcasting ) and the like) may be used.

Next, the configuration of the database device 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 device 400 of this system, conceptually showing only the part related to the present invention in the configuration.

The database device 400 stores the evaluation device 100 or index state information used when creating an expression in the database device, the expression created in the evaluation device 100, the evaluation result in the evaluation device 100, and the like. has a function As shown in Fig. 13, the database device 400 intervenes a control unit 402 such as a CPU that collectively controls the database device, a communication device such as a router, and a wired or wireless communication circuit such as a dedicated line. A communication interface unit 404 that communicatively connects the database device to the network 300, a storage unit 406 that stores various databases, tables, files (eg, web page files), and the like It is composed of an input/output interface unit 408 connected to a device 412 or an output device 414, and each of these units is communicatively connected via an arbitrary communication path.

The storage unit 406 is a storage means, and for example, a memory device such as RAM/ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like can be used. In the storage unit 406, various programs used for various processes and the like are stored. 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 an input device 412 or an output device 414 . Here, as the output device 414, in addition to a monitor (including a home television), a speaker or a printer can be used. In addition, as the input device 412, a keyboard, mouse, microphone, or a monitor that realizes a pointing device function in cooperation with a mouse can be used.

The control unit 402 has an internal memory for storing control programs such as an OS, programs defining various processing procedures, etc., required data, etc., and executes various information processes based on these programs. As shown in the figure, the control unit 402 is roughly divided and includes a transmission unit 402a and a reception unit 402b. The transmitter 402a transmits various types of information such as index state information and formulas to the evaluation device 100 . The receiver 402b receives various types of information such as expressions and evaluation results transmitted from the evaluation device 100 .

In this description, the evaluation device 100 executes from reception of the concentration data, calculation of the value of the formula, classification into categories of individuals, and transmission of the evaluation result, and the client device 200 executes the evaluation result. Although a case in which the reception of is given as an example, in the case where the evaluation unit 210a is provided in the client device 200, it is sufficient for the evaluation device 100 to calculate the value of the expression. Conversion of values, generation of positional information, classification into categories of entities, and the like may be appropriately shared between the evaluation device 100 and the client device 200.

For example, when the client device 200 receives an expression value from the evaluation device 100, the evaluation unit 210a converts the expression value in the conversion unit 210a2 or the generation unit 210a3. ) may generate positional information corresponding to the value of the expression or the value after conversion, or the classification unit 210a4 may use the value of the expression or the value after conversion to classify the object into one of a plurality of categories.

In addition, when the client device 200 receives the value after conversion from the evaluation device 100, the evaluation unit 210a generates positional information corresponding to the value after conversion in the generation unit 210a3 or classifies it. The value after conversion in unit 210a4 may be used to classify the object into one of a plurality of categories.

In the case where the client device 200 receives the value of the expression or the value after conversion and position information from the evaluation device 100, the evaluation unit 210a determines the value of the expression or the value after conversion in the classification unit 210a4. A value may be used to classify an object into one of a plurality of divisions.

[2-3. Other embodiments]

The evaluation device, evaluation method, evaluation program, evaluation system, and terminal device according to the present invention may be implemented in various different embodiments within the scope of the technical idea described in the claims, in addition to the second embodiment described above. will be.

In addition, among the processes described in the second embodiment, all or part of the processes described as being performed automatically may be manually performed, or all or part of the processes described as being performed manually may be automatically performed by a known method. can also be done with

In addition to this, information including parameters such as processing procedures, control procedures, specific names, registration data for each processing and search conditions, examples of screens, and database configurations shown in the above documents and drawings are arbitrarily provided, except where noted otherwise. can be changed

In addition, regarding the evaluation apparatus 100, each illustrated component is functionally conceptual, and is not necessarily physically configured as shown.

