Disease onset risk prediction device, prediction marker, prediction method, program, and recording medium

Abstract

The purpose of the present disclosure is to provide a disease onset risk prediction device for predicting the onset risk of lung cancer regardless of the smoking status of a prediction subject.　A disease onset risk prediction device according to the present disclosure includes an information acquisition unit, a prediction unit, and an output unit. The information acquisition unit acquires disease-related information of a prediction subject. The disease is lung cancer. The disease-related information includes information about the age, a carcinoembryonic antigen (CEA), and the 1-second rate (FEV10). The prediction unit predicts the onset risk of the disease in the prediction subject from the disease-related information. The output unit outputs the onset risk of the disease.

Description

Disease onset risk prediction device, prediction marker, prediction method, program, and recording medium

[0001] The present disclosure relates to a disease onset risk prediction device, a predictive marker, a prediction method, a program, and a recording medium.

[0002] It is known that the incidence and mortality of lung cancer are closely related to smoking patterns (Non-Patent Document 1).

[0003] Barta JA, Powell CA, Wisnivesky JP. Global Epidemiology of Lung Cancer. Ann Glob Health. 2019 Jan 22;85(1):8. doi: 10.5334 / aogh.2419. PMID: 30741509; PMCID: PMC6724220.

[0004] However, lung cancer can occur even in non-smokers. Furthermore, even non-smokers may have smoked in the past, making it difficult to accurately assess smoking status. Therefore, it is desirable to establish a method for predicting the risk of developing lung cancer regardless of smoking status.

[0005] Therefore, the present disclosure aims to provide a disease onset risk prediction device, a predictive marker, a prediction method, a program, and a recording medium for predicting the risk of developing lung cancer regardless of the smoking status of the subject.

[0006] In order to achieve the above-mentioned object, the disease onset risk prediction device of the present disclosure includes an information acquisition unit, a prediction unit, and an output unit, wherein the information acquisition unit acquires disease-related information of the subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction unit predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output unit outputs the risk of developing the disease.

[0007] The disease onset risk prediction marker disclosed herein is an indicator for predicting the risk of developing a disease, the disease being lung cancer, and is used in combination with information including age and forced expiratory volume in one second (FEV10), and includes carcinoembryonic antigen (CEA).

[0008] The disease onset risk prediction method disclosed herein includes an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of a subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction step predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output step outputs the risk of developing the disease, and each of the steps is performed by a computer.

[0009] The program of the present disclosure is a program for causing a computer to execute the steps of the method of the present disclosure as procedures.

[0010] The recording medium of the present disclosure is a computer-readable recording medium on which the program of the present disclosure is recorded.

[0011] According to the present disclosure, the risk of developing lung cancer can be predicted regardless of the smoking status of the subject.

[0012] FIG. 1 is a block diagram showing an example of the configuration of a disease onset risk prediction device of the present disclosure. FIG. 2 is a block diagram showing an example of the hardware configuration of the disease onset risk prediction device of the present disclosure. FIG. 3 is a flowchart showing an example of processing in the disease onset risk prediction device of the present disclosure. FIG. 4 is a diagram showing a breakdown of training data and evaluation data. FIG. 5 is a diagram showing an example of selection of data to be used for training data. FIG. 6 is a scatter plot showing the relationship between the hazard ratio and p-value of disease-related information related to malignant neoplasms <tumors> of the bronchus and lungs (C34). FIG. 7 is a diagram comparing details of trained models for predicting the onset of malignant neoplasms <tumors> of the bronchus and lungs (C34). FIG. 8 is a diagram comparing the prediction accuracy of trained models for predicting the onset of malignant neoplasms <tumors> of the bronchus and lungs (C34). FIG. 9 is a diagram showing prediction results of disease onset risk predicted by trained models for predicting the onset of malignant neoplasms <tumors> of the bronchus and lungs (C34). Figure 10 compares the prediction accuracy by age of trained models for predicting the onset of malignant neoplasms (tumors) of the bronchi and lungs (C34).

[0013] Embodiments of the present disclosure will be described with reference to the drawings. The present disclosure is not limited to the following embodiments. In the following drawings, the same parts are denoted by the same reference numerals. Furthermore, the descriptions of the embodiments can be mutually incorporated unless otherwise specified, and the configurations of the embodiments can be combined unless otherwise specified.

