Determination Device, Determination Method, and Program

Abstract

To provide a determination device, a determination method and a program that can determine the risk of Cushing's syndrome on the basis of abdominal image data.SOLUTION: A determination device includes an image acquisition unit that acquires a subject's waist CT image, an area calculating unit that calculates an iliopsoas muscle area and a visceral fat area from the waist CT image, an IVR calculating unit that calculates IVR, a ratio of the calculated iliopsoas muscle area and visceral fat area, and a determination unit that determines the risk of Cushing's syndrome of the subject on the basis of the calculated IVR.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a determination device, a determination method, and a program. [Background technology]

[0002] Cushing's syndrome is a condition in which the body produces an excess of corticosteroids (steroids). This condition can occur when excessive corticosteroids are secreted from tumors in the adrenal glands or pituitary gland, or when excessive use of steroid drugs used to treat autoimmune diseases causes a similar condition.

[0003] Cushing's syndrome is known to cause an increase in fat and a decrease in muscle mass, and is also known to be associated with an increase in visceral fat. Regarding the relationship between Cushing's syndrome and visceral fat mass, abdominal fat distribution in Cushing's syndrome (CS) has been investigated using computed tomography (CT). Research has shown that there is no significant difference between Cushing's syndrome and nonfunctioning adrenal tumors in the ratio of visceral fat mass to total fat mass and the ratio of visceral fat mass to subcutaneous fat mass. Total muscle mass (rectus abdominis, oblique abdominal muscles, iliopsoas, quadratus lumborum, and paraspinal muscles) is significantly lower in Cushing's syndrome (see, for example, Non-Patent Document 1). It is known that the ratio of visceral fat mass to subcutaneous fat mass is significantly increased in Cushing's syndrome (see, for example, Non-Patent Document 2). In addition, the ratio of visceral fat area to subcutaneous fat area has been proposed as an auxiliary diagnostic method for Cushing's syndrome using CT, but opinions are divided as to its usefulness. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Danae A. Delivanis, Nicole M. Iniguez-Ariza, Muhammad H.Zeb, Michael R.Moynagh, Naoki Takahashi, Travis J. McKenzie, Melinda A. Thomas, Charalambos Gogos, William F Young, Irina Bancos, Venetsana Kyriazopoulou, “Impact of hypercortisolism on skeletal muscle mass and adipose tissue mass in patients with adrenal adenomas”, Clinical Endocrinology. 2018; 88:209-216. [Non-patent document 2] AG Rockall, SA Sohaib, D Evans, G Kaltsas, AM Isidori, JP Monson, GM Besser, AB Grossman and RH Reznek, “Computed tomography assessment of fat distribution in male and female patients with Cushing's syndrome”, European Journal of Endocrinology (2003) 149 561-567 Summary of the Invention [Problem to be solved by the invention]

[0005] To diagnose Cushing's syndrome, it is necessary to demonstrate excess levels of adrenocortical hormones in the blood and urine. However, because adrenocortical hormones easily rise under stress, there are many false positives. A definitive diagnosis of Cushing's syndrome requires a stress test under bed rest in hospital, which is costly and burdensome for the patient. Furthermore, when hormone testing is outsourced, it can take up to a week for the results to become available.

[0006] An object of the present invention is to provide a determination device, a determination method, and a program that can determine the possibility of Cushing's syndrome based on abdominal image data. [Means for solving the problem]

[0007] One embodiment of the present invention is a determination device comprising an image acquisition unit that acquires lumbar CT images of a subject, an area calculation unit that calculates the iliopsoas muscle area and visceral fat area from the lumbar CT images, an IVR calculation unit that calculates IVR, which is the ratio of the calculated iliopsoas muscle area to the visceral fat area, and a determination unit that determines the possibility of Cushing's syndrome in the subject based on the calculated IVR. In one embodiment of the present invention, in the aforementioned determination device, the lumbar CT image is a CT image sliced ​​through the third lumbar vertebra. In one embodiment of the present invention, in the aforementioned determination device, the iliopsoas muscle includes a psoas major muscle, and the area calculation unit calculates the area of ​​the psoas major muscle as the iliopsoas muscle area. One embodiment of the present invention is the above-mentioned determination device, further comprising a height information acquisition unit that acquires the height of the subject, and a PMI calculation unit that calculates the PMI, which is the ratio of the iliopsoas muscle area to the height, from the calculated iliopsoas muscle area and the acquired height, and the determination unit determines the possibility of Cushing's syndrome in the subject based on the calculated IVR and the PMI. One embodiment of the present invention is directed to the above-mentioned judgment device, which further includes a gender information acquisition unit that acquires information identifying the gender of the subject, and the judgment unit acquires a threshold value for determining the possibility of Cushing's syndrome based on the acquired information identifying the gender of the subject, and determines the possibility of Cushing's syndrome of the subject based on the calculated IVR based on the acquired threshold value. One embodiment of the present invention is directed to the above-mentioned judgment device, which further includes an age information acquisition unit that acquires information identifying the age of the subject, and the judgment unit acquires a threshold value for determining the possibility of Cushing's syndrome based on the acquired information identifying the age of the subject, and determines the possibility of Cushing's syndrome in the subject based on the calculated IVR based on the acquired threshold value. One embodiment of the present invention is the above-mentioned determination device, further comprising a white blood cell information acquisition unit that acquires information specifying the eosinophil count and neutrophil count of the subject, and an ENR calculation unit that calculates an ENR from the information specifying the eosinophil count and neutrophil count, and the determination unit determines the possibility of Cushing's syndrome in the subject based on the calculated ENR and the IVR.

[0008] One embodiment of the present invention is a computer-implemented determination method comprising the steps of acquiring a lumbar CT image of a subject, calculating an iliopsoas muscle area and a visceral fat area from the lumbar CT image, calculating an IVR, which is the ratio of the calculated iliopsoas muscle area to the visceral fat area, and determining the possibility of Cushing's syndrome in the subject based on the calculated IVR.