For example, as for the processing functions provided in the evaluation device 100, in particular, each processing function performed in the control unit 102, all or any part thereof may be realized by a CPU and a program interpreted and executed by the CPU. Alternatively, it may be realized as hardware using wired logic. In addition, the program is recorded in a non-transitory computer readable recording medium containing programmed instructions for executing the evaluation method according to the present invention in the information processing device, and is mechanically transferred to the evaluation device 100 as necessary. is read That is, computer programs for giving commands to the CPU in cooperation with the OS and performing various processes are recorded in the storage unit 106 or the like such as a ROM or a HDD (Hard Disk Drive). This computer program is executed by being loaded into RAM, and constitutes a control unit in cooperation with the CPU.

In addition, this computer program may be stored in an application program server connected to the evaluation apparatus 100 via an arbitrary network, and it is possible to download all or part of it as needed.

Further, the evaluation program according to the present invention may be stored in a non-temporary computer readable recording medium, or may be configured as a program product. Here, the "recording medium" means memory card, USB (Universal Serial Bus) memory, SD (Secure Digital) card, flexible disk, magneto-optical disk, ROM, EPROM (Erasable Programmable Read Only Memory), EPPROM (Electrically Erasable and Programmable Read Only Memory (registered trademark), CD-ROM (Compact Disc Read Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Disk), and Blu-ray (registered trademark) Disc. A portable physical medium” shall be included.

In addition, a "program" is a data processing method described in an arbitrary language or description method, regardless of format such as source code or binary code. Also, a "program" is not necessarily limited to being configured singly, but includes those configured in a distributed manner as a plurality of modules or libraries, and those that achieve their functions in cooperation with a separate program represented by the OS. In addition, in each device shown in the embodiment, with respect to the specific configuration and reading procedure for reading the recording medium, and the installation procedure after reading, well-known configurations and procedures can be used.

Various databases and the like stored in the storage unit 106 are storage means such as memory devices such as RMA and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and are used for various processing and website provision. It stores various programs, tables, databases, and files for web pages.

Further, the evaluation device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which an arbitrary peripheral device is connected. Further, the evaluation device 100 may be realized by mounting software (including programs, data, etc.) for realizing the evaluation method of the present invention in the information processing device.

In addition, the specific form of distribution/integration of the device is not limited to that shown in the figure, and all or part thereof can be configured by functionally or physically distributing/integrating in an arbitrary unit according to various additions or functional loads. . That is, the above-described embodiments may be arbitrarily combined and implemented, or the embodiments may be selectively implemented.

Example 1

Amino acid concentrations in pre- and postoperative blood samples of 72 lung cancer patients who underwent surgical therapy were measured by the measurement method (A) described in the above-described embodiment. There were 14 cases of recurrence among 72 cases.

Regarding 21 amino acids of Glu, Arg, Orn, Cit, His, Val, Phe, Tyr, Met, Pro, Trp, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA and Gly Concentration data of recurrence-free cases are shown in FIG. 14 . In Fig. 14, the horizontal axis represents the preoperative value (pre) and the postoperative value (post) of each amino acid concentration, and the vertical axis represents the average value of each amino acid concentration. As a result of the Student's t-test, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, and Trp were significantly decreased postoperatively compared to preoperatively (*: p<0.05, **: p <0.01, ***: p<0.001).

On the other hand, FIG. 15 shows the concentration data of the recurrent example for the above 21 amino acids. In Fig. 15, the horizontal axis represents the preoperative value (pre) and the postoperative value (post) of each amino acid concentration, and the vertical axis represents the average value of each amino acid concentration. As a result of Student's t-test, there were no amino acids significantly changed after surgery compared to before surgery.

16 shows a radar chart showing "the distribution of each amino acid after surgery when each amino acid before surgery is 100%" related to recurrence-free. Fig. 17 shows a radar chart showing the "distribution of each postoperative amino acid when each amino acid before surgery is assumed to be 100%" concerning recurrence cases. The changes in the amino acid profile in the relapse-free cases were different from the changes in the amino acid profiles in the relapse cases. Thus, it has been found that the characteristic plasma amino acid profile is altered when relapse can occur.

From the above, it was found that Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, and Trp, which had a significant difference only before and after surgery without recurrence, had the ability to discriminate the possibility of cancer recurrence.