[0014] In the present disclosure, the term "disease" refers to lung cancer unless otherwise specified. More specifically, examples of lung cancer include malignant neoplasms of the bronchus and lung (tumor) (C34) in the International Classification of Diseases (ICD (International Statistical Classification of Diseases and Related Health Problems)-10). Examples of lung cancer include primary lung cancer, right upper lobe lung cancer, right upper lobe lung adenocarcinoma, right lower lobe lung cancer, right lung cancer, left lung cancer, lung cancer, lung cancer (post-operative), lung adenocarcinoma, hilar small cell carcinoma, non-small cell lung cancer, right lower lobe small cell lung cancer, and right upper lobe lung cancer.

[0015] [Embodiment 1] Fig. 1 is a block diagram showing an example of the configuration of a disease onset risk prediction device 10 (hereinafter also referred to as "the device 10") according to the present disclosure. As shown in Fig. 1, the device 10 includes an information acquisition unit 11, a prediction unit 12, and an output unit 13.

[0016] The device 10 may be, for example, a single device including the above-mentioned components, or a device in which the components can be connected via a communication network. The device 10 may also be connected to an external device (described later) via the communication network. The communication network is not particularly limited and may be any known network, for example, wired or wireless. Examples of the communication network include the Internet, the World Wide Web (WWW), a telephone line, a Local Area Network (LAN), a Storage Area Network (SAN), a Delay Tolerant Networking (DTN), a Low Power Wide Area Network (LPWA), and a Local 5G (L5G). Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), local 5G, and LPWA. The wireless communication may be a form in which each device communicates directly (ad hoc communication), infrastructure communication, indirect communication via an access point, or the like. The device 10 may be incorporated into a system server, for example. The device 10 may also be, for example, a personal computer (PC, e.g., desktop or laptop) on which the program of the present disclosure is installed, a smartphone, a tablet terminal, or the like. Furthermore, the device 10 may be, for example, in the form of cloud computing or edge computing, in which at least one of the components is located on a server and the other components are located on a terminal.

[0017] 2 is a block diagram illustrating an example of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, a memory 102, a bus 103, a storage device 104, an input device 105, an output device 106, and a communication device 107. The components of the device 10 are connected to each other via the bus 103 and their respective interfaces (I / F).

[0018] The central processing unit 101 operates in cooperation with other components via a controller (such as a system controller or an I / O controller) and is responsible for overall control of the device 10. In the device 10, the central processing unit 101 executes, for example, the program disclosed herein and other programs, and also reads and writes various types of information. Specifically, for example, the central processing unit 101 functions as an information acquisition unit 11, a prediction unit 12, and an output unit 13. The device 10 may include, as a computing device, other computing devices such as a CPU, a GPU (Graphics Processing Unit), an APU (Accelerated Processing Unit), or the like, or may include a combination of a CPU and these.

[0019] The bus 103 can also be connected to, for example, external devices. Examples of the external devices include a user terminal, an external storage device (such as an external database), a printer, an external input device, an external display device, and an external imaging device. The device 10 can be connected to an external network (the communication line network) by, for example, a communication device 107 connected to the bus 103, and can also be connected to other devices via the external network.

[0020] The memory 102 may be, for example, a main memory (primary storage device). When the central processing unit 101 performs processing, the memory 102 reads various operating programs, such as the program of the present disclosure, stored in the storage device 104 (described later), and the central processing unit 101 receives data from the memory 102 and executes the programs. The main memory may be, for example, a RAM (random access memory). Alternatively, the memory 102 may be, for example, a ROM (read only memory).

[0021] The storage device 104 is also referred to as an auxiliary storage device, for example, in contrast to the main memory (primary storage device). As described above, the storage device 104 stores an operating program including the program of the present disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive that reads and writes from and to the recording medium. The recording medium is not particularly limited and may be, for example, an internal or external type, such as a hard disk drive (HDD), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, or memory card. The storage device 104 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD) in which the recording medium and drive are integrated.

[0022] In the present device 10, the memory 102 and the storage device 104 can also store various information such as log information, information acquired from an external database (not shown) or an external device, information generated by the present device 10, and information used when the present device 10 executes processing. In this case, the memory 102 and the storage device 104 may store, for example, disease-related information, etc., as described below. Note that at least a portion of the information may be stored, for example, in an external server other than the memory 102 and the storage device 104, or may be stored in a distributed manner across multiple terminals using blockchain technology or the like.