[0009] One embodiment of the present invention is a program that causes a computer to perform the following steps: acquiring a lumbar CT image of a subject; calculating the iliopsoas muscle area and visceral fat area from the lumbar CT image; calculating an IVR, which is the ratio of the calculated iliopsoas muscle area to the visceral fat area; and assessing the possibility of Cushing's syndrome in the subject based on the calculated IVR. [Effects of the Invention]

[0010] According to the embodiments of the present invention, it is possible to provide a determination device, a determination method, and a program that can determine the possibility of Cushing's syndrome based on abdominal image data. [Brief explanation of the drawings]

[0011] [Figure 1]FIG. 1 is a diagram illustrating an example of a determination device according to an embodiment of the present invention. [Figure 2] 3A and 3B are diagrams illustrating an example of image data input to the determination device according to the present embodiment. [Figure 3] FIG. 4 is a diagram illustrating an example of the operation of the determination device according to the present embodiment. [Figure 4A] FIG. 10 is a diagram illustrating Example 1 of receiver operating characteristics. [Figure 4B] FIG. 10 is a diagram illustrating Example 2 of receiver operating characteristics. [Figure 5] FIG. 10 is a diagram illustrating an example of a determination device according to a first modified example of the embodiment. [Figure 6] FIG. 10 is a diagram illustrating an example of the operation of a determination device according to a first modified example of the embodiment. [Figure 7] FIG. 10 is a diagram illustrating an example of a determination device according to a second modified example of the embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of the operation of a determination device according to a second modification of the embodiment. [Figure 9] FIG. 10 is a diagram illustrating an example of a determination device according to a third modified example of the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of the operation of a determination device according to a third modified example of the embodiment. [Figure 11] FIG. 10 is a diagram illustrating an example of a determination device according to a fourth modified example of the embodiment. [Figure 12] FIG. 10 is a diagram illustrating an example of the operation of a determination device according to a fourth modified example of the embodiment. [Figure 13] FIG. 10 is a diagram illustrating Example 3 of receiver operating characteristics. DETAILED DESCRIPTION OF THE INVENTION

[0012] Next, a determination device, a determination method, and a program according to an embodiment of the present invention will be described with reference to the drawings. The embodiment described below is merely an example, and the embodiment to which the present invention is applied is not limited to the following embodiment. In all the drawings for explaining the embodiments, the same reference numerals are used for components having the same functions, and repeated explanations will be omitted. Furthermore, in this application, "based on XX" means "based on at least XX," and includes cases where it is based on other elements in addition to XX. Furthermore, "based on XX" is not limited to cases where XX is used directly, but also includes cases where it is based on XX that has been calculated or processed. "XX" is any element (for example, any information).

[0013] (Embodiment) (judgment device) DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A determination device according to an embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram illustrating an example of a determination device according to an embodiment of the present invention. A determination device 100 according to an embodiment determines the possibility of Cushing's syndrome in a subject. The determination device 100 is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, or an industrial computer. The determination device 100 includes, for example, an input unit 102, an image acquisition unit 104, an area calculation unit 106, an IVR calculation unit 108, a determination unit 110, an output unit 112, and a storage unit 114.

[0014] The input unit 102 includes an input device. Image data is input to the input unit 102. The image acquisition unit 104 acquires image data input to the input unit 102. An example of the image data is CT (Computed Tomography) image data of the subject's lower back. 2 is a diagram showing an example of image data input to the determination device according to this embodiment. As an example of a lumbar CT image, Fig. 2 shows a CT image of a healthy subject, with the slice plane set at the level of the third lumbar vertebra. The area indicated by "V-fat" on lumbar CT images is visceral fat, and its area is called the visceral fat area. The area indicated by "S-fat" on lumbar CT images is subcutaneous fat, and its area is called the subcutaneous fat area. The area indicated by "Ip-M" on lumbar CT images is the iliopsoas muscle, and its area is called the iliopsoas muscle area. The area indicated by "Ps-M" on lumbar CT images is the paraspinal muscle, and its area is called the paraspinal muscle area.

[0015] Visceral fat is fat that surrounds organs such as the stomach and intestines. Visceral fat area is the area of ​​fat in the cross-sectional area of ​​the body. Here, visceral fat area is the area of ​​fat in a CT image with a slice plane at the height of the third lumbar vertebra. Subcutaneous fat is the fat that accumulates under the skin in the lower abdomen, around the waist, and buttocks. Subcutaneous fat area is the area of ​​subcutaneous fat that occupies the cross-sectional area of ​​the body. Here, subcutaneous fat area is the area of ​​subcutaneous fat that occupies the CT image with the slice plane at the height of the third lumbar vertebra. The iliopsoas muscle area is a muscle that is related to the iliacus and psoas major muscles. The iliopsoas muscle area is the area of ​​the iliopsoas muscle relative to the cross-sectional area of ​​the body. Here, the iliopsoas muscle area is the area of ​​the psoas major muscle in a CT image with the slice plane at the height of the third lumbar vertebra. It is known that Cushing's syndrome causes an increase in fat and a decrease in muscle. In other words, in Cushing's syndrome, visceral fat area and subcutaneous fat area increase, and iliopsoas muscle area decreases. Furthermore, it is known that visceral fat is more likely to increase than other types of fat. In other words, visceral fat area increases. Let's return to Figure 1 and continue our explanation.

[0016] The area calculation unit 106 acquires the image data acquired by the image acquisition unit 104. The area calculation unit 106 calculates the iliopsoas area and the visceral fat area based on the acquired image data. For example, the area calculation unit 106 may count the number of pixels in the iliopsoas portion and calculate the iliopsoas area based on the counted pixel number. Specifically, the area calculation unit 106 may use the counted pixel number in the iliopsoas portion as the iliopsoas area. For example, the area calculation unit 106 may count the number of pixels in the visceral fat portion and calculate the visceral fat area based on the counted number of pixels. Specifically, the area calculation unit 106 may use the counted number of pixels in the visceral fat portion as the visceral fat area. The IVR calculation unit 108 acquires the iliopsoas muscle area and the visceral fat area calculated by the area calculation unit 106, and calculates the IVR based on the acquired iliopsoas muscle area and visceral fat area. Specifically, the IVR calculation unit 108 calculates the IVR by calculating the ratio of the iliopsoas muscle area to the visceral fat area (iliopsoas muscle area / visceral fat area).