Example 2

Using the same plasma amino acid concentration data as in Example 1, logistic regression analysis was performed with the target variable as presence or absence of recurrence (binary variable) (FIG. 18). In addition, logistic regression analysis uses a preoperative value (pre) as an explanatory variable, a postoperative value (post) as an explanatory variable, and a value obtained by subtracting the preoperative value from the postoperative value (post-pre) as an explanatory variable. It was performed for each case.

When pre is used as an explanatory variable, His, Met, and Trp are significantly changed. When post is used as an explanatory variable, His, a-ABA, and Orn are significantly changed, and post-pre is used as an explanatory variable. In the case of , Pro and Trp were significantly changed (p<0.05, Wald's Test). From this, it was found that the amino acid variables His, Met, Trp, a-ABA, Orn, and Pro have the ability to discriminate the possibility of cancer recurrence.

Example 3

Using the same preoperative value (pre) of plasma amino acid concentration data as in Example 1, a multivariate discriminant (multivariate function) was obtained.

Using the method described in International Publication No. 2004/052191, a combination of variables included in the logistic regression equation was searched, and a search for a logistic regression equation maximizing the discriminant performance of the possibility of cancer recurrence was intensively carried out.

In the search, the explanatory variable was preoperative value (pre), and the objective variable was recurrence (binary variable). Combinations of 2 to 4 variables obtained by total sugar were evaluated by ROC_AUC, and combinations of 2 to 4 variables with ROC_AUC greater than 0.7 were extracted.

19 shows a list of combinations of two variables in which ROC_AUC is larger than 0.7. Specific combinations of amino acids are (Arg, Trp), (Orn, Trp), (Cit, Trp), (His, Cit), (His, Orn), (His, Arg), (Val, Orn), (Try , Trp), (Gln, His), (Met, Orn), (Ala, Orn), (Asn, His), (Cit, Met), (Orn, Lys), (His, Thr), (Glu, Trp) ), (His, Trp), (Thr, Trp), (Orn, Phe), (Orn, Leu), (Phe, Trp), (Gln, Met), (Orn, Ile), (Cit, Val), (Glu, His), (Asn, Trp), (Ser, Orn), (Val, Ile), (Ile, Trp), (Leu, Trp), (Ala, Trp), (a_ABA, Trp), (Asn , Orn), (Val, Trp), (Lys, Trp), (Pro, Trp), (Ser, Trp), (His, Ile), (a_ABA, Orn), (Cit, Phe), (Arg, Met ) and (Tyr, Orn).

In addition, there were 515 combinations of three variables with ROC_AUC greater than 0.7. Fig. 20 shows a list of combinations of three variables in which ROC_AUC is greater than the maximum value of ROC_AUC in the combination of two variables. Specific combinations of amino acids are (Arg, Orn, Trp), (Glu, Met, Orn), (Cit, Orn, Trp), (Orn, Lys, Trp), (His, Cit, Orn), (Cit, Arg , Trp), (Glu, Orn, Trp), (Arg, Lys, Trp), (His, Arg, Trp), (Cit, Val, Orn), (Orn, Leu, Trp), (His, Cit, Trp) ), (Arg, Tyr, Trp), (Gln, His, Cit), (Val, Orn, Trp), (Ser, Cit, Trp), (Met, Orn, Trp), (Arg, Met, Trp), (Arg, Met, Orn), (Glu, Arg, Trp), (Asn, Arg, Trp), (Arg, Pro, Trp), (Pro, Orn, Trp), (His, Arg, Orn), (a_ABA , Orn, Trp), (Ser, Arg, Trp), (Ala, Orn, Trp), (Gln, His, Arg), (Thr, Arg, Trp), (Arg, Leu, Trp), (Orn, Phe , Trp), (Ser, Orn, Trp), (Asn, Orn, Trp), (Gly, Arg, Trp), (Gly, Orn, Trp), (Thr, Orn, Trp), (Cit, Lys, Trp) ), (Arg, a_ABA, Trp), (Tyr, Orn, Trp) and (Orn, Ile, Trp).