[0023] The device 10 further includes, for example, an input device 105 and an output device 106. Examples of the input device 105 include pointing devices such as a touch panel, track pad, or mouse; a keyboard; imaging means such as a camera or scanner; card readers such as an IC card reader or a magnetic card reader; and audio input means such as a microphone. Examples of the output device 106 include display devices such as an LED display or a liquid crystal display; audio output devices such as a speaker; a printer; etc. In the present disclosure 1, the input device 105 and the output device 106 are configured separately, but the input device 105 and the output device 106 may be configured as an integrated device, such as a touch panel display.

[0024] Next, an example of the disease onset risk prediction method of the present disclosure will be described based on the flowchart of Fig. 3. The disease onset risk prediction method of the present disclosure is carried out as follows, for example, using the present device 10 of Fig. 1 or Fig. 2. Note that the disease onset risk prediction method of the present disclosure is not limited to use with the present device 10 of Fig. 1 or Fig. 2.

[0025] First, the information acquisition unit 11 acquires disease-related information of the subject to be predicted (S11, information acquisition step). The disease-related information includes information on age, carcinoembryonic antigen (CEA), and forced expiratory volume (FEV10). The disease-related information may be, for example, information acquired by the subject to be predicted. Examples of the information acquired by the subject to be predicted include information acquired by a wearable device, a home health measurement device, etc. The disease-related information may also include medical record information. Examples of the disease-related information include test result information, interview information, and subject attribute information (such as gender).

[0026] Here, the forced expiratory volume in one second (FEV10) means, for example, the ratio of the forced expiratory volume in one second to the total exhaled volume. The forced expiratory volume in one second (FEV10) can be measured, for example, by the method described in "Clinical Respiratory Function Test, 7th Edition (edited by the Pulmonary Physiology Specialist Committee of the Japanese Respiratory Society)." The forced expiratory volume in one second (FEV10) can be calculated, for example, by the following formula: Forced expiratory volume in one second (FEV10) = Forced vital capacity in one second (FEV1) / Forced vital capacity (FVC) × 100

[0027] Furthermore, the disease-related information may further include, for example, creatinine-equivalent estimated glomerular filtration rate (eGFR Cre (Estimated Glomerular Filtration Rate (Creatinine))), albumin, MCH (Mean Corpuscular Hemoglobin), BUN (Blood Urea Nitrogen), ECG (Electrocardiogram), AG ratio (Albumin-Globulin ratio), MCV (Mean Corpuscular Volume), LD or LDH (Lactate Dehydrogenase), FEV1 (Forced Expiratory Volume in 1 Second), total cholesterol, uric acid, body fat percentage, direct bilirubin, blood glucose level, LDL (Low-Density Lipoprotein), ALT (Alanine Aminotransferase), white blood cell count, CRP (C-Reactive Protein), ALP (Alkaline Phosphatase), HDL (High-Density Lipoprotein), MCHC (Mean Corpuscular Hemoglobin), and Concentration), vital capacity, non-HDL cholesterol, basophils, urine specific gravity, urine pH, cholinesterase, first systolic blood pressure, platelet count, eosinophils, occult blood (fecal occult blood), abdominal circumference, hemoglobin, monocytes, urinary urobilinogen, total bilirubin, total protein, hematocrit, forced vital capacity, red blood cell count, first diastolic blood pressure, LAP (Leucine Aminopeptidase), blood glucose level after 60 minutes, neutrophils, BMI (Body Mass Index), lymphocytes, triglycerides, γGTP (Gamma-Glutamyl Transferase), AST (Aspartate Aminotransferase), LH ratio (ratio of LDL to HDL), HBs antigen (hepatitis B surface antigen), and HCV antibody (hepatitis C virus antibody).

[0028] The disease-related information is not limited to these, and may include other information as long as it provides information necessary for predicting the risk of developing a disease. The disease-related information may be, for example, information selected based on the results of analyzing the association with a disease using a Cox proportional hazards model. Methods for acquiring the disease-related information include, but are not limited to, acquiring it from a connected external database, acquiring it via a communication line, or directly inputting required information into the device 10.