[0017] The determination unit 110 acquires the IVR calculated by the IVR calculation unit 108 and determines the possibility of Cushing's syndrome in the subject based on the acquired IVR. Specifically, the determination unit 110 determines whether the IVR is less than the PMI threshold. If the IVR is less than the IVR determination threshold, the determination unit 110 determines that there is a high possibility of Cushing's syndrome, and if the IVR is equal to or greater than the IVR determination threshold, the determination unit 110 determines that there is a low possibility of Cushing's syndrome. The IVR determination threshold is set in advance. An example of the IVR determination threshold is equal to or greater than 3.5 and less than 5. The output unit 112 acquires the determination result of the possibility of Cushing's syndrome from the determination unit 110. The output unit 112 may display the acquired determination result of the possibility of Cushing's syndrome on a display unit (not shown) or may output it as sound. The storage unit 114 is realized by a hard disk drive (HDD), a flash memory, a random access memory (RAM), a read only memory (ROM), etc. The storage unit 114 stores programs such as a determination program (application).

[0018] All or part of the image acquisition unit 104, area calculation unit 106, IVR calculation unit 108, judgment unit 110, and output unit 112 are functional units (hereinafter referred to as software functional units) that are realized by a processor such as a CPU (Central Processing Unit) executing a program stored in the memory unit 114. In addition, all or part of the image acquisition unit 104, area calculation unit 106, IVR calculation unit 108, judgment unit 110, and output unit 112 may be realized by hardware such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field-Programmable Gate Array), or may be realized by a combination of software functional units and hardware.

[0019] (Operation of the determination device) FIG. 3 is a diagram showing an example of the operation of the determination device according to this embodiment. (Step S1-1) In the determination device 100, image data of a subject is input to the input unit 102. The image acquisition unit 104 acquires the image data of the subject input to the input unit 102. (Step S2-1) In the determination device 100, the area calculation unit 106 acquires image data of the subject acquired by the image acquisition unit 104. The area calculation unit 106 calculates the iliopsoas muscle area and the visceral fat area based on the acquired image data of the subject.

[0020] (Step S3-1) In the determination device 100, the IVR calculation unit 108 acquires information specifying the iliopsoas muscle area and information specifying the visceral fat area calculated by the area calculation unit 106, and calculates the IVR based on the acquired information specifying the iliopsoas muscle area and information specifying the visceral fat area. (Step S4-1) In the determination device 100, the determination unit 110 acquires the IVR calculated by the IVR calculation unit 108, and determines the possibility of Cushing's syndrome in the subject based on the acquired IVR. (Step S5-1) In the determination device 100, the output unit 112 acquires the determination result of the possibility of Cushing's syndrome from the determination unit 110. The output unit 112 may display the acquired determination result of the possibility of Cushing's syndrome on a display unit (not shown) or may output it as sound.

[0021] According to the determination device 100 of this embodiment, the determination device 100 includes an image acquisition unit 104 that acquires lumbar CT images of the subject, an area calculation unit 106 that calculates the iliopsoas muscle area and visceral fat area from the lumbar CT images, an IVR calculation unit 108 that calculates the IVR, which is the ratio between the calculated iliopsoas muscle area and the visceral fat area, and a determination unit 110 that determines the possibility of Cushing's syndrome in the subject based on the calculated IVR. With this configuration, the determination device 100 can calculate the IVR and determine the possibility of Cushing's syndrome in the subject based on the calculated IVR. Therefore, the subject can know the possibility of Cushing's syndrome in a short time without undergoing a stress test under bed rest in a hospital.

[0022] Here, the effect of determining the possibility of Cushing's syndrome in a subject based on IVR will be described. 4A is a diagram showing example 1 of receiver operating characteristics. In FIG. 4A, (A) is an example of a receiver operating characteristic (ROC) of an IVR, and (B) is an example of a receiver operating characteristic of a PMI. PMI is the iliopsoas index, calculated by dividing the iliopsoas muscle by the square of the height, i.e., PMI is calculated by Equation (1). Psoas index = iliopsoas muscle / height^2 (1) The psoas index has been used as an indicator of sarcopenia.

[0023] In Figure 4A (A) and (B), the horizontal axis is specificity, and the vertical axis is sensitivity. Specificity is the rate at which negative cases are correctly identified as negative, and sensitivity is the rate at which positive cases are correctly identified as positive. If the test is effective, the curve will deviate from the 45-degree line to the upper left. The further away it is, the more effective the test is. In Figure 4A (A) and (B), the area under the ROC curve (AUC) was calculated between 0.0 and 1.0 on the vertical axis. The AUC for (A) was 0.930, and the AUC for (B) was 0.848.

[0024] 4B is a diagram showing example 2 of the receiver operation characteristic. In FIG. 4B, (C) is an example of the receiver operation characteristic of the V-fat / S-fat ratio, and (D) is an example of the receiver operation characteristic of the Ip-M / Ps-M ratio. The V-fat / S-fat ratio is the ratio of visceral fat area to subcutaneous fat area. The V-fat / S-fat ratio has been reported as a diagnostic aid for Cushing's syndrome. The Ip-M / Ps-M ratio is the ratio of the iliopsoas muscle area to the paraspinal muscle area. The paraspinal muscles are composed of the erector spinae and multifidus muscles, which contribute to the protection and stability of the lumbar spine.

[0025] In Figure 4B (C) and (D), the horizontal axis is specificity, and the vertical axis is sensitivity. If the test is effective, the curve will move away from the 45-degree line to the upper left. The further away it is, the more effective the test is. The area under the ROC curve (AUC) was calculated for (C) and (D) in Figure 4B, where the vertical axis is between 0.0 and 1.0. The AUC for (C) is 0.760, and the AUC for (D) is 0.749. Figures 4A (A) and (B) and Figure 4B (C) and (D) show that the ROC AUC for IVR is the largest compared to the ROC AUCs for PMI, V-fat / S-fat ratio, and Ip-M / Ps-M ratio. This indicates that IVR is effective in determining the possibility of Cushing's syndrome.

[0026] In the determination device 100, the lumbar CT image is a CT image in which the third lumbar vertebra is used as a slice plane. With this configuration, the determination device 100 can calculate the iliopsoas muscle area and visceral fat area from lumbar CT images sliced ​​at the third lumbar vertebra, and calculate IVR, which is the ratio of the calculated iliopsoas muscle area to the visceral fat area. Therefore, compared to using lumbar CT images other than those sliced ​​at the third lumbar vertebra, the accuracy of determining the possibility of Cushing's syndrome in a subject based on the calculated IVR can be improved.

