JP6272065B2 - NASH diagnostic program, NASH diagnostic device operating method, and NASH diagnostic device - Google Patents

Description

  The present invention relates to a method for diagnosing nonalcoholic steatohepatitis (NASH) among nonalcoholic fatty liver disease (NAFLD).

  In recent years, the number of NAFLD patients has increased rapidly. One of the pathologies of NAFLD is NASH. NASH is accompanied by inflammatory cell infiltration, hepatocyte degeneration, necrosis, and fibrosis in addition to the deposition of neutral fat in hepatocytes. NASH is distinguished from simple fatty liver (SS) among NAFLDs, in which only fat is deposited on hepatocytes and there are no other lesions. Since it is known that a predetermined percentage of NASH patients shift to cirrhosis and hepatocellular carcinoma, it is desirable to distinguish NASH from SS and detect NASH at an early stage.

  Conventionally, a liver biopsy has been performed for a definitive diagnosis of NASH. However, since the biopsy is invasive and imposes a physical burden on the patient, a diagnosis by a non-invasive method has been desired. For this purpose, various diagnostic imaging methods such as CT, ultrasound, MRI, transient elastography, in vitro biomarkers, etc. have been studied so far. Until now, it has not been put into practical use.

The inventors of the present invention examined the possibility of evaluating the progression of NAFLD patients using ¹²³ I-BMIPP, a radiopharmaceutical marketed for fatty acid metabolism scintigraphy of heart disease, and advanced NASH patients Then, it has been found that the accumulation time of fatty acids in the liver is prolonged and the accumulation degree is lowered (Non-patent Document 1).

Special table 2008-538893 gazette

Ryogo Minamimoto et al. “Study on diagnosis of progression of NAFLD patients by fatty acid metabolism scintigraphy” Nuclear Medicine, Vol. 49, No. 3

Patent Document 1 describes that evaluation of fatty acid β-oxidation ability in the body can be performed using ¹²³ I-BMIPP. However, Patent Document 1 does not disclose a specific method for distinguishing NASH from SS.

  The present inventors' research disclosed in the above-mentioned Non-Patent Document 1 has only shown the possibility of evaluating the degree of progression of NAFLD patients. In clinical practice, there is an urgent need to effectively extract progressive NASH patients who require liver biopsy from among the fatty liver patients who are said to have more than 10 million people. In particular, it is difficult to discriminate between initial NASH and SS, and a method for discriminating this is required.

  Therefore, an object of the present invention is to provide a method capable of noninvasively diagnosing NASH, particularly early NASH and SS.

The program of the present invention is for diagnosing whether or not a patient to be examined is NASH based on data of a time activity curve obtained by scintigraphy using ¹²³ I-BMIPP for a patient to be examined. Scintigraphy using ¹²³ I-BMIPP for at least one subject known to be NASH and at least one subject known to be non-NASH to a computer for each data of the obtained time activity curves performed to a height of ¹²³ I-BMIPP incorporation into liver, the inflow rate of ¹²³ I-BMIPP, the explanatory variables and exit velocity of ¹²³ I-BMIPP The step of obtaining a fitting curve, and logistic regression analysis of the fitting curve of the subject, A step of generating a regression equation for obtaining a probability value that the patient is NASH based on the number, and the time activity curve data of the patient under examination, the height of ¹²³ I-BMIPP uptake into the liver, and ¹²³ I A step of obtaining a fitting curve having the inflow velocity of BMIPP and the outflow velocity of ¹²³ I-BMIPP as explanatory variables, and substituting the explanatory variables of the fitting curve into the regression equation, so that the patient to be examined is NASH It has the structure which performs the step which calculates a certain probability value, and the step which outputs the said probability value.

The present inventors have repeated further experiments and studies based on the findings obtained from the above research results, and the time activity curve obtained as a result of liver scintigraphy shows that the uptake of ¹²³ I-BMIPP into the liver is high. ^{It has been} found that the inflow rate of ¹²³ I-BMIPP and the outflow rate of ¹²³ I-BMIPP can be appropriately represented by a fitting curve having explanatory variables. By generating logistic regression analysis based on the explanatory variables obtained by fitting the fitting curve and the NASH / non-NASH data, a regression equation for obtaining a probability value of NASH based on the explanatory variables is generated. Thus, the present invention has been completed in which the regression value can be used to obtain the probability value that the patient to be examined is NASH. Fitting curve f is specifically the exit velocity of the height of the ¹²³ I-BMIPP uptake A, ¹²³ I-BMIPP α, as the inlet velocity of ^{123 I-BMIPP β, f =} Ae -βt -Ae ^-αt is preferred.