[0029] Next, the prediction unit 12 predicts the disease development risk of the prediction subject from the disease-related information (S12, prediction step). The disease development risk is predicted, for example, based on the association between the disease-related information and the disease. The disease development risk prediction may be, for example, a prediction of the disease development risk within an arbitrary period of time.

[0030] The prediction unit 12 may be a trained model (disease onset risk prediction model). In this case, the disease onset risk prediction model predicts the disease onset risk of the prediction subject based on the disease-related information. The disease onset risk prediction model is, for example, a trained model that outputs the disease onset risk of the prediction subject when the disease-related information is input. The disease onset risk prediction model can also be referred to as a trained model that is machine-learned using, for example, multiple pieces of disease-related information as training data and causes a computer to function to predict the disease onset risk of the prediction subject. The multiple pieces of disease-related information may or may not include the disease-related information of the prediction subject. The disease onset risk prediction model may be, for example, a trained model using a Cox proportional hazards model. The covariates may be selected, for example, by a stepwise method using AIC (Akaike's Information Criterion). The trained model may, for example, evaluate the AUC (Area Under Curve) for disease onset within an arbitrary period using ROC (Receiver Operating Characteristic) analysis. The trained model may be, for example, a trained model that predicts the risk of developing a disease within an arbitrary period of time. In this case, the probability of developing a disease within an arbitrary period of time can be calculated, for example, by the following formula (1): In the formula, t is the arbitrary period of time, S ０ is the baseline survival function, X is the test value, β is the regression coefficient of the prediction model, and the X bar represents the mean of the test value.

[0031] Thereafter, the output unit 13 outputs the disease development risk (S13, output step), and the process ends. The disease development risk to be output is not particularly limited as long as it can grasp the disease development risk of the prediction subject, and may be, for example, an absolute evaluation, a relative evaluation, a numerical value, an evaluation result based on a threshold, etc. The output may be, for example, output to the output device 106 included in the present apparatus 10, or may be output to an output device included in a device other than the present apparatus 10.

[0032] As described above, according to the disease risk prediction device of the present disclosure, the information acquisition unit 11 acquires information on the subject's age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10) as disease-related information, the prediction unit 12 predicts the subject's lung cancer risk from the disease-related information, and the output unit 13 outputs the lung cancer risk. This makes it possible to predict the lung cancer risk regardless of the subject's smoking status. Therefore, for example, it is possible to predict the lung cancer risk even for non-smokers. Furthermore, the disease-related information includes only a minimum number of tumor markers. Therefore, it is possible to predict the lung cancer risk without using multiple tumor markers.

[0033] [Embodiment 2] Next, a disease onset risk prediction marker, which serves as an index for predicting the risk of developing a disease, will be described.

[0034] The disease onset risk prediction marker of the present disclosure serves as an indicator for predicting the risk of developing a disease, the disease being lung cancer. The disease onset risk prediction marker is used in combination with information including age and forced expiratory volume in one second (FEV10), and includes carcinoembryonic antigen (CEA). The disease onset risk prediction marker may further include, for example, estimated glomerular filtration rate (eGFR Cre). Furthermore, for example, at least one of the disease-related information may be included as the combined information or the disease onset risk prediction marker. The description of the disease onset risk prediction device described above can be used to explain the disease onset risk prediction marker of the present disclosure.

[0035] [Embodiment 3] Next, the selection of disease-related information (covariates) for predicting the risk of developing a disease, the creation of a trained model, and the evaluation results of the trained model will be described with reference to Figures 4 to 10.

[0036] 4 is a diagram showing the breakdown of the training data and the evaluation data. In creating a trained model, as shown in FIG. 4, for example, a portion of the plurality of pieces of disease-related information may be randomly selected and used as training data, a portion may be used as evaluation data, and the remaining portion may be used for final evaluation.

[0037] The training data may be, for example, data selected based on any condition. Fig. 5 is a diagram showing an example of the selection of data to be used as training data. The any condition may exclude, for example, cases where the disease developed before the start of observation (HC1) (5) and cases where there is only one medical checkup information up to the last medical checkup (HCx) (6), as shown in Fig. 5, and the remaining data (1 to 4) may be used as training data.

[0038] Here, an example of a method for selecting disease-related information (covariates) for predicting the risk of developing a disease based on selected training data will be described. The disease-related information can be obtained, for example, using a Cox proportional hazards model. The Cox proportional hazards model may be used to analyze the association between the disease-related information and the disease.

