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

Abstract

This disclosure aims to provide a disease risk prediction device for predicting the risk of developing lung cancer, regardless of the smoking status of the person being predicted. [Solution] 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 a person to be predicted, the disease being lung cancer, and the disease-related information includes carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function information, the prediction unit predicts the risk of the person to develop the disease from the disease-related information, and the output unit outputs the risk of the disease to be developed.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=3(5)]]On the other hand, lung cancer can occur even in non-smokers. Also, even non-smokers may have smoked in the past, and it is difficult to accurately grasp the smoking situation. Therefore, it is desired to establish a method for predicting the onset risk of lung cancer regardless of the smoking situation.

[0005] Therefore, an object of the present disclosure is to provide a disease onset risk prediction device, a prediction marker, a prediction method, a program, and a recording medium for predicting the onset risk of lung cancer regardless of the smoking situation of the subject to be predicted.

Means for Solving the Problems

[0006] To achieve the above object, the disease onset risk prediction device of the present disclosure Including an information acquisition unit, a prediction unit, and an output unit, The aforementioned information acquisition unit acquires disease-related information of the person to be predicted, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction unit predicts the risk of the disease developing in the person being predicted based on the disease-related information. The output unit outputs the risk of developing the disease. It is a device.

[0007] The disease risk predictor markers disclosed herein are: It serves as an indicator to predict the risk of developing a disease. The aforementioned disease is lung cancer. This includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. It is a marker.

[0008] The disease onset risk prediction method disclosed herein is: This includes the information acquisition process, the prediction process, and the output process. The aforementioned information acquisition process acquires disease-related information of the target person, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction step predicts the risk of developing the disease for the person to be predicted based on the disease-related information. The output step outputs the risk of developing the disease. This method involves each of the aforementioned steps being performed by a computer.

[0009] The program disclosed herein is This includes information acquisition procedures, prediction procedures, and output procedures. The aforementioned information acquisition procedure acquires disease-related information of the target individuals, The aforementioned disease is lung cancer. As the disease-related information, it includes carcinoembryonic antigen (CEA), white blood cell count (WBC), and information regarding liver function. The prediction procedure predicts the onset risk of the disease for the person to be predicted from the disease-related information. The output procedure outputs the onset risk of the disease. It is a program for causing a computer to execute each of the above 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.

Advantages of the Invention

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

Brief Description of the Drawings

[0012] [Figure 1] FIG. 1 is a block diagram showing the configuration of an example of the disease onset risk prediction device of the present disclosure. [Figure 2] FIG. 2 is a block diagram showing an example of the hardware configuration of the disease onset risk prediction device of the present disclosure. [Figure 3] FIG. 3 is a flowchart showing an example of the processing in the disease onset risk prediction device of the present disclosure. [Figure 4] FIG. 4 is a diagram showing the breakdown of the learning data and the evaluation data. [Figure 5] FIG. 5 is a diagram showing an example of the selection of the data used for the learning data. [Figure 6] FIG. 6 is a scatter diagram showing the relationship between the hazard ratio and the p-value of the disease-related information related to malignant neoplasm <tumor> of bronchus and lung (C34). [Figure 7] FIG. 7 is a diagram showing (A) the details of the learned model for predicting the onset of malignant neoplasm <tumor> of bronchus and lung (C34) and (B) the prediction accuracy of the created learned model. [Figure 8]FIG. 8 is a diagram showing (A) details of another pre-trained model for predicting the onset of malignant neoplasms <tumors> (C34) of the bronchus and lung, and (B) the prediction accuracy of another pre-trained model created. [Figure 9] FIG. 9 is a diagram showing the prediction accuracy of (A) a pre-trained model including "age, gender, and smoking information" as an index of disease onset risk, and (B) a pre-trained model including "age and CEA" as an index of disease onset risk. [Figure 10] FIG. 10 is a diagram showing the prediction results of disease onset risk predicted by a pre-trained model for predicting the onset of malignant neoplasms <tumors> (C34) of the bronchus and lung. [Figure 11] FIG. 11 is a survival curve derived from the created pre-trained model.

MODE FOR CARRYING OUT THE INVENTION

[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. Also, the descriptions of the respective embodiments can be mutually referred to unless otherwise specified, and the configurations of the respective embodiments can be combined unless otherwise specified.