In addition, there were 3,369 combinations of 4 variables with ROC_AUC greater than 0.7. Fig. 21 shows a list of combinations of four variables in which ROC_AUC is larger than the maximum value of ROC_AUC in the combination of three variables. Specific amino acid combinations are (Arg, Orn, Lys, Trp), (Arg, Met, Orn, Trp), (Ser, Arg, Orn, Trp), (Arg, a_ABA, Orn, Trp), (His, Arg, Orn, Trp), (Arg, Val, Orn, Trp), (Arg, Orn, Ile, Trp), (Glu, Arg, Met, Orn), (Gly, Arg, Orn, Trp), (Cit, Orn, Leu, Trp), (Thr, Arg, Orn, Trp), (Arg, Orn, Phe, Trp), (Arg, Tyr, Orn, Trp) and (Ala, Arg, Orn, Trp).

From the above, it was found that the amino acid combinations shown in Figs. 19 to 21 have the ability to discriminate the possibility of cancer recurrence as amino acid variables.

Example 4

Based on the same plasma amino acid concentration data as in Example 1, a value obtained by subtracting the preoperative value from the postoperative value (post-pre) was calculated, and change data related to the difference was obtained. Using the obtained change amount data, a multivariate discriminant (multivariate function) for discriminating the possibility of cancer recurrence including plasma amino acid concentration as a variable, which is effective for evaluating the possibility of cancer recurrence, was obtained.

Using the method described in International Publication No. 2004/052191, a combination of variables included in the logistic regression equation was searched, and a search for a logistic regression equation maximizing the discriminant performance of the possibility of cancer recurrence was intensively carried out.

In the search, the explanatory variable was a value obtained by subtracting the preoperative value from the postoperative value (post-pre), and the objective variable was the presence or absence of recurrence (binary variable). Combinations of 2 to 4 variables obtained by total sugar were evaluated by ROC_AUC, and combinations of 2 to 4 variables with ROC_AUC greater than 0.7 were extracted.

22 shows a list of combinations of two variables in which ROC_AUC is greater than 0.7. Specific combinations of amino acids are (Ala, Trp), (Pro, Trp), (Cit, Trp), (Glu, Leu), (Arg, Trp), (Phe, Trp), (Glu, Trp), (Cit , Val), (Glu, Pro), (Orn, Trp), (Glu, Val), (Lys, Trp), (Glu, Ala), (Tyr, Trp), (His, Trp), (Asn, Trp) ) and (Leu, Trp).

In addition, there were 241 combinations of three variables with ROC_AUC greater than 0.7. Fig. 23 shows a list of combinations of three variables in which ROC_AUC is greater than the maximum value of ROC_AUC in a combination of two variables. Specific combinations of amino acids are (Pro, Tyr, Trp), (Glu, Pro, Leu), (Tyr, Phe, Trp), (Arg, Pro, Lys), (Arg, Pro, Trp), (Ala, Ile , Leu), (Ala, Arg, Trp), (Arg, Lys, Trp), (Pro, Tyr, Phe), (Glu, Ile, Leu), (Thr, Pro, Lys), (Glu, Pro, Lys ), (Thr, Pro, Tyr), (Ala, Tyr, Trp), (Cit, a_ABA, Leu), (Cit, Phe, Trp), (Arg, Pro, Phe), (Glu, Ala, Leu), (Glu, Cit, Leu), (Glu, Arg, Leu), (Arg, Pro, Leu), (His, Ala, Trp) and (Arg, Phe, Trp).