[0027] In addition, in the determination device 100, the iliopsoas muscle includes the psoas major muscle, and the area calculation unit 106 calculates the area of ​​the psoas major muscle as the iliopsoas muscle area. With this configuration, the determination device 100 can calculate the psoas major muscle area and visceral fat area from lumbar CT images sliced ​​through the third lumbar vertebra, and calculate IVR, which is the ratio of the calculated psoas major muscle area to the visceral fat area. This improves the accuracy of determining the possibility of Cushing's syndrome in a subject based on the calculated IVR, compared to when a parameter other than the psoas major muscle area is used.

[0028] (Modification 1 of the embodiment) FIG. 5 is a diagram illustrating an example of a determination device according to the first modification of the embodiment. A determination device 100a according to the modification of the embodiment determines the possibility of Cushing's syndrome in a subject based on PMI in addition to IVR in the determination device 100 according to the embodiment. The determination device 100a is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, an industrial computer, etc. The determination device 100a includes, for example, an input unit 102, an image acquisition unit 104, a height information acquisition unit 105a, an area calculation unit 106, a PMI calculation unit 107a, an IVR calculation unit 108, a determination unit 110a, an output unit 112, and a storage unit 114.

[0029] The input unit 102 includes an input device. In addition to image data, information specifying the height of the subject is input to the input unit 102. The height information acquiring unit 105a acquires information that specifies the height of the subject input to the input unit . The PMI calculation unit 107a acquires information specifying the iliopsoas muscle area calculated by the area calculation unit 106, and acquires information specifying height from the height information acquisition unit 105a. The PMI calculation unit 107a calculates the PMI based on the acquired information specifying the iliopsoas muscle area and information specifying height. Specifically, the PMI calculation unit 107a calculates the PMI by calculating the ratio of the iliopsoas muscle area to the square of the height (iliopsoas muscle area / (height)^2).

[0030] The determination unit 110a acquires the PMI calculated by the PMI calculation unit 107a and the IVR calculated by the IVR calculation unit 108. The determination unit 110a determines the possibility of Cushing's syndrome in the subject based on the acquired PMI and IVR. Specifically, the determination unit 110a determines whether the PMI is less than the PMI threshold and whether the IVR is less than the IVR threshold. The determination unit 110a determines that there is a high possibility of Cushing's syndrome when the PMI is less than the PMI threshold and the IVR is less than the IVR threshold. The determination unit 110a determines that there is a medium possibility of Cushing's syndrome when the PMI is less than the PMI threshold and the IVR is equal to or greater than the IVR threshold, or when the PMI is equal to or greater than the PMI threshold and the IVR is less than the IVR threshold. The determination unit 110a determines that there is a low possibility of Cushing's syndrome when the PMI is equal to or greater than the PMI threshold and the IVR is equal to or greater than the IVR threshold. The PMI determination threshold is set in advance. An example of the PMI determination threshold is equal to or greater than 0.05 and less than 2. The IVR determination threshold is set in advance. An example of the IVR determination threshold is equal to or greater than 3.5 and less than 5.

[0031] All or part of the height information acquisition unit 105a, the PMI calculation unit 107a, and the judgment unit 110a are functional units (hereinafter referred to as software functional units) that are realized, for example, by a processor such as a CPU executing a program stored in the memory unit 114. In addition, all or part of the height information acquisition unit 105a, the PMI calculation unit 107a, and the determination unit 110a may be realized by hardware such as an LSI, an ASIC, or an FPGA, or may be realized by a combination of a software function unit and hardware.

[0032] (Operation of the determination device) FIG. 6 is a diagram illustrating an example of the operation of the determination device according to the first modification of the embodiment. (Step S1-2) In the determination device 100a, image data of a subject and information specifying the height of the subject are input to the input unit 102. The image acquisition unit 104 acquires the image data input to the input unit 102. (Step S2-2) In the determination device 100a, the height information acquisition unit 105a acquires information that specifies the height of the subject input to the input unit . (Step S3-2) In the determination device 100a, the area calculation unit 106 acquires image data of the subject acquired by the area calculation unit 106. The area calculation unit 106 calculates the iliopsoas muscle area and the visceral fat area based on the acquired image data.

[0033] (Step S4-2) In the determination device 100a, the IVR calculation unit 108 acquires the iliopsoas muscle area and the visceral fat area calculated by the area calculation unit 106, and calculates the IVR based on the acquired iliopsoas muscle area and visceral fat area. (Step S5-2) In the determination device 100a, the PMI calculation unit 107a acquires information specifying the iliopsoas muscle area calculated by the area calculation unit 106, and acquires information specifying the height of the subject from the height information acquisition unit 105a. The PMI calculation unit 107a calculates the PMI based on the acquired information specifying the iliopsoas muscle area and information specifying the height of the subject. (Step S6-2) In the determination device 100a, the determination unit 110a acquires the PMI calculated by the PMI calculation unit 107a and the IVR calculated by the IVR calculation unit 108. The determination unit 110a determines the possibility of Cushing's syndrome in the subject based on the acquired PMI and IVR. (Step S7-2) In the determination device 100a, the output unit 112 acquires the determination result of the possibility of Cushing's syndrome from the determination unit 110a. The output unit 112 may display the acquired determination result of the possibility of Cushing's syndrome on a display unit (not shown) or may output it as audio. 6, the processing order from steps S1-2 to S3-2 may be S1-2, S3-2, S2-2, or S2-2, S1-2, S3-2. Also, the processing order from steps S4-2 to S5-2 may be S5-2, S4-2.

[0034] According to a first modification of the embodiment, the above-described determination device 100 further includes a height information acquisition unit 105a that acquires information specifying the height of the subject, and a PMI calculation unit 107a that calculates the PMI, which is the ratio of the iliopsoas muscle area to the height, from the calculated iliopsoas muscle area and the acquired height. The determination unit 110a determines the possibility of Cushing's syndrome in the subject based on the calculated IVR and PMI. With this configuration, the determination device 100a calculates the PMI in addition to the IVR, and can determine the possibility of Cushing's syndrome in a subject based on the calculated IVR and the PMI. This allows the subject to know the possibility of Cushing's syndrome in a short time without undergoing a stress test while in hospital and under bed rest. Furthermore, the accuracy of determining the possibility of Cushing's syndrome in a subject can be improved compared to when the possibility of Cushing's syndrome in a subject is determined based on the IVR.