[0014] In the present disclosure, unless otherwise specified, the term "disease" means lung cancer. More specifically, examples of the lung cancer include malignant neoplasms <tumors> (C34) of the bronchus and lung in the International Classification of Diseases (ICD (International Statistical Classification of Diseases and Related Health Problems)-10). Examples of the lung cancer include, for example, 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 (after surgery), lung adenocarcinoma, hilar small cell lung cancer, non-small cell lung cancer, right lower lobe small cell lung cancer, and right upper lobe lung cancer of the right lung, etc.

[0015] [Embodiment 1] Figure 1 is a block diagram showing an example configuration of the disease onset risk prediction device 10 (hereinafter also referred to as "the device 10") of this disclosure. As shown in Figure 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 aforementioned parts, or it may be a device in which the aforementioned parts can be connected via a communication network. Furthermore, the device 10 can be connected to an external device described later via the communication network. The communication network is not particularly limited and can use a known network, for example, it may be wired or wireless. Examples of the communication network include the Internet, WWW (World Wide Web), telephone lines, LAN (Local Area Network), SAN (Storage Area Network), DTN (Delay Tolerant Networking), LPWA (Low Power Wide Area), L5G (Local 5G), etc. Examples of wireless communication include Wi-Fi (registered trademark), Bluetooth (registered trademark), Local 5G, LPWA, etc. The wireless communication may be in the form of direct communication between devices (Ad Hoc communication), infrastructure communication, indirect communication via an access point, etc. The device 10 may be, for example, incorporated into a system server. Furthermore, the device 10 may be, for example, a personal computer (PC, e.g., desktop or notebook), smartphone, tablet terminal, etc., on which the program of this disclosure is installed. In addition, the device 10 may be in the form of cloud computing or edge computing, for example, in which at least one of the aforementioned parts is on a server and the other aforementioned parts are on a terminal.

[0017] Figure 2 illustrates a block diagram of the hardware configuration of the device 10. The device 10 includes, for example, a central processing unit (CPU, GPU, etc.) 101, memory 102, bus 103, storage device 104, input device 105, output device 106, communication device 107, etc. Each part of the device 10 is interconnected via the bus 103 through its respective interface (I / F).

[0018] The central processing unit 101 operates in coordination with other components via controllers (system controller, I / O controller, etc.) and is responsible for the 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 also include other computing devices such as a CPU, GPU (Graphics Processing Unit), APU (Accelerated Processing Unit) as its computing device, or it may include a combination of a CPU and these.

[0019] Bus 103 can also be connected to external devices, for example. Examples of such 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 aforementioned communication network) via a communication device 107 connected to bus 103, for example, and can also be connected to other devices via the external network.

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

[0021] The storage device 104 is also called a so-called auxiliary storage device, for example, in relation to the main memory (primary memory). As described above, the storage device 104 stores an operating program including the program of this disclosure. The storage device 104 may be, for example, a combination of a recording medium and a drive for reading from and writing to the recording medium. The recording medium is not particularly limited and may be internal or external, for example, an HD (hard disk), CD-ROM, CD-R, CD-RW, MO, DVD, flash memory, memory card, etc. The storage device 104 may be, for example, a hard disk drive (HDD) in which the recording medium and the drive are integrated, or a solid state drive (SSD).

[0022] In this device 10, the memory 102 and the storage device 104 can also store various types of information, such as log information, information obtained from an external database (not shown) or external devices, information generated by this device 10, and information used by this device 10 when executing processing. In this case, the memory 102 and the storage device 104 may store, for example, disease-related information as described later. At least some of the information may be stored on an external server other than the memory 102 and the storage device 104, or it 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. The input device 105 may include, for example, a pointing device such as a touch panel, trackpad, or mouse; a keyboard; imaging means such as a camera or scanner; a card reader such as an IC card reader or magnetic card reader; an audio input means such as a microphone; and so on. The output device 106 may include, for example, a display device such as an LED display or liquid crystal display; an audio output device such as a speaker; a printer; and so on. In this 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 unit, such as a touch panel display.