  In addition, the program of the present invention obtains a probability value that the subject is NASH using the regression equation, and generates an ROC curve generated using the threshold value for distinguishing NASH from non-NASH as a variable. Determining a threshold value for distinguishing NASH from non-NASH based on the step, and determining whether the patient to be tested is NASH or non-NASH based on the probability value that the patient to be examined is NASH and the threshold value. The step of discrimination may be executed. With this configuration, the patient to be examined can be identified as NASH or non-NASH with an appropriate threshold.

  The program according to another aspect of the present invention includes a step of reading the regression equation from a storage unit that stores in advance a regression equation for obtaining a probability value that the patient is NASH, which is obtained by the above method, and an explanatory variable of the fitting curve. Is substituted into the regression equation, and a step of calculating a probability value that the patient to be examined is NASH and a step of outputting the probability value are executed. Also with this configuration, the probability value that the patient to be examined is NASH can be obtained as described above.

The NASH diagnostic method of the present invention comprises performing scintigraphy using ¹²³ I-BMIPP on at least one subject known to be NASH and at least one patient known to be non-NASH. Each of the time activity curve data, and for each time activity curve data of the subject, the height of ¹²³ I-BMIPP uptake into the liver, the inflow rate of ¹²³ I-BMIPP, ^123, obtaining a fitting curve having an outflow rate of I-BMIPP as an explanatory variable, and logistic regression analysis of the fitting curve of the subject, thereby calculating a probability value that the patient is NASH based on the explanatory variable. and generating a regression formula for determining, for a patient to be examined, the scintigraphy using ¹²³ I-BMIPP Acquiring data in the time activity curves I, the data of the time-activity curve of patients of the test object, the height of the ¹²³ I-BMIPP incorporation into liver, the inflow rate of ¹²³ I-BMIPP, ¹²³ A step of obtaining a fitting curve having an outflow velocity of I-BMIPP as an explanatory variable, and a step of calculating a probability value that the patient to be examined is NASH by substituting the explanatory variable of the fitting curve into the regression equation. And outputting the probability value.

The NASH diagnostic device of the present invention is a scintigraphy using ¹²³ I-BMIPP for at least one subject known to be NASH and / or at least one subject known to be non-NASH. for each data of the obtained time activity curves performing photography, and height of ¹²³ I-BMIPP incorporation into liver, ¹²³ and inflow rate of the I-BMIPP, and the explanatory variables ¹²³ I-BMIPP exit velocity of A storage unit storing a regression equation for obtaining a probability value that the patient is NASH based on the explanatory variable, which is generated by logistic regression analysis of the fitting curve of the subject, and a test object de the relative of the patient, to acquire data of the obtained time activity curves performed scintigraphy using ¹²³ I-BMIPP A data acquisition unit, the data of the time-activity curve of patients of the test object, the height of the ¹²³ I-BMIPP incorporation into liver, the inflow rate of ¹²³ I-BMIPP, the exit velocity of ¹²³ I-BMIPP A calculation curve that obtains a fitting curve as an explanatory variable, substitutes the explanatory variable of the fitting curve into the regression equation, calculates a probability value that the patient to be examined is NASH, and an output that outputs the probability value A part.

The radiopharmaceutical of the present invention contains ¹²³ I-BMIPP, which is used for differentiation between early NASH and simple fatty liver by liver scintigraphy. Conventionally, the use of ¹²³ I-BMIPP for differentiation between early NASH and simple fatty liver by liver scintigraphy has not been known. The present invention provides a use of ¹²³ I-BMIPP that could not be conceived by those skilled in the art of non-invasive early NASH discrimination. The term “early NASH” used here refers to a stage 1-2 in the Brunt pathological classification.

According to the present invention, it is possible to appropriately distinguish whether or not a patient to be examined is NASH by liver scintigraphy using ¹²³ I-BMIPP.