[0035] (Modification 2 of the embodiment) FIG. 7 is a diagram illustrating an example of a determination device according to the second modification of the embodiment. The determination device 100b according to the second modification of the embodiment acquires information identifying the gender of the subject and acquires an IVR threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the gender of the subject in the determination device 100 according to the embodiment. The determination device 100b determines the possibility of Cushing's syndrome in the subject based on the acquired IVR threshold and the calculated IVR. The determination device 100b is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, an industrial computer, etc. The determination device 100b includes, for example, an input unit 102, an image acquisition unit 104, an area calculation unit 106, an IVR calculation unit 108, a gender information acquisition unit 109b, a determination unit 110b, an output unit 112, and a storage unit 114.

[0036] The input unit 102 includes an input device. In addition to image data, information specifying the gender of the subject is input to the input unit 102. The gender information acquisition unit 109b acquires information that specifies the gender of the subject input to the input unit . The determination unit 110b acquires the IVR calculated by the IVR calculation unit 108 and acquires the information identifying the gender of the subject acquired by the gender information acquisition unit 109b. The determination unit 110b determines the possibility of the subject having Cushing's syndrome based on the acquired IVR and the information identifying the gender of the subject. Specifically, the determination unit 110b stores information specifying the gender of the subject in association with an IVR threshold. For example, the IVR threshold when the gender of the subject is female is higher than the IVR threshold when the gender of the subject is male. The determination unit 110b acquires the IVR threshold associated with the acquired information specifying the gender of the subject.

[0037] The determination unit 110b determines whether the IVR is less than the IVR threshold. The determination unit 110b determines that the possibility of Cushing's syndrome is high when the IVR is less than the IVR determination threshold, and determines that the possibility of Cushing's syndrome is low when the IVR is equal to or greater than the IVR determination threshold. The gender information acquisition unit 109b and the determination unit 110b are all or partly functional units (hereinafter referred to as software functional units) that are realized by a processor such as a CPU executing a program stored in the storage unit 114. All or part of the gender information acquisition unit 109b and the determination unit 110b may be realized by hardware such as an LSI, an ASIC, or an FPGA, or may be realized by a combination of a software function unit and hardware.

[0038] (Operation of the determination device) FIG. 8 is a diagram illustrating an example of the operation of the determination device according to the second modification of the embodiment. (Step S1-3) In the determination device 100b, image data of a subject and information specifying the gender of the subject are input to the input unit 102. The image acquisition unit 104 acquires the image data input to the input unit 102. (Step S2-3) In the determination device 100b, the gender information acquisition unit 109b acquires information input to the input unit 102 that identifies the gender of the subject. (Step S3-3) In the determination device 100b, the area calculation unit 106 acquires image data of the subject acquired by the area calculation unit 106. The area calculation unit 106 calculates the iliopsoas muscle area and the visceral fat area based on the acquired image data of the subject.

[0039] (Step S4-3) In the determination device 100b, the IVR calculation unit 108 acquires the iliopsoas muscle area and the visceral fat area calculated by the area calculation unit 106, and calculates the IVR based on the acquired iliopsoas muscle area and visceral fat area. (Step S5-3) In the determination device 100b, the determination unit 110b acquires the IVR calculated by the IVR calculation unit 108, and acquires information identifying the gender of the subject acquired by the gender information acquisition unit 109b. The determination unit 110b acquires an IVR threshold associated with the acquired information identifying the gender of the subject. (Step S6-3) In the determination device 100b, the determination unit 110b determines the possibility of Cushing's syndrome in the subject based on the acquired IVR and the IVR threshold. (Step S7-3) In the determination device 100b, the output unit 112 acquires the determination result of the possibility of Cushing's syndrome from the determination unit 110b. The output unit 112 may display the acquired determination result of the possibility of Cushing's syndrome on a display unit (not shown) or output it as audio. 8, the processing order from steps S1-3 to S3-3 may be S1-3, S3-3, S2-3, or S2-3, S1-3, S3-3. Also, the processing order from steps S4-3 to S5-3 may be S5-3, S4-3.

[0040] In the above-mentioned variant example 2 of the embodiment, the determination device 100 according to the embodiment acquires information identifying the gender of the subject, acquires an IVR threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the gender of the subject, and determines the possibility of Cushing's syndrome in the subject based on the acquired IVR threshold and the calculated IVR, but this example is not limited to this. For example, the determination device 100a according to the first modification of the embodiment may acquire information identifying the sex of the subject, acquire an IVR threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the sex of the subject, and determine the possibility of Cushing's syndrome in the subject based on the acquired IVR threshold and the calculated IVR. The determination device 100a according to the first modification of the embodiment may acquire information identifying the sex of the subject, acquire a PMI threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the sex of the subject, and determine the possibility of Cushing's syndrome in the subject based on the acquired PMI threshold and the calculated PMI.

[0041] According to the second modification of the embodiment, the aforementioned determination device 100 further includes a gender information acquisition unit 109b that acquires information identifying the gender of the subject. The determination unit 110b acquires a threshold value for determining the possibility of Cushing's syndrome based on the acquired information identifying the gender of the subject, and determines the possibility of Cushing's syndrome in the subject based on the acquired threshold value and the calculated IVR. With this configuration, the determination device 100b can acquire information identifying the gender of the subject and acquire a threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the gender of the subject.The determination device 100b can determine the possibility of Cushing's syndrome in the subject based on the calculated IVR based on the acquired threshold.This allows the subject to know the possibility of Cushing's syndrome in a short period of time without undergoing a stress test while in hospital and under bed rest.Furthermore, the accuracy of determining the possibility of Cushing's syndrome in the subject can be improved compared to when the possibility of Cushing's syndrome in the subject is determined based on a threshold not based on information identifying the gender of the subject.

[0042] (Modification 3 of the embodiment) FIG. 9 is a diagram illustrating an example of a determination device according to a third modification of the embodiment. The determination device 100c according to the third modification of the embodiment acquires information specifying the age of the subject and acquires an IVR threshold for determining the possibility of Cushing's syndrome based on the acquired information specifying the age of the subject in the determination device 100 according to the embodiment. The determination device 100c determines the possibility of Cushing's syndrome in the subject based on the acquired IVR threshold and the calculated IVR. The determination device 100c is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, an industrial computer, etc. The determination device 100c includes, for example, an input unit 102, an image acquisition unit 104, an area calculation unit 106, an IVR calculation unit 108, an age information acquisition unit 109c, a determination unit 110c, an output unit 112, and a storage unit 114.