[0024] Next, an example of the disease onset risk prediction method described herein will be explained based on the flowchart in Figure 3. The disease onset risk prediction method described herein is implemented, for example, using the device 10 shown in Figure 1 or Figure 2, as follows. However, the disease onset risk prediction method described herein is not limited to the use of the device 10 shown in Figure 1 or Figure 2.

[0025] First, the information acquisition unit 11 acquires disease-related information of the person to be predicted (S11, information acquisition step). The disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The disease-related information may be information obtained, for example, through health checkups or medical examinations, or information obtained by the person to be predicted. Information obtained by the person to be predicted may be information obtained, for example, through wearable devices or home health measurement devices. The disease-related information may also include medical record information. Examples of disease-related information include test result information, medical interview information, and subject attribute information (gender, etc.). The liver function information may include, for example, at least one piece of information selected from γ-GTP (Gamma-Glutamyl Transferase), ALP (Alkaline Phosphatase), AST (Aspartate Aminotransferase), and ALT (Alanine Aminotransferase).

[0026] Furthermore, the disease-related information includes, for example, forced expiratory volume in one second (FEV10), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), sex, urinary glucose, vital capacity, frequency of alcohol consumption (occasionally), cholinesterase, hypertension, neutrophils, LDL (Low-Density Lipoprotein), snacking, total cholesterol, body fat percentage, FEV1 (Forced Expiratory Volume in 1 Second), eosinophils, urinary protein, cancer, non-HDL cholesterol, weight gain since age 20, late dinner, lymphocytes, triglycerides, albumin, prostate-specific antigen (PSA), blood hemoglobin, frequency of alcohol consumption (daily), heart disease, hemoglobin, CA19-9 (carbohydrate antigen 19-9), hematocrit, and BUN (Blood Urinary Units). Nitrogen), diabetes, HbA1c, uric acid, exercise, urine specific gravity, AG ratio (Albumin-Globulin ratio), HDL (High-Density Lipoprotein), MCHC (Mean Corpuscular Hemoglobin Concentration), CRP (C-Reactive Protein), urine pH, sleep quality, walking speed, LH ratio (Ratio of LDL to HDL), anemia, CA125 (Cancer Antigen 125), LAP (Leucine Aminopeptidase), platelet count, creatinine, total protein, skipping breakfast, direct bilirubin, alcohol consumption (360-540 mL), blood glucose level, fecal occult blood test (2-day method, day 1), red blood cell count, fecal occult blood test (2-day method, day 2), stroke, forced vital capacity, BMI (Body Mass Index), alpha-fetoprotein (AFP), total bilirubin, systolic blood pressure (first measurement), alcohol consumption (180-360 mL) Examples of indicators include (mL), waist circumference, basophils, LD or LDH (Lactate Dehydrogenase), weight change over one year, monocytes, first diastolic blood pressure reading, alcohol consumption (540 mL or more), chronic obstructive pulmonary disease, kidney disease, etc.

[0027] Here, the FEV10 refers, for example, to the ratio of the forced expiratory volume in one second to the total expiratory volume. The FEV10 can be measured, for example, by the method described in "Clinical Respiratory Function Tests, 7th Edition (edited by the Pulmonary Physiology Committee of the Japanese Respiratory Society)." The FEV10 can be calculated, for example, by the following formula. 1 second rate (FEV10) = 1 second volume (FEV1) / forced vital capacity (FVC) x 100

[0028] The disease-related information is not limited to this, and may include other information as long as it provides the information necessary to predict the risk of disease onset. The disease-related information may be selected based on the results of analyzing the association with diseases using a Cox proportional hazards model, for example. The method for obtaining the disease-related information includes, but is not limited to, obtaining it from a connected external database, obtaining it via a communication line, or directly inputting the necessary items into this device 10.

[0029] Next, the prediction unit 12 predicts the disease onset risk of the target individual from the disease-related information (S12, prediction step). The prediction of the disease onset risk is made, for example, based on the relationship between the disease-related information and the disease. The prediction of the disease onset risk may be, for example, a prediction of the disease onset risk within any given period.