[0039] Next, an example of creating a disease onset risk prediction model using disease-related information will be shown. The disease onset risk prediction model utilizes a Cox proportional hazards model, and covariates can be selected from the disease-related information, for example, by applying a stepwise method using AIC. The created prediction model can be evaluated by evaluating the AUC for disease onset within an arbitrary period using ROC analysis. The created trained model can predict the risk of developing the disease.

[0040] Below, an example of the association between the disease-related information and the disease analyzed using the Cox proportional hazards model, and a trained model are shown. Note that the "corrected p-value" in Table 1 means the p-value after correction using the Benjamini-Hochberg method.

[0041] Malignant neoplasms of the bronchus and lungs (tumors) (C34)

[0042] Figure 6 is a scatter plot showing the relationship between the hazard ratio and p-value of disease-related information related to malignant bronchial and pulmonary neoplasms (tumors) (C34). In Figure 6, the vertical axis represents -log(p-value), and the horizontal axis represents the hazard ratio. When predicting the risk of developing malignant bronchial and pulmonary neoplasms (tumors) (C34), it is preferable to include disease-related information with a small corrected p-value (large -log(p-value)) and a large or small hazard ratio as a covariate, as shown in Table 1 and Figure 6. Figure 7 compares the details of trained models for predicting the development of malignant bronchial and pulmonary neoplasms (tumors) (C34). As shown in Figure 7, the disease-related information "age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10)" can be used as indicators of disease development risk.

[0043] The prediction accuracy was compared for a disease risk prediction model including information on "age, gender, and smoking" as disease-related information, a disease risk prediction model including information on "age, carcinoembryonic antigen (CEA)," and a disease risk prediction model including information on "age, carcinoembryonic antigen (CEA) and forced expiratory volume in one second (FEV10)." Figure 8 compares the prediction accuracy of trained models for predicting the onset of bronchial and pulmonary malignant neoplasms (tumors) (C34). In each graph in Figure 8, the vertical axis represents sensitivity and the horizontal axis represents specificity. The age used to evaluate the prediction accuracy of all disease risk prediction models was 50 years or older. Details of the disease risk prediction model including information on "age, gender, and smoking" and the disease risk prediction model including information on "age and carcinoembryonic antigen (CEA)" are shown in Figure 7. As shown in Figure 8, the disease onset risk prediction model (AUC = 0.82) that includes information on "age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10)" as disease-related information was found to have higher prediction accuracy than the disease onset risk prediction model (AUC = 0.74) that includes information on "age, sex, and smoking" and the disease onset risk prediction model (AUC = 0.80) that includes information on "age and carcinoembryonic antigen (CEA)." This result was found to have the same accuracy as a prediction model (AUC = 0.83) including four tumor markers disclosed in a prior publication (Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer. Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins. JAMA Oncol. 2018;4(10):e182078. doi:10.1001 / jamaoncol.2018.2078). Therefore, according to the present disclosure, for example, since measurement or testing of numerous tumor markers is not required, the risk of developing a disease can be predicted more easily than when using existing prediction models.

[0044] A disease risk prediction model was used to predict disease risk, including disease-related information such as age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10). Figure 9 shows the results of a trained model for predicting the onset of bronchial and pulmonary malignant neoplasms (tumors) (C34). In Figure 9, the vertical axis represents the linear predictor, and the horizontal axis represents the smoking status of the individual. As shown in Figure 9, high-risk individuals were identified among non-smokers in the event group (disease onset). This demonstrates that disease onset risk can be predicted for ex-smokers and non-smokers, regardless of their current smoking status.

[0045] Next, we compared the prediction accuracy by age for disease risk prediction models that included information on "age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10)" as disease-related information. Figure 10 compares the prediction accuracy by age for trained models for predicting the onset of bronchial and pulmonary malignant neoplasms (tumors) (C34). In each graph in Figure 10, the vertical axis represents sensitivity and the horizontal axis represents specificity. As shown in Figure 10, for all age groups, high prediction accuracy was observed even when only information on age was included (AUC = 0.83). However, the disease risk prediction model that included information on "age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10)" as disease-related information showed higher prediction accuracy than the other disease risk prediction models (AUC = 0.87). On the other hand, when age was limited to 50 years or older and 60 years or older, the prediction accuracy was low when only age information was included (AUC = 0.71, and AUC = 0.64), but the disease risk prediction model including information on "age, carcinoembryonic antigen (CEA), and forced expiratory volume in 1 second (FEV10)" showed significantly improved prediction accuracy (AUC = 0.82, and AUC = 0.78). This indicates that the disease risk prediction model has higher prediction accuracy for those 50 years or older and 60 years or older.