It is a figure which shows the structure of the NASH diagnostic apparatus of this Embodiment. It is a figure which shows the structure of a NASH diagnostic program. (A) It is a flowchart which shows the operation | movement of the production | generation of the regression type by a NASH diagnostic apparatus, and the determination of a cutoff value. (B) It is a flowchart which shows the operation | movement which discriminate | determines whether it is NASH by a NASH diagnostic apparatus. (A) It is a graph which shows the time activity curve of simple fatty liver (SS). (B) It is a graph which shows the time radioactivity curve of early (Brunt pathological classification Stage1-2) NASH (eNASH). (C) It is a graph which shows the time radioactivity curve of progress (Stage3-4) NASH (aNASH).

  Hereinafter, a NASH diagnostic apparatus, a NASH diagnostic method, and a NASH diagnostic program according to embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a diagram illustrating a configuration of a NASH diagnostic apparatus 1 according to the embodiment. The NASH diagnostic apparatus 1 is configured by a computer including a CPU, RAM, ROM, hard disk, monitor, keyboard, communication interface, and the like. The NASH diagnostic device 1 functionally includes a data acquisition unit 2, a calculation control unit 3, a storage unit 4, and an output unit 5.

The data acquisition unit 2 has a function of acquiring scintigraphic data using ¹²³ I-BMIPP. In the present embodiment, the data acquisition unit 2 is connected to the SPECT device 6 and acquires SPECT data captured by the SPECT device 6. The data acquisition unit 2 does not need to be directly connected to the SPECT device 6. It is also possible to adopt a configuration in which SPECT data acquired at another hospital or laboratory is received via a network.

  The arithmetic control unit 3 has a function of obtaining a probability value that the patient to be inspected is NASH using the SPECT data acquired by the data acquisition unit 2 and discriminating whether or not the patient is NASH. Detailed processing performed by the arithmetic control unit 3 will be described later.

  Based on SPECT data of a patient whose NASH or non-NASH is known, the storage unit 4 is a regression equation for determining the probability value of NASH generated by the arithmetic control unit 3 or a threshold value for distinguishing NASH (hereinafter referred to as NASH). (Referred to as “cut-off value”). The storage unit 4 stores a program for causing the computer to function as the NASH diagnostic device 1.

  FIG. 2 is a diagram showing the configuration of the NASH diagnostic program 10. The NASH diagnostic program includes a fitting curve generation module 11, a regression equation generation module 12, a NASH probability value calculation module 13, a cutoff value determination module 14, an NASH discrimination module 15, and an output module 16. . When the CPU reads and executes the NASH diagnostic program 10, the functions of the arithmetic control unit 3 and the output unit 5 described above are realized.

Next, functions of the arithmetic control unit 3 will be described. The functions of the arithmetic control unit mainly use (1) a function for generating a regression equation for determining a probability value of NASH and determining a cutoff value, and (2) ¹²³ I-BMIPP of the patient to be examined. It has a function of discriminating whether or not it is NASH by applying the scintigraphy data to the regression equation. Hereinafter, it demonstrates in order.

[Function to generate regression equations]
Prior to the processing of the arithmetic control unit 3, the data acquisition unit 2 has ¹²³ I− of at least one subject known to be NASH and at least one subject known to be non-NASH. SPECT data using BMIPP is acquired. The SPECT data includes at least one subject known to be early NASH, at least one subject known to be advanced NASH, and at least one subject known to be simple fatty liver. It is preferably collected from the subject. “Early NASH” refers to that of Stage 1-2 in the Brunt pathology classification, and “advanced NASH” refers to that of Stage 3-4 in the Brunt pathology classification. These data are learning data for generating regression equations and the like. The acquired learning data may be stored in the storage unit 4.

The arithmetic control unit 3 generates a fitting curve for each of the time activity curves of SPECT data using ¹²³ I-BMIPP of a plurality of patients acquired by the data acquisition unit 2. In the present embodiment, the calculation control unit 3 uses f = Ae ^−βt −Ae ^−αt as a model equation for the fitting curve. In this equation, A, α, and β are explanatory variables, and generation of a fitting curve is synonymous with obtaining values of these explanatory variables.