[0030] The prediction unit 12 may be a pre-trained model (disease onset risk prediction model). In this case, the disease onset risk prediction model predicts the disease onset risk of the target person based on the disease-related information. The disease onset risk prediction model is, for example, a pre-trained model that outputs the disease onset risk of the target person when the disease-related information is input. Alternatively, the disease onset risk prediction model can be, for example, a pre-trained model that is machine-learned using multiple disease-related information as training data, and is designed to function in a computer to predict the disease onset risk of the target person. The multiple disease-related information may or may not include the disease-related information of the target person. The disease onset risk prediction model may be, for example, a pre-trained model that utilizes a Cox proportional hazards model. The covariates may be selected, for example, by a stepwise method using AIC (Akaike's Information Criterion). The pre-trained model may, for example, evaluate the AUC (Area Under Curve) related to disease onset within an arbitrary period using ROC (Receiver Operating Characteristic) analysis. Furthermore, the trained model may be, for example, a trained model that predicts the risk of developing a disease within any given period. In this case, the probability of developing a disease within any given period can be calculated, for example, by the following equation (1). In the following equation, t is the arbitrary period, S0 is the baseline survival function, X is the test value, β is the regression coefficient of the prediction model, and X-bar represents the mean of the test value.

number

[0031] Subsequently, the output unit 13 outputs the disease onset risk (S13, output process) and terminates. The output disease onset risk is not particularly limited as long as it allows for the understanding of the disease onset risk of the person being predicted, but 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, an output to the output device 106 provided by this device 10, or an output to an output device provided by another device other than this device 10.

[0032] As described above, the disease risk prediction device of this disclosure has the following capabilities: the information acquisition unit 11 acquires information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function as disease-related information for the person to be predicted; the prediction unit 12 predicts the risk of developing lung cancer for the person to be predicted from the disease-related information; and the output unit 13 outputs the risk of developing lung cancer. Therefore, the risk of developing lung cancer can be predicted regardless of the smoking status of the person to be predicted. For example, it is possible to predict the risk of developing lung cancer even for non-smokers. Furthermore, the disease-related information includes only a minimum number of tumor markers. Therefore, for example, it is possible to predict the risk of developing lung cancer without using multiple tumor markers.

[0033] [Embodiment 2] Next, we will explain disease risk prediction markers, which are indicators that predict the risk of developing a disease.

[0034] The disease onset risk prediction markers of this disclosure are indicators that predict the risk of developing a disease, the disease being lung cancer. The disease onset risk prediction markers include information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The disease onset risk prediction markers are also used in combination with information on at least one of, for example, age and forced expiratory volume in one second (FEV10). The liver function information may include, for example, at least one selected from γ-GTP, ALP, AST, and ALT. Furthermore, for example, at least one of the disease-related information may be included as the combined information or as the disease onset risk prediction marker. The description of the disease onset risk prediction markers of this disclosure can be based on the description of the disease onset risk prediction device described above.

[0035] [Embodiment 3] The program of this disclosure is a program that causes a computer to perform each of the steps of the disclosure described above. Specifically, the program of this disclosure is a program that causes a computer to perform, for example, an information acquisition procedure, a prediction procedure, and an output procedure.

[0036] Furthermore, the program described herein can also be described as a program that causes a computer to function, for example, as an information acquisition procedure, a prediction procedure, and an output procedure.

[0037] The program of this disclosure can be based on the descriptions in the disease onset risk prediction device, disease onset risk prediction method, and disease onset risk prediction marker of Embodiments 1 and 2 of this disclosure. In each of the aforementioned procedures, for example, "procedure" can be read as "process." The program of this disclosure may also be recorded on a computer-readable recording medium, for example. The recording medium is, for example, a non-transitory computer-readable storage medium. The recording medium is not particularly limited and includes, for example, random access memory (RAM), read-only memory (ROM), hard disk (HD), flash memory (e.g., SSD (Solid State Drive), 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. The program of this disclosure (for example, also called a programming product or program product) may also be delivered, for example, from an external computer. The aforementioned "distribution" may be, for example, distribution via a communication network, or distribution via a wired connected device. The program of this disclosure may be installed and executed on the distributed device, or it may be executed without being installed. [Examples]

[0038] Next, we will explain the selection of disease-related information (covariates) for predicting disease onset risk, the creation of a trained model, and the evaluation results of the trained model using Figures 4 to 11.