In addition, there were 2012 combinations of 4 variables with ROC_AUC greater than 0.7. Fig. 24 shows a list of combinations of four variables in which ROC_AUC is larger than the maximum value of ROC_AUC in the combination of three variables. Specific combinations of amino acids are (Glu, Ala, Ile, Leu), (Thr, Pro, Tyr, Met), (Pro, Tyr, Phe, Trp), (Thr, Pro, Tyr, Trp), (Thr, Pro , Tyr, Lys), (Arg, Pro, Tyr, Phe), (Arg, Pro, Tyr, Trp), (Arg, Pro, Ile, Leu), (Arg, Pro, Lys, Trp), (Glu, Thr , Pro, Lys), (Arg, Pro, Tyr, Leu), (Thr, Pro, Tyr, Phe), (Cit, Tyr, Phe, Trp), (Ala, Tyr, Phe, Trp), (Arg, Pro , Lys, Ile), (Pro, Tyr, Leu, Trp), (Glu, Tyr, Phe, Trp), (Thr, Arg, Pro, Lys), (Gly, Pro, Tyr, Trp), (Gln, Pro , Tyr, Trp), (His, Pro, Tyr, Trp), (Thr, Pro, Tyr, Leu), (Cit, Pro, a_ABA, Leu), (Glu, Pro, Ile, Leu), (Pro, a_ABA , Tyr, Phe), (a_ABA, Tyr, Leu, Trp), (Ser, Pro, Tyr, Trp), (Asn, Thr, Pro, Tyr), (His, Tyr, Val, Trp), (Glu, Thr , Ala, Lys), (Gly, Thr, Pro, Tyr), (Glu, a_ABA, Ile, Leu), (a_ABA, Tyr, Phe, Trp), (Glu, Thr, Ala, Leu), (Ala, Arg , Phe, Trp), (Glu, Pro, Tyr, Phe), (Ala, a_ABA, Ile, Leu), (Pro, a_ABA, Tyr, Trp), (Pro, Tyr, Val, Trp), (Glu, Pro , Val, Leu), (Glu, Pro, Orn, Leu), (Ala, Arg, Tyr, Trp), (Ala, Ile, Leu, Trp), (Cit, a_ABA, Ile, Leu), (Glu, Pro, Try, Trp), (Cit, Pro, Tyr, Phe), (Arg, Pro, Tyr, Lys), (Pro, Tyr, Ile, Trp), (Arg, Pro, Lys, Phe), (Ala, Arg, Lys, Trp), (Glu, Cit, Phe, Trp), (Ala, Arg, a_ABA, Leu), (Pro, a_ABA, Ile, Leu), (Pro, Tyr, Ile, Leu), (Thr, Cit, Pro, Lys), (Glu, Cit, Pro, Leu), (Arg, Tyr, Phe, Trp), (Glu, His, Pro, Leu) and (Arg, Pro, Leu, Trp) .

From the above, it was found that the amino acid combinations shown in Figs. 22 to 24 have the ability to discriminate the possibility of cancer recurrence as amino acid variables.

As described above, the present invention can be widely practiced in many industrial fields, particularly in the fields of medicine, food, and medicine, and is particularly useful in the field of bioinformatics for cancer recurrence evaluation.

100 Evaluation Unit
102 Control
102a receiver
102b designation
102c Expression Builder
102d evaluation department
102d1 calculation unit
102d2 conversion unit
102d3 generator
102d4 classification unit
102e result output unit
102f transmitter
104 communication interface
106 storage unit
106a concentration data file
106b Indicator Status Information File
106c Designated Indicator Status Information File
106d Expression Related Information Database
106d1 expression file
106e Evaluation Results File
108 input/output interface
112 input devices
114 output devices
200 client device (terminal device (information communication terminal device))
300 network
400 database device

Claims (12)

At least one of Trp, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, His, Orn and Pro or Trp, Glu, Arg, Orn, Concentration values of at least two of Cit, His, Val, Phe, Tyr, Met, Pro, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly, or a variable to which the concentration values are substituted. and an evaluation step of evaluating the possibility of recurrence of lung cancer in the evaluation target using a value of the formula calculated using an expression including and the concentration value. delete The evaluation method according to claim 1, characterized in that the evaluation step is executed in the control unit of an information processing apparatus having a control unit. At least one of Trp, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, His, Orn and Pro or Trp, Glu, Arg, Orn, Concentration values of at least two of Cit, His, Val, Phe, Tyr, Met, Pro, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly, and a variable to which the concentration values are substituted. Characterized in that it comprises a calculation step of calculating the value of the expression using an expression for evaluating the possibility of recurrence of lung cancer comprising a, the calculation method. The calculation method according to claim 4, characterized in that the calculation step is executed in the control unit of an information processing apparatus having a control unit. In the evaluation device having a control unit,
The control unit,
At least one of Trp, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, His, Orn and Pro or Trp, Glu, Arg, Orn, Concentration values of at least two of Cit, His, Val, Phe, Tyr, Met, Pro, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly, or a variable to which the concentration values are substituted. An evaluation unit for evaluating the possibility of recurrence of lung cancer in the evaluation target using an expression including and a value of the expression calculated using the concentration value. According to claim 6,
Is communicatively connected via a network with a terminal device that provides concentration data related to the concentration value or the value of the expression,
The control unit,
data receiving means for receiving the concentration data or the value of the expression of the evaluation target transmitted from the terminal device;
further comprising result transmission means for transmitting evaluation results obtained by the evaluation means to the terminal device;
The evaluation device according to claim 1, wherein the evaluation means uses the concentration value or the value of the expression included in the concentration data received by the data receiving means. In the calculation device having a control unit,
The control unit,
At least one of Trp, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, His, Orn and Pro or Trp, Glu, Arg, Orn, Concentration values of at least two of Cit, His, Val, Phe, Tyr, Met, Pro, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly, and a variable to which the concentration values are substituted. Using an equation for evaluating the possibility of lung cancer recurrence including, a calculation unit for calculating a value of the equation, characterized in that, the calculation device. A computer-readable recording medium recording an evaluation program for execution in an information processing device having a control unit, comprising:
For the control unit to execute,
At least one of Trp, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, His, Orn and Pro or Trp, Glu, Arg, Orn, Concentration values of at least two of Cit, His, Val, Phe, Tyr, Met, Pro, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly, or a variable to which the concentration values are substituted. and an evaluation step of evaluating the possibility of recurrence of lung cancer with respect to the evaluation target using the formula calculated using the concentration value, and a computer-readable record recording an evaluation program. media. A computer-readable recording medium recording a calculation program for execution in an information processing device having a control unit, comprising:
For the control unit to execute,
At least one of Trp, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, His, Orn and Pro or Trp, Glu, Arg, Orn, Concentration values of at least two of Cit, His, Val, Phe, Tyr, Met, Pro, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly, and a variable to which the concentration values are substituted. A computer-readable recording medium recording a calculation program, characterized in that it comprises a calculation step of calculating a value of the equation by using an equation for evaluating the recurrence possibility of lung cancer comprising a. An evaluation device provided with a control unit and a control unit, including Trp, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, His, Orn, and Pro in the blood of an evaluation target in whom lung cancer has been found. at least one of Trp, Glu, Arg, Orn, Cit, His, Val, Phe, Tyr, Met, Pro, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA and Gly Concentration data for two concentration values, or an expression including a variable to which the concentration values are substituted, and a terminal device providing a value of the expression calculated using the concentration values are communicatively connected via a network, In the configured evaluation system,
The control unit of the terminal device,
data transmission means for transmitting the concentration data or the value of the expression to the evaluation device;
Equipped with result receiving means for receiving an evaluation result regarding the possibility of recurrence of lung cancer transmitted from the evaluation device;
The control unit of the evaluation device,
data receiving means for receiving the concentration data or the value of the expression transmitted from the terminal device;
evaluation means for evaluating the possibility of recurrence of lung cancer in the evaluation target using the concentration value or the value of the expression included in the concentration data received by the data receiving means;
An evaluation system characterized by comprising a result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device. In a terminal device having a control unit,
The control unit,
Equipped with a result acquisition means for acquiring evaluation results regarding the possibility of recurrence of lung cancer;
The evaluation results indicate that at least one of Trp, Glu, Ser, a-ABA, Val, Met, Lys, Ile, Leu, His, Orn, and Pro or Trp, Glu in the blood of an evaluation subject in whom lung cancer has been found. , Arg, Orn, Cit, His, Val, Phe, Tyr, Met, Pro, Asn, Leu, Lys, Thr, Ile, Gln, Ala, Ser, a-ABA, and Gly concentration values, or the concentrations A terminal device characterized in that the result of evaluating the possibility of recurrence of lung cancer for the evaluation target using an expression including a variable to which a value is substituted and a value of the expression calculated using the concentration value.

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