  The arithmetic control unit 3 generates a fitting curve of the time activity curve of the patient using a non-linear least square method. In this embodiment, the nonlinear least square method is used. However, the method for generating the fitting curve is not limited to the nonlinear least square method, and other methods may be used. This function is realized by the fitting curve generation module 11.

  The arithmetic control unit 3 performs logistic regression analysis of the patient explanatory variables A, α, and β related to the learning data and NASH / non-NASH data, and the patient is NASH based on the explanatory variables A, α, and β. A regression equation for obtaining a probability value is generated. The arithmetic control unit 3 stores the generated regression equation in the storage unit 4. A well-known method can be employ | adopted for logistic regression analysis. This function is realized by the regression equation generation module 12.

  The arithmetic control unit 3 substitutes the explanatory variables A, α, and β obtained from the SPECT data of the patient related to the learning data into the regression equation, and calculates the probability value that each patient is NASH. This function is realized by the NASH probability value calculation module 13.

  Then, based on the probability value that each patient is NASH and the data that each patient is NASH or non-NASH, an ROC curve is generated, and an appropriate cutoff value for distinguishing NASH from non-NASH is determined. The appropriate cutoff value is determined based on the ROC curve, for example, if the standard is determined such that the sensitivity is a predetermined value or more and the sensitivity / (1-specificity) is the maximum. be able to. This function is realized by the cut-off value determination module 14.

  The ROC curve is a graph with sensitivity on the vertical axis and (1-specificity) on the horizontal axis. Sensitivity is the true positive rate, that is, the probability of determining a NASH patient as a NASH patient. (1-specificity) is the false positive rate, that is, the probability of determining a non-NASH patient as a NASH patient. If a high NASH probability value is set as the cutoff value, the true positive rate is low but the false positive rate is low, and the true positive rate and false positive rate increase as the cutoff value is lowered. The arithmetic control unit 3 determines a cutoff value for discriminating between NASH and non-NASH based on the true positive rate and the false positive rate.

[Function for distinguishing NASH]
Prior to the processing of the arithmetic control unit 3, the data acquisition unit 2 acquires SPECT data using ¹²³ I-BMIPP of the patient to be examined. The acquired data may be stored in the storage unit 4.

The arithmetic control unit 3 generates a SPECT data fitting curve using the ¹²³ I-BMIPP of the patient acquired by the data acquisition unit 2. That is, the explanatory variables A, α and β of f = Ae ^−βt −Ae ^−αt are obtained. This function is realized by the fitting curve generation module 11.

  Next, the arithmetic control unit 3 reads the regression equation from the storage unit 4 and substitutes the values of the explanatory variables A, α, and β obtained for the patient to be examined into the read regression equation. Compute a probability value that is This function is realized by the NASH probability value calculation module 13. Subsequently, the arithmetic control unit 3 compares the NASH probability value with the cut-off value, and discriminates whether or not the patient is NASH. This function is realized by the NASH discrimination module 15.

  The arithmetic control unit 3 sends the NASH discrimination result to the output unit 5, and the output unit 5 outputs the NASH discrimination result to the outside. As an output method, for example, it can be displayed on a monitor. This function is realized by the output module 16.

  3A and 3B explain the operation of the NASH diagnostic apparatus 1 according to the present embodiment. FIG. 3A is a flowchart showing the operation of the NASH diagnostic apparatus 1 for generating a regression equation, and FIG. 3B is a flowchart for discriminating whether or not the patient has NASH.

The NASH diagnostic device 1 first acquires learning data from the SPECT device 6 (S10). As described above, the learning data is SPECT data by ¹²³ I-BMIPP of at least one subject known to be NASH and at least one subject known to be non-NASH. The NASH diagnostic device 1 may store the acquired data in the storage unit 4.

  The NASH diagnostic apparatus 1 generates a SPECT data fitting curve related to the acquired learning data (S11), performs logistic regression analysis based on the obtained explanatory variables A, α, β and NASH / non-NASH data, and performs regression. Generate an expression. The NASH diagnostic device 1 stores the generated regression equation in the storage unit 4 (S12).

  Next, the NASH diagnostic device 1 substitutes the explanatory variable obtained from the SPECT data of the patient related to the learning data into the regression equation, and calculates the probability value of NASH. Then, as described above, the NASH diagnostic device 1 determines a cutoff value for distinguishing NASH / non-NASH based on the ROC curve having the cutoff value as a variable, and stores it in the storage unit 4 (S13). . In this embodiment, the learning data used to generate the regression equation is used. However, if the NASH / non-NASH data is known, the data of the patient used to generate the regression equation is used. It is also possible to use data different from the data.