[0043] The input unit 102 includes an input device. In addition to image data of the subject, information specifying the age of the subject is input to the input unit 102. The age information acquiring unit 109c acquires information that specifies the age of the subject input to the input unit . The determination unit 110c acquires the IVR calculated by the IVR calculation unit 108 and acquires information identifying the subject's age acquired by the age information acquisition unit 109c. The determination unit 110c determines the possibility of the subject having Cushing's syndrome based on the acquired IVR and the information identifying the subject's age. Specifically, the determination unit 110c stores the information identifying the subject's age in association with an IVR threshold. For example, as the subject's age increases from younger to older, the IVR threshold decreases from higher to lower values. The determination unit 110c acquires the IVR threshold associated with the acquired information identifying the subject's age.

[0044] The determination unit 110c determines whether the IVR is less than the IVR threshold. The determination unit 110c determines that the possibility of Cushing's syndrome is high when the IVR is less than the IVR determination threshold, and determines that the possibility of Cushing's syndrome is low when the IVR is equal to or greater than the IVR determination threshold. The age information acquisition unit 109c and the determination unit 110c are all or partly functional units (hereinafter referred to as software functional units) that are realized by a processor such as a CPU executing a program stored in the storage unit 114. All or part of the age information acquisition unit 109c and the determination unit 110c may be realized by hardware such as an LSI, an ASIC, or an FPGA, or may be realized by a combination of a software function unit and hardware.

[0045] (Operation of the determination device) FIG. 10 is a diagram illustrating an example of the operation of the determination device according to the second modification of the embodiment. (Step S1-4) In the determination device 100c, image data of the subject and information specifying the age of the subject are input to the input unit 102. The image acquisition unit 104 acquires the image data of the subject input to the input unit 102. (Step S2-4) In the determination device 100c, the age information acquiring unit 109c acquires information that specifies the age of the subject input to the input unit . (Step S3-4) In the determination device 100c, the area calculation unit 106 acquires image data acquired by the area calculation unit 106. The area calculation unit 106 calculates the iliopsoas muscle area and the visceral fat area based on the acquired image data.

[0046] (Step S4-4) In the determination device 100c, the IVR calculation unit 108 acquires the iliopsoas muscle area and the visceral fat area calculated by the area calculation unit 106, and calculates the IVR based on the acquired iliopsoas muscle area and visceral fat area. (Step S5-4) In the determination device 100c, the determination unit 110c acquires the IVR calculated by the IVR calculation unit 108, and acquires information identifying the age of the subject acquired by the age information acquisition unit 109c. The determination unit 110c acquires an IVR threshold associated with the acquired information identifying the age of the subject. (Step S6-4) In the determination device 100c, the determination unit 110c determines the possibility of Cushing's syndrome in the subject based on the acquired IVR and the IVR threshold. (Step S7-4) In the determination device 100c, the output unit 112 acquires the determination result of the possibility of Cushing's syndrome from the determination unit 110c. The output unit 112 may display the acquired determination result of the possibility of Cushing's syndrome on a display unit (not shown) or may output it as audio. 10, the processing order from steps S1-4 to S3-4 may be S1-4, S3-4, S2-4, or S2-4, S1-4, S3-4. Also, the processing order from steps S4-4 to S5-4 may be S5-4, S4-4.

[0047] In the determination device 100c according to variant example 3 of the above-described embodiment, the determination device 100 according to the embodiment acquires information identifying the age of the subject, acquires an IVR threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the age of the subject, and determines the possibility of Cushing's syndrome in the subject based on the acquired IVR threshold and the calculated IVR, but this example is not limited to this. For example, in the determination device 100a according to the first modification of the embodiment, information specifying the age of the subject may be acquired, an IVR threshold for determining the possibility of Cushing's syndrome may be acquired based on the acquired information specifying the age of the subject, and the possibility of Cushing's syndrome in the subject may be determined based on the acquired IVR threshold and the calculated IVR. In the determination device 100a according to the first modification of the embodiment, information specifying the age of the subject may be acquired, a PMI threshold for determining the possibility of Cushing's syndrome may be acquired based on the acquired information specifying the age of the subject, and the possibility of Cushing's syndrome in the subject may be determined based on the acquired PMI threshold and the calculated PMI. Furthermore, in the determination device 100b according to the second modification of the embodiment, information specifying the age of the subject may be acquired, an IVR threshold for determining the possibility of Cushing's syndrome may be acquired based on the acquired information specifying the age of the subject, and the possibility of Cushing's syndrome in the subject may be determined based on the acquired IVR threshold and the calculated IVR.In the determination device 100b according to the second modification of the embodiment, information specifying the sex and age of the subject may be acquired, an IVR threshold for determining the possibility of Cushing's syndrome may be acquired based on the acquired information specifying the sex and age of the subject, and the possibility of Cushing's syndrome in the subject may be determined based on the acquired IVR threshold and the calculated IVR.

[0048] According to the third modification of the embodiment, the above-described determination device 100 further includes an age information acquisition unit 109c that acquires information specifying the age of the subject. The determination unit 110c acquires a threshold value for determining the possibility of Cushing's syndrome based on the acquired information specifying the age of the subject, and determines the possibility of Cushing's syndrome in the subject based on the calculated IVR based on the acquired threshold value. With this configuration, the determination device 100c can acquire information identifying the subject's age and acquire a threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the subject's age.The determination device 100c can determine the possibility of Cushing's syndrome in a subject based on the calculated IVR based on the acquired threshold.This allows the subject to know the possibility of Cushing's syndrome in a short period of time without undergoing a stress test while in hospital and under bed rest.Furthermore, the accuracy of determining the possibility of Cushing's syndrome in a subject can be improved compared to when the possibility of Cushing's syndrome in a subject is determined based on a threshold not based on information identifying the subject's age.

[0049] (Fourth Modification of the Embodiment) FIG. 11 is a diagram illustrating an example of a determination device according to the fourth modification of the embodiment. A determination device 100d according to a fourth modification of the embodiment determines the possibility of Cushing's syndrome in a subject based on an ENR (Eosinophil / Neutrophil ratio) in addition to IVR in the determination device 100 according to the embodiment. The determination device 100d is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, an industrial computer, etc. The determination device 100d includes, for example, an input unit 102, a white blood cell information acquisition unit 103d, an image acquisition unit 104, an area calculation unit 106, an ENR calculation unit 107d, an IVR calculation unit 108, a determination unit 110d, an output unit 112, and a storage unit 114.

[0050] The input unit 102 includes an input device. In addition to image data, white blood cell information is input to the input unit 102. Here, the white blood cell information includes information specifying the eosinophil count and information specifying the neutrophil count. The white blood cell information acquisition unit 103d acquires the white blood cell information input to the input unit . The ENR calculation unit 107d acquires the white blood cell information acquired by the white blood cell information acquisition unit 103d, and calculates the ENR based on the information specifying the eosinophil count and the information specifying the neutrophil count included in the acquired white blood cell information. Specifically, the ENR calculation unit 107d calculates the ENR by dividing the eosinophil count by the neutrophil count.

[0051] The determination unit 110d acquires the ENR calculated by the ENR calculation unit 107d and the IVR calculated by the IVR calculation unit 108. The determination unit 110d determines the possibility of Cushing's syndrome in the subject based on the acquired ENR and IVR. Specifically, the determination unit 110d determines whether the ENR is less than the ENR threshold and whether the IVR is less than the IVR threshold. The determination unit 110d determines that there is a high possibility of Cushing's syndrome when the ENR is less than the ENR threshold and the IVR is less than the IVR threshold. The determination unit 110d determines that there is a medium possibility of Cushing's syndrome when the ENR is less than the ENR threshold and the IVR is equal to or greater than the IVR threshold, or when the ENR is equal to or greater than the ENR threshold and the IVR is less than the IVR threshold. The determination unit 110d determines that there is a low possibility of Cushing's syndrome when the ENR is equal to or greater than the ENR threshold and the IVR is equal to or greater than the IVR threshold. The ENR determination threshold is set in advance. An example of the ENR determination threshold is equal to or greater than 0.005 and less than 0.02. The IVR determination threshold is set in advance. An example of the IVR determination threshold is equal to or greater than 3.5 and less than 5. All or part of the white blood cell information acquisition unit 103d, the ENR calculation unit 107d, and the judgment unit 110d are functional units (hereinafter referred to as software functional units) that are realized, for example, by a processor such as a CPU executing a program stored in the memory unit 114. In addition, all or part of the white blood cell information acquisition unit 103d, the ENR calculation unit 107d, and the judgment unit 110d may be realized by hardware such as an LSI, an ASIC, or an FPGA, or may be realized by a combination of a software function unit and hardware.

[0052] (Operation of the determination device) FIG. 12 is a diagram illustrating an example of the operation of the determination device according to the fourth modification of the embodiment. (Step S1-5) In the determination device 100d, image data of the subject and white blood cell information of the subject are input to the input unit 102. The image acquisition unit 104 acquires the image data of the subject input to the input unit 102. (Step S2-5) In the determination device 100d, the white blood cell information acquisition unit 103d acquires the white blood cell information of the subject input to the input unit . (Step S3-5) In the determination device 100d, the area calculation unit 106 acquires image data acquired by the area calculation unit 106. The area calculation unit 106 calculates the iliopsoas muscle area and the visceral fat area based on the acquired image data.

[0053] (Step S4-5) In the determination device 100d, the IVR calculation unit 108 acquires information specifying the iliopsoas muscle area and information specifying the visceral fat area calculated by the area calculation unit 106, and calculates the IVR based on the acquired information specifying the iliopsoas muscle area and information specifying the visceral fat area. (Step S5-5) In the determination device 100d, the ENR calculation unit 107d acquires the white blood cell information acquired by the white blood cell information acquisition unit 103d, and calculates the ENR based on the information specifying the number of eosinophils and the information specifying the number of neutrophils contained in the acquired white blood cell information. (Step S6-5) In the determination device 100d, the determination unit 110d acquires the ENR calculated by the ENR calculation unit 107d and the IVR calculated by the IVR calculation unit 108. The determination unit 110d determines the possibility of Cushing's syndrome in the subject based on the acquired ENR and IVR. (Step S7-5) In the determination device 100d, the output unit 112 acquires the determination result of the possibility of Cushing's syndrome from the determination unit 110d. The output unit 112 may display the acquired determination result of the possibility of Cushing's syndrome on a display unit (not shown) or may output it as audio. 12, the processing order from steps S1-5 to S3-5 may be S1-5, S3-5, S2-5, or S2-5, S1-5, S3-5. Also, the processing order from steps S4-5 to S5-5 may be S5-5, S4-5.

[0054] In the determination device 100d according to the fourth modification of the embodiment described above, the determination device 100 according to the embodiment determines whether a subject has Cushing's syndrome based on ENR in addition to IVR, but this example is not limiting. For example, in the determination device 100a according to the first modification of the embodiment, the possibility of Cushing's syndrome in a subject may be determined based on the ENR in addition to the IVR and PMI. Also, the determination device 100a according to the first modification of the embodiment may acquire information identifying the gender of the subject, acquire an ENR threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the gender of the subject, and determine the possibility of Cushing's syndrome in a subject based on the acquired ENR threshold and the calculated ENR. For example, in the determination device 100b according to the second modification of the embodiment, the possibility of Cushing's syndrome in a subject may be determined based on the ENR in addition to the IVR. The determination device 100b according to the second modification of the embodiment may acquire information identifying the gender of the subject, acquire an EVR threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the gender of the subject, and determine the possibility of Cushing's syndrome in the subject based on the acquired ENR threshold and the calculated ENR. For example, in the determination device 100c according to the third modification of the embodiment, information specifying the age of the subject may be acquired, an EVR threshold for determining the possibility of Cushing's syndrome may be acquired based on the acquired information specifying the age of the subject, and the possibility of Cushing's syndrome in the subject may be determined based on the acquired ENR threshold and the calculated ENR. In the determination device 100b according to the third modification of the embodiment, information specifying the sex and age of the subject may be acquired, an ENR threshold for determining the possibility of Cushing's syndrome may be acquired based on the acquired information specifying the sex and age of the subject, and the possibility of Cushing's syndrome in the subject may be determined based on the acquired ENR threshold and the calculated ENR.

[0055] According to the fourth modification of the embodiment, the determination device 100 further includes a white blood cell information acquisition unit 103d that acquires information specifying the eosinophil count and the neutrophil count of the subject, and an ENR calculation unit 107d that calculates the ENR from the information specifying the eosinophil count and the neutrophil count. The determination unit 110d determines the possibility of Cushing's syndrome in the subject based on the calculated ENR and the IVR. With this configuration, the determination device 100d calculates the ENR in addition to the IVR and can determine the possibility of Cushing's syndrome in a subject based on the calculated IVR and ENR. This allows the subject to know the possibility of Cushing's syndrome in a short time without undergoing a stress test while in hospital and under bed rest. Furthermore, the accuracy of determining the possibility of Cushing's syndrome in a subject can be improved compared to when the possibility of Cushing's syndrome in a subject is determined based on the IVR.

[0056] Here, the effect of determining the possibility of Cushing's syndrome in a subject based on ENR will be described. FIG. 13 is a diagram illustrating Example 3 of receiver operating characteristics. ENR is the ratio of eosinophils to neutrophils, calculated by dividing the eosinophil count by the neutrophil count. It is known that Cushing's syndrome is characterized by a decrease in eosinophils and an increase in neutrophils. In Figure 13, the horizontal axis is specificity, and the vertical axis is sensitivity. Specificity is the rate at which negative cases are correctly identified as negative, and sensitivity is the rate at which positive cases are correctly identified as positive. If the test is effective, this curve will deviate from the 45-degree line to the upper left. The further away it is, the more effective the test is. The area under the ROC curve (AUC) between 0.0 and 1.0 is 0.879. Figure 13 shows that the ROC AUC for ENR is the largest compared to the ROC AUC for PMI, V-fat / S-fat ratio, and Ip-M / Ps-M ratio. This indicates that ENR is effective in determining the possibility of Cushing's syndrome.

[0057] Although the embodiments of the present invention have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be embodied in various other forms, and various omissions, substitutions, modifications, and combinations can be made without departing from the spirit of the invention. These embodiments and their modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as set forth in the claims. For example, the embodiment, the first modification of the embodiment, the second modification of the embodiment, the third modification of the embodiment, and the fourth modification of the embodiment may be combined. The aforementioned determination devices 100, 100a, 100b, 100c, and 100d each have a computer built in. The processing steps of each of the aforementioned devices are stored in a computer-readable recording medium in the form of a program, and the computer reads and executes the program to perform the above processing. Here, computer-readable recording media refers to magnetic disks, magneto-optical disks, CD-ROMs, DVD-ROMs, semiconductor memories, etc. Alternatively, the computer program may be distributed to a computer via a communication line, and the computer that receives the program may execute the program. The program may also be for realizing part of the above-mentioned functions. Furthermore, the above-mentioned functions may be realized in combination with a program already recorded in the computer system, that is, a so-called differential file (differential program). [Explanation of symbols]

[0058] 100, 100a, 100b, 100c, 100d...determination device, 102...input unit, 103d...white blood cell information acquisition unit, 104...image acquisition unit, 105a...height information acquisition unit, 106...area calculation unit, 107a...PMI calculation unit, 107d...ENR calculation unit, 108...IVR calculation unit, 109b...gender information acquisition unit, 109c...age information acquisition unit, 110, 110a, 110b, 110c, 110d...determination unit, 112...output unit

Claims

1. an image acquisition unit that acquires a lumbar CT image of the subject; an area calculation unit that calculates an iliopsoas muscle area and a visceral fat area from the lumbar CT image; an IVR calculation unit that calculates an IVR, which is a ratio between the calculated iliopsoas muscle area and the visceral fat area; a determination unit that determines the possibility of Cushing's syndrome in the subject based on the calculated IVR; A determination device comprising:

2. The determination device according to claim 1 , wherein the lumbar CT image is a CT image sliced ​​along a third lumbar vertebra.

3. The iliopsoas muscles include the psoas major, The determination device according to claim 1 or 2, wherein the area calculation unit calculates an area of ​​the psoas major muscle as the iliopsoas muscle area.

4. a height information acquiring unit for acquiring information specifying the height of the subject; a PMI calculation unit that calculates a PMI, which is a ratio of the iliopsoas muscle area to the height, based on the calculated iliopsoas muscle area and the acquired information specifying the height; Furthermore, The determination device according to claim 1 , wherein the determination unit determines a possibility of Cushing's syndrome in the subject based on the calculated IVR and the PMI.

5. a gender information acquisition unit that acquires information that identifies the gender of the subject; Furthermore, The determination device according to any one of claims 1 to 4, wherein the determination unit obtains a threshold for determining the possibility of Cushing's syndrome based on the information identifying the gender of the subject, and determines the possibility of Cushing's syndrome of the subject based on the obtained threshold and the calculated IVR.

6. an age information acquisition unit that acquires information specifying the age of the subject; Furthermore, The determination device according to any one of claims 1 to 4, wherein the determination unit obtains a threshold for determining the possibility of Cushing's syndrome based on the acquired information identifying the age of the subject, and determines the possibility of Cushing's syndrome of the subject based on the acquired threshold and the calculated IVR.

7. a white blood cell information acquiring unit that acquires information specifying the eosinophil count and the neutrophil count of the subject; an ENR calculation unit that calculates an ENR from information specifying the number of eosinophils and the number of neutrophils; Furthermore, The determination device according to claim 1 , wherein the determination unit determines the possibility of Cushing's syndrome in the subject based on the calculated ENR and the IVR.

8. A computer-implemented determination method comprising: acquiring a lumbar CT image of the subject; Calculating an iliopsoas muscle area and a visceral fat area from the lumbar CT image; Calculating an IVR, which is a ratio between the calculated iliopsoas muscle area and the visceral fat area; determining the possibility of Cushing's syndrome in the subject based on the calculated IVR; A determination method having the following.

9. On the computer, acquiring a lumbar CT image of the subject; Calculating an iliopsoas muscle area and a visceral fat area from the lumbar CT image; Calculating an IVR, which is a ratio between the calculated iliopsoas muscle area and the visceral fat area; determining the possibility of Cushing's syndrome in the subject based on the calculated IVR; A program that executes.

Images