[0046] [Embodiment 4] The program of the present disclosure is a program for causing a computer to execute each of the steps of the present disclosure described above. Specifically, the program of the present disclosure is a program for causing a computer to execute, for example, an information acquisition procedure, a prediction procedure, and an output procedure.

[0047] The program of the present disclosure can also be said to be a program that causes a computer to function as, for example, an information acquisition procedure, a prediction procedure, and an output procedure.

[0048] The program of the present disclosure can be incorporated by reference to the descriptions of the disease onset risk prediction device and disease onset risk prediction method according to embodiments 1 to 3 of the present disclosure. For example, the "procedure" in each of the steps can be replaced with "processing." The program of the present disclosure may also be recorded on a computer-readable recording medium. The recording medium may be, for example, a non-transitory computer-readable storage medium. The recording medium is not particularly limited, and examples thereof include random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., solid state drive (SSD), USB flash memory, SD / SDHC card, etc.), optical disc (e.g., CD-R / CD-RW, DVD-R / DVD-RW, BD-R / BD-RE, etc.), magneto-optical disk (MO), floppy disk (FD), etc. Furthermore, the program of the present disclosure (e.g., a programming product or a program product) may be distributed from an external computer, for example. The "distribution" may be, for example, distribution via a communication network or distribution via a device connected via a wire. The program of the present disclosure may be installed and executed on the device to which it is distributed, or may be executed without being installed.

[0049] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0050] <Supplementary Notes> Some or all of the above embodiments may be described as, but are not limited to, the following supplementary notes. (Supplementary Note 1) A disease onset risk prediction device comprising an information acquisition unit, a prediction unit, and an output unit, wherein the information acquisition unit acquires disease-related information of a subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction unit predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output unit outputs the risk of developing the disease. (Supplementary Note 2) The disease onset risk prediction device according to Supplementary Note 1, wherein the disease-related information further includes information on estimated glomerular filtration rate (eGFR Cre) converted to creatinine. (Supplementary Note 3) The disease onset risk prediction device according to Supplementary Note 1 or 2, wherein the lung cancer is at least one of those classified under C34 (Malignant neoplasms of the bronchus and lung <tumor>) of the International Classification of Diseases (ICD-10). (Appendix 4) A disease onset risk prediction marker which serves as an index for predicting the risk of developing a disease, wherein the disease is lung cancer, and which is used in combination with information including age and forced expiratory volume in one second (FEV10), and which includes carcinoembryonic antigen (CEA). (Appendix 5) The disease onset risk prediction marker according to Appendix 4, which further includes estimated glomerular filtration rate converted to creatinine (eGFR Cre). (Appendix 6) The disease onset risk prediction marker according to Appendix 4 or 5, wherein the lung cancer is at least one classified under C34 of the International Classification of Diseases (ICD-10). (Supplementary Note 7) A disease onset risk prediction method comprising an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of a subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction step predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output step outputs the risk of developing the disease, wherein each of the steps is carried out by a computer. (Supplementary Note 8) The disease onset risk prediction method according to Supplementary Note 7, wherein the disease-related information further includes information on creatinine-equivalent estimated glomerular filtration rate (eGFR Cre).(Appendix 9) The method for predicting a disease onset risk according to Appendix 7 or 8, wherein the lung cancer is at least one of those classified as C34 (Malignant neoplasms of the bronchus and lung <tumor>) of the International Classification of Diseases (ICD-10). (Appendix 10) A program for causing a computer to execute each of the steps, including an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of a subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction step predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output step outputs the risk of developing the disease. (Appendix 11) The program according to Appendix 10, wherein the disease-related information further includes information on creatinine-equivalent estimated glomerular filtration rate (eGFR Cre). (Appendix 12) The program according to Appendix 10 or 11, wherein the lung cancer is at least one of those classified under C34 (Malignant neoplasms of the bronchus and lung <tumor>) of the International Classification of Diseases (ICD-10). (Appendix 13) A computer-readable recording medium having recorded thereon a program for causing a computer to execute each of the steps, including an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of a subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction step predicts the subject's risk of developing the disease from the disease-related information, and the output step outputs the risk of developing the disease. (Appendix 14) The recording medium according to Appendix 13, wherein the disease-related information further includes information on creatinine-equivalent estimated glomerular filtration rate (eGFR Cre). (Appendix 15) The recording medium according to appendix 13 or 14, wherein the lung cancer is at least one classified under C34 (malignant neoplasms of the bronchus and lung <tumor>) of the International Classification of Diseases, ICD-10.

[0051] This application claims priority based on Japanese Patent Application No. 2024-105977, filed July 1, 2024, the disclosure of which is incorporated herein in its entirety by reference.

[0052] According to the present disclosure, it is possible to predict the risk of developing lung cancer regardless of the smoking status of the person being predicted. The fields to which the present disclosure can be applied are not limited, and the present disclosure can be applied to a wide range of fields using disease onset risk prediction devices.

[0053] REFERENCE SIGNS LIST 10 Disease onset risk prediction device 11 Information acquisition unit 12 Prediction unit 13 Output unit 101 Central processing unit 102 Memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication device

Claims

1. A disease onset risk prediction device comprising an information acquisition unit, a prediction unit, and an output unit, wherein the information acquisition unit acquires disease-related information of a subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction unit predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output unit outputs the risk of developing the disease.

2. The disease onset risk prediction device according to claim 1, wherein the disease-related information further includes information on estimated glomerular filtration rate converted to creatinine (eGFR Cre).

3. The disease onset risk prediction device according to claim 1 or 2, wherein the lung cancer is at least one of those classified under C34 (malignant neoplasms of the bronchus and lungs <tumor>) of the International Classification of Diseases (ICD-10).

4. A marker for predicting the risk of developing a disease, the disease being lung cancer, which is used in combination with information including age and forced expiratory volume in one second (FEV10), and which includes carcinoembryonic antigen (CEA).

5. The disease onset risk prediction marker according to claim 4, further comprising creatinine-equivalent estimated glomerular filtration rate (eGFR Cre).

6. The disease onset risk prediction marker according to claim 4 or 5, wherein the lung cancer is at least one classified as C34 of the International Classification of Diseases (ICD-10).

7. A disease onset risk prediction method comprising an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of the subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction step predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output step outputs the risk of developing the disease, and each of the steps is performed by a computer.

8. The method for predicting the risk of developing a disease according to claim 7, wherein the disease-related information further includes information on the creatinine-equivalent estimated glomerular filtration rate (eGFR Cre).

9. The method for predicting the risk of developing a disease according to claim 7 or 8, wherein the lung cancer is at least one classified under C34 (malignant neoplasms of the bronchus and lung) of the International Classification of Diseases (ICD-10).

10. A program for causing a computer to execute each of the above steps, including an information acquisition step, a prediction step, and an output step, wherein the information acquisition step acquires disease-related information of a subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction step predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output step outputs the risk of developing the disease.

11. The program according to claim 10, wherein the disease-related information further includes information on creatinine-equivalent estimated glomerular filtration rate (eGFR Cre).

12. The program according to claim 10 or 11, wherein the lung cancer is at least one classified under C34 (malignant neoplasms of the bronchus and lung) of the International Classification of Diseases (ICD-10).

13. A computer-readable recording medium having recorded thereon a program for causing a computer to execute each of the above procedures, the computer-readable recording medium comprising an information acquisition procedure, a prediction procedure, and an output procedure, wherein the information acquisition procedure acquires disease-related information of a subject to be predicted, the disease being lung cancer, the disease-related information including information on age, carcinoembryonic antigen (CEA), and forced expiratory volume in one second (FEV10), the prediction procedure predicts the risk of developing the disease of the subject to be predicted from the disease-related information, and the output procedure outputs the risk of developing the disease.

14. The recording medium according to claim 13, wherein the disease-related information further includes information on creatinine-equivalent estimated glomerular filtration rate (eGFR Cre).

15. The recording medium according to claim 13 or 14, wherein the lung cancer is at least one classified under C34 (malignant neoplasms of the bronchus and lung) of the International Classification of Diseases (ICD-10).

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