Next, with reference to FIG. 3B, an operation for identifying whether or not the patient is NASH by the NASH diagnostic apparatus 1 will be described. First, the NASH device acquires SPECT data based on ¹²³ I-BMIPP of the patient from the SPECT device 6 (S20). The NASH diagnostic device 1 may store the acquired SPECT data in the storage unit 4.

  The NASH diagnostic apparatus 1 generates a fitting curve of the acquired SPECT data (S21), and obtains explanatory variables A, α, and β. Subsequently, the NASH diagnostic apparatus 1 reads the regression equation from the storage unit 4 and substitutes the explanatory variables A, α, and β into the regression equation to calculate a probability value that the patient is NASH (S22).

  Next, the NASH diagnostic device 1 discriminates whether the patient is NASH or non-NASH depending on whether or not the NASH probability value is equal to or greater than the cutoff value (S23). The NASH diagnostic apparatus 1 outputs the identified result to the outside, for example, by displaying it on a monitor (S24). The configuration and operation of the NASH diagnostic apparatus 1 according to the embodiment have been described above.

4 (a) to 4 (c) are diagrams showing the results of liver scintigraphy. FIG. 4 (a) shows the time activity curve of simple fatty liver (SS), FIG. 4 (b) shows the time activity curve of early (Brunt pathological classification Stage1-2) NASH (eNASH), and FIG. 4 (c) progression. (Stage3-4) It is a graph which respectively shows the time activity curve of NASH (aNASH). 4A to 4C, SS, eNASH, and aNASH have different levels of ¹²³ I-BMIPP uptake into the liver, their speed, and the like.

The NASH diagnostic apparatus 1 according to the present embodiment ^obtains a fitting curve of SPECT data by ¹²³ I-BMIPP of a patient related to learning data using f = Ae ^−βt −Ae ^−αt as a model formula, and uses the obtained fitting curve. With the configuration for distinguishing NASH from non-NASH based on the regression equation generated in this way, it is possible to diagnose NASH appropriately noninvasively. This means that among f = Ae ^−βt −Ae ^−αt , the explanatory variable A represents the height of ¹²³ I-BMIPP uptake into the liver, the explanatory variable α represents the outflow rate of ¹²³ I-BMIPP, and the explanatory variable β represents the inflow speed of ¹²³ I-BMIPP, and it is considered that the difference in the characteristics of the NASH and non-NASH curves appears appropriately in the explanatory variables.

  Further, the NASH diagnostic apparatus 1 according to the present embodiment determines a cutoff value for distinguishing NASH from non-NASH based on the ROC curve, and performs NASH / non-NASD discrimination using this cutoff value. Therefore, NASH can be diagnosed easily.

In the above-described embodiment, the NASH diagnostic apparatus 1, the NASH diagnostic method, and the program acquire SPECT data by ¹²³ I-BMIPP of a plurality of patients related to learning data, and generate a regression equation for determining the probability of NASH. However, the NASH diagnosis apparatus 1 and NASH diagnosis method of the present invention store the regression equation obtained beforehand using the learning data in the storage unit 4 and perform NASH diagnosis using the regression equation. Also included is an apparatus and method for performing. That is, an apparatus, a method, and a program that perform only the operation of FIG. 3B are also included in the scope of the present invention.

  Further, in the above-described embodiment, the NASH diagnostic device 1, the NASH diagnostic method, and the program, after obtaining the NASH probability value, discriminate between NASH and non-NASH based on the cut-off value. However, the NASH diagnosis apparatus 1, NASH diagnosis method, and program of the present invention may output NASH probability values. By providing the NASH probability value, the doctor can comprehensively determine whether or not the NASH is based on the result of the NASSH probability value and other findings.

Next, examples of the present invention will be described.
[Learning data]
The subjects were 14 NAFLD patients (male / female = 11/3 (person), average age 49 (years)) and one healthy person who had pathological diagnosis by liver biopsy. The breakdown of 14 NAFLD patients was 4 simple fatty liver (SS), 6 early NASH (eNASH), and 4 advanced NASH (aNASH).