[0039] Figure 4 shows the breakdown of training data and evaluation data. Health checkup information, medical examination data, and medical record information were obtained from 6098 individuals (ages 20-90) who underwent CEA measurement between 2012 and 2020. As shown in Figure 4, a portion of the aforementioned disease-related information was randomly selected and used as training data, and a portion was used as evaluation data. In this embodiment, the endpoint was the onset of C34 "Malignant neoplasms (tumors) of the bronchi and lungs" (hereinafter sometimes simply referred to as "disease" or "lung cancer") according to the International Classification of Diseases (ICD-10), and confirmed diagnoses and suspected diagnoses were excluded.

[0040] Figure 5 shows an example of data selection for use as training data. As shown in Figure 5, cases where the onset of the above disease was diagnosed before the start of observation (HC1) or where there was only one health checkup record up to the last health checkup (HCx) were excluded, and the remaining data was used as training data.

[0041] The predictive model was constructed using a Cox proportional hazards model. Covariates were selected by applying a stepwise method with AIC. The constructed predictive model was evaluated using AUC (presence or absence of the above disease within 4 years) and the C statistic. In addition, survival curves were plotted and visualized from the constructed predictive model.

[0042] The following shows the relationship between the disease-related information and the disease itself, as analyzed using a Cox proportional hazards model, and an example of a trained model. Note that the "corrected p-value" in Tables 1-1 and 1-2 refers to the p-value after correction using the Benjamini-Hochberg method.

[0043] <Malignant neoplasms (tumors) of the bronchi and lungs (C34)> [Table 1-1] [Table 1-2]

[0044] Figure 6 is a scatter plot showing the relationship between hazard ratios and p-values ​​for disease-related information concerning malignant neoplasms (tumors) of the bronchi and lungs (C34). In Figure 6, the vertical axis represents -log(q-value), and the horizontal axis represents the hazard ratio. The q-value was calculated from the p-value after correction using the Benjamini-Hochberg method. When predicting the risk of developing malignant neoplasms (tumors) of the bronchi and lungs (C34), it can be seen from Tables 1-1, 1-2, and Figure 6 that it is preferable to include disease-related information as covariates that has a small corrected p-value (large -log(q-value)) and a large or small hazard ratio.

[0045] Figure 7 shows (A) details of a trained model for predicting the development of malignant neoplasms (tumors) (C34) of the bronchi and lungs, and (B) the predictive accuracy of the trained model. In Figure 7(B), the vertical axis represents sensitivity and the horizontal axis represents specificity. As shown in Figure 7(A), a trained model that includes "carcinoembryonic antigen (CEA), white blood cell count, and AST as information on liver function" as disease-related information, and further includes "BUN, FEV10, and Eos," was found to have an AUC (Derivation) of 0.79 and an AUC (Verification) of 0.79, as shown in Figure 7(B).

[0046] Figure 9 shows the predictive accuracy of two trained models: (A) a trained model that includes "age, sex, and smoking information" as indicators of disease onset risk, and (B) a trained model that includes "age and CEA" as indicators of disease onset risk. In Figure 9, the vertical axis represents sensitivity and the horizontal axis represents specificity. As shown in Figures 7(B) and 9, the trained model in Figure 7 showed higher predictive accuracy for disease onset compared to both the trained model that includes "age, sex, and smoking information" as indicators of disease onset risk (AUC (Derivation) = 0.63, AUC (Verification) = 0.74) and the trained model that includes "age and CEA" (AUC (Derivation) = 0.61, AUC (Verification) = 0.75).

[0047] Figure 8 shows (A) details of other trained models for predicting the onset of malignant neoplasms (tumors) of the bronchi and lungs (C34) and (B) the predictive accuracy of other trained models created. In Figure 8(B), the vertical axis is sensitivity and the horizontal axis is specificity. As shown in Figure 8(A), a trained model that includes "carcinoembryonic antigen (CEA), white blood cell count, and ALT as information on liver function" as disease-related information, and further includes "age and LDL," was found to have an AUC (Derivation) of 0.74 and an AUC (Verification) of 0.73, as shown in Figure 8(B). From these results, it was found that the trained model in Figure 8 also shows predictive accuracy equivalent to the trained model in Figure 9. In particular, the trained model in Figure 8 does not require "FEV10" information, which requires a special testing environment, as the trained model in Figure 7 does, and can predict the risk of disease onset from only age and blood test information. Therefore, for example, by using a service that allows blood tests to be performed at home, the pre-trained model shown in Figure 8 can be used to easily understand the above-mentioned risk of developing the disease at home.

[0048] Next, we performed disease onset risk prediction using the trained predictive model shown in Figure 7. Figure 10 shows the predicted disease onset risk results predicted by the trained model for predicting the onset of malignant neoplasms (tumors) (C34) of the bronchi and lungs. In Figure 10, the vertical axis is the linear predictor, and the horizontal axis is non-event (FALSE) and event (TRUE). As shown in Figure 10, we were able to identify high-risk individuals among non-smokers in the event group (disease onset). From this, we found that the risk of disease onset can be predicted not only for current smoking status but also for former smokers and non-smokers.

[0049] Figure 11 shows the survival curve derived from the trained model created in Figure 7. As shown in Figure 11, the trained model was found to be able to identify populations at high risk of developing the disease. These results suggest the potential for early intervention to prevent the onset of the disease.

[0050] As described above, this disclosure makes it possible to predict the risk of developing lung cancer regardless of the smoking status of the person being predicted.

[0051] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure are possible, which can be understood by those skilled in the art within the scope of the present disclosure.

[0052] <Note> Some or all of the above embodiments may be described as follows, but are not limited to the following: (Note 1) Including an information acquisition unit, a prediction unit, and an output unit, The aforementioned information acquisition unit acquires disease-related information of the person to be predicted, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction unit predicts the risk of the disease developing in the person being predicted based on the disease-related information. The output unit outputs the risk of developing the disease. A device for predicting the risk of developing a disease. (Note 2) The disease onset risk prediction device according to Appendix 1, further comprising information on age and at least one of forced expiratory volume in one second (FEV10) as disease-related information. (Note 3) A disease risk prediction device according to Appendix 1 or 2, wherein the information relating to liver function includes at least one piece of information selected from γ-GTP, ALP, AST, and ALT. (Note 4) A disease onset risk prediction device as described in any of Appendix 1 to 3, wherein the lung cancer is classified as at least one of the C34 (malignant neoplasms of the bronchi and lungs <tumors>) of the International Classification of Diseases ICD-10. (Note 5) It serves as an indicator to predict the risk of developing a disease. The aforementioned disease is lung cancer. This includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. Disease risk predictor markers. (Note 6) This information is used in conjunction with information on age and at least one of the forced expiratory volume in one second (FEV10). Disease risk predictor markers as described in Appendix 5. (Note 7) The aforementioned liver function information includes at least one piece of information selected from γ-GTP, ALP, AST, and ALT. Disease risk predictor markers as described in Appendix 5 or 6. (Note 7) The aforementioned lung cancer is classified as at least one of the C34 categories in the International Classification of Diseases (ICD-10). A disease onset risk predictor marker listed in any of Appendix 5 to 7. (Note 9) This includes the information acquisition process, the prediction process, and the output process. The aforementioned information acquisition process acquires disease-related information of the target person, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction step predicts the risk of developing the disease for the person to be predicted based on the disease-related information. The output step outputs the risk of developing the disease. A method for predicting the risk of disease onset, wherein each of the aforementioned steps is performed by a computer. (Note 10) The disease onset risk prediction method described in Appendix 9 further includes, as disease-related information, information on age and at least one of FEV10. (Note 11) A method for predicting the risk of disease onset according to Appendix 9 or 10, wherein the information relating to liver function includes at least one piece of information selected from γ-GTP, ALP, AST, and ALT. (Note 12) A disease onset risk prediction method described in any of the appendices 9 to 11, wherein the lung cancer is classified as at least one of the C34 (malignant neoplasms of the bronchi and lungs <tumors>) of the International Classification of Diseases ICD-10. (Note 13) This includes information acquisition procedures, prediction procedures, and output procedures. The aforementioned information acquisition procedure acquires disease-related information of the target individuals, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction procedure predicts the risk of developing the disease for the person to be predicted based on the disease-related information, The output procedure outputs the risk of developing the disease. A disease onset risk prediction program that causes a computer to perform each of the above steps. (Note 14) The disease onset risk prediction program described in Appendix 13 further includes, as disease-related information, information on age and at least one of FEV10. (Note 15) A disease risk prediction program according to Appendix 13 or 14, comprising, as information relating to liver function, at least one piece of information selected from γ-GTP, ALP, AST, and ALT. (Note 16) A disease risk prediction program as described in any of Appendix 13 to 15, wherein the lung cancer is classified as at least one of the C34 (malignant neoplasms of the bronchi and lungs <tumors>) in the International Classification of Diseases (ICD-10). (Note 17) This includes information acquisition procedures, prediction procedures, and output procedures. The aforementioned information acquisition procedure acquires disease-related information of the target individuals, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction procedure predicts the risk of developing the disease for the person to be predicted based on the disease-related information, The output procedure outputs the risk of developing the disease. A computer-readable recording medium containing a program that causes a computer to perform each of the aforementioned steps. (Note 18) The recording medium according to Appendix 17 further includes, as disease-related information, information on age and at least one of FEV10. (Note 19) The recording medium according to Appendix 17 or 18, wherein the information relating to liver function includes at least one piece of information selected from γ-GTP, ALP, AST, and ALT. (Note 20) A recording medium as described in any of the appendices 17 to 19, wherein the lung cancer is classified as at least one of the malignant neoplasms (tumors) of the bronchi and lungs in the International Classification of Diseases (ICD-10). [Industrial applicability]

[0053] According to this disclosure, the risk of developing lung cancer can be predicted regardless of the smoking status of the person being predicted. The fields to which this disclosure can be applied are not limited and it is applicable to a wide range of fields using disease risk prediction devices. [Explanation of symbols]

[0054] 10. Disease onset risk prediction device 11 Information acquisition department 12 Prediction Section 13 Output section 101 Central Processing Unit 102 memory 103 Bus 104 Storage device 105 Input device 106 Output device 107 Communication devices

Claims

1. Including an information acquisition unit, a prediction unit, and an output unit, The aforementioned information acquisition unit acquires disease-related information of the person to be predicted, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction unit predicts the risk of the disease developing in the person being predicted based on the disease-related information. The output unit outputs the risk of developing the disease. A device for predicting the risk of developing a disease.

2. The disease onset risk prediction device according to claim 1, further comprising information on age and at least one of forced expiratory volume in one second (FEV10) as disease-related information.

3. The disease onset risk prediction device according to claim 1 or 2, wherein the information relating to liver function includes at least one piece of information selected from γ-GTP, ALP, AST, and ALT.

4. The disease onset risk prediction device according to claim 1 or 2, wherein the lung cancer is classified as at least one of the malignant neoplasms (tumors) of the bronchi and lungs in the International Classification of Diseases (ICD-10).

5. It serves as an indicator to predict the risk of developing a disease. The aforementioned disease is lung cancer. This includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. Disease risk predictor markers.

6. This information is used in conjunction with information on age and at least one of the forced expiratory volume in one second (FEV10). A disease onset risk prediction marker according to claim 5.

7. The aforementioned liver function information includes at least one piece of information selected from γ-GTP, ALP, AST, and ALT. A disease onset risk predictor marker according to claim 5 or 6.

8. This includes the information acquisition process, the prediction process, and the output process. The aforementioned information acquisition process acquires disease-related information of the target person, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction step predicts the risk of developing the disease for the person to be predicted based on the disease-related information. The output step outputs the risk of developing the disease. A method for predicting the risk of disease onset, wherein each of the aforementioned steps is performed by a computer.

9. This includes information acquisition procedures, prediction procedures, and output procedures. The aforementioned information acquisition procedure acquires disease-related information of the target individuals, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction procedure predicts the risk of developing the disease for the person to be predicted based on the disease-related information, The output procedure outputs the risk of developing the disease. A disease onset risk prediction program that causes a computer to perform each of the above steps.

10. This includes information acquisition procedures, prediction procedures, and output procedures. The aforementioned information acquisition procedure acquires disease-related information of the target individuals, The aforementioned disease is lung cancer. The aforementioned disease-related information includes information on carcinoembryonic antigen (CEA), white blood cell count (WBC), and liver function. The prediction procedure predicts the risk of developing the disease for the person to be predicted based on the disease-related information, The output procedure outputs the risk of developing the disease. A computer-readable recording medium containing a program that causes a computer to perform each of the aforementioned steps.

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