[Method]
After rapid intravenous injection of ¹²³ I-BMIPP111MBq to the patient, liver scintigraphy up to 60 minutes later was performed using a 2-head gamma camera.

[result]
A fitting curve of SPECT data obtained by liver scintigraphy was obtained. The explanatory variables A, α, and β of the fitting curve gave the following results.

Subsequently, when the above result is subjected to logistic regression analysis, the odds ratio Xb is
Xb = −14.11−0.58 × A / 16363 × β + 2.321 × α
I was asked. Using the odds ratio Xb, the formula for determining the NAS probability value Pr is:
Pr = 1 / (e ^−Xb +1)
It is represented by

Using this regression equation, the probability values of NASH of 15 patients were obtained, and the following results were obtained.

The ROC curve described based on the above results is as follows.

  In the ROC curve, the larger the area of AUC (Area Under Curve), the better the determination method. In this example, AUC was 0.980, indicating that this is a very good discrimination method.

INDUSTRIAL APPLICABILITY According to the present invention, liver scintigraphy using ¹²³ I-BMIPP can noninvasively identify whether or not a patient to be examined is NASH, and is useful for diagnosing whether or not it is NASH.

DESCRIPTION OF SYMBOLS 1 NASH diagnostic apparatus 2 Data acquisition part 3 Operation control part 4 Memory | storage part 5 Output part 6 SPECT apparatus 10 NASH diagnostic program 11 Fitting curve generation module 12 Regression expression generation module 13 NASH probability value calculation module 14 Cutoff value determination module 15 NASH discrimination Module 16 Output module

Claims (10)

A program for diagnosing whether or not a patient to be examined is NASH based on data of a time activity curve obtained by scintigraphy using ¹²³ I-BMIPP, comprising:
Fitting of ¹²³ I-BMIPP to the liver, ¹²³ I-BMIPP inflow rate, and ¹²³ I-BMIPP outflow rate as explanatory variables for the time activity curve data of the patient to be examined A step of determining a curve;
Time radiation obtained by performing scintigraphy using ¹²³ I-BMIPP on at least one subject known to be NASH and at least one subject known to be non-NASH, respectively. for each data capacity curve, determined the height of the ¹²³ I-BMIPP incorporation into liver, the inflow rate of ¹²³ I-BMIPP, the fitting curve of an explanatory variable and a flux rate of ¹²³ I-BMIPP, said subject Reading the regression equation from a storage unit that stores a regression equation that is generated by performing logistic regression analysis of the person's fitting curve and obtaining a probability value that the patient is NASH based on the explanatory variable;
Substituting the explanatory variable of the fitting curve into the regression equation to calculate a probability value that the patient to be examined is NASH;
Outputting the probability value;
A program that executes Said fitting curve f is the flow rate of ¹²³ I-BMIPP of the height of the uptake A, ¹²³ I-BMIPP α, as the inlet velocity of ¹²³ I-BMIPP beta,
f = Ae ^-βt -Ae ^-αt
The program according to claim 1. In the computer,
A probability value that the subject is NASH is obtained using the regression equation, and NASH and non-NASH determined based on a ROC curve generated using a threshold value for distinguishing NASH from non-NASH as a variable. Reading the threshold value from the storage unit storing the threshold value for discrimination;
Differentiating whether the patient to be examined is NASH or non-NASH based on the probability value that the patient to be examined is NASH and the threshold value;
The program according to claim 1 or 2, wherein the program is executed. A program for diagnosing whether or not a patient to be examined is NASH based on data of a time activity curve obtained by scintigraphy using ¹²³ I-BMIPP, comprising:
Time radiation obtained by performing scintigraphy using ¹²³ I-BMIPP on at least one subject known to be NASH and at least one subject known to be non-NASH, respectively. for each data capacity curve, and determining the height of the ¹²³ I-BMIPP incorporation into liver, the inflow rate of ¹²³ I-BMIPP, the fitting curve of an explanatory variable and a flux rate of ¹²³ I-BMIPP,
Generating a regression equation for determining a probability value that the patient is NASH based on the explanatory variable by logistic regression analysis of the fitting curve of the subject;
Fitting of ¹²³ I-BMIPP to the liver, ¹²³ I-BMIPP inflow rate, and ¹²³ I-BMIPP outflow rate as explanatory variables for the time activity curve data of the patient to be examined A step of determining a curve;
Substituting the explanatory variable of the fitting curve into the regression equation to calculate a probability value that the patient to be examined is NASH;
Outputting the probability value;
A program that executes Said fitting curve f is the flow rate of ¹²³ I-BMIPP of the height of the uptake A, ¹²³ I-BMIPP α, as the inlet velocity of ¹²³ I-BMIPP beta,
f = Ae ^-βt -Ae ^-αt
The program according to claim 4. In the computer,
To obtain a probability value that the subject is NASH using the regression equation, and to distinguish NASH from non-NASH based on a ROC curve generated using a threshold value for distinguishing NASH from non-NASH as a variable Determining a threshold of
Differentiating whether the patient to be examined is NASH or non-NASH based on the probability value that the patient to be examined is NASH and the threshold; and
The program according to claim 4 or 5, wherein the program is executed. The NASH diagnostic apparatus is obtained by performing scintigraphy using ¹²³ I-BMIPP for at least one subject known to be NASH and at least one subject known to be non-NASH. It was, acquiring each data time activity curve,
The NASH diagnosis apparatus, the data of the time-activity curve of the subject, and the height of the ¹²³ I-BMIPP incorporation into liver, the inflow rate of ¹²³ I-BMIPP, the outflow speed of ¹²³ I-BMIPP Obtaining a fitting curve with as an explanatory variable,
The NASH diagnostic device performs a logistic regression analysis of the subject's fitting curve to generate a regression equation for obtaining a probability value that the patient is NASH based on the explanatory variable;
The NASH diagnostic device performs scintigraphy using ¹²³ I-BMIPP for a patient to be examined to obtain time-activity curve data;
The NASH diagnosis apparatus, the data of the time-activity curve of patients of the test object, the height of the ¹²³ I-BMIPP incorporation into liver, the inflow rate of ¹²³ I-BMIPP, the outflow speed of ¹²³ I-BMIPP Obtaining a fitting curve with as an explanatory variable,
The NASH diagnostic device substituting explanatory variables of the fitting curve into the regression equation to calculate a probability value that the patient to be examined is NASH;
The NASH diagnostic device outputting the probability value;
A method of operating a NASH diagnostic device comprising : Said fitting curve f is the flow rate of ¹²³ I-BMIPP of the height of the uptake A, ¹²³ I-BMIPP α, as the inlet velocity of ¹²³ I-BMIPP beta,
f = Ae ^-βt -Ae ^-αt
The operation method of the NASH diagnostic apparatus according to claim 7. The NASH diagnosis device obtains a probability value that the subject is NASH using the regression equation, and determines whether the subject is NASH or not based on a ROC curve generated using a threshold for distinguishing NASH from non-NASH as a variable. Determining a threshold for differentiating from NASH;
The NASH diagnostic device discriminates whether the patient to be inspected is NASH or non-NASH based on the probability value that the patient to be inspected is NASH and the threshold; and
The operation | movement method of the NASH diagnostic apparatus of Claim 7 or 8 which has these. Time radioactivity obtained by scintigraphy using ¹²³ I-BMIPP for at least one subject known to be NASH and at least one subject known to be non-NASH for each data curve, the height of the ¹²³ I-BMIPP incorporation into liver, determined the inflow rate of ¹²³ I-BMIPP, the fitting curve of an explanatory variable and a flux rate of ¹²³ I-BMIPP, the subject A storage unit that stores a regression equation that is generated by performing logistic regression analysis of the fitting curve of and calculates a probability value that the patient is NASH based on the explanatory variable;
A data acquisition unit for acquiring data of a time activity curve obtained by performing scintigraphy using ¹²³ I-BMIPP for a patient to be examined;
Fitting of ¹²³ I-BMIPP to the liver, ¹²³ I-BMIPP inflow rate, and ¹²³ I-BMIPP outflow rate as explanatory variables for the time activity curve data of the patient to be examined Calculating a curve, substituting explanatory variables of the fitting curve into the regression equation, and calculating a probability value that the patient to be examined is NASH;
An output unit for outputting the probability value;
A NASH diagnostic apparatus comprising: