Determination method, determination device, and computer program

Abstract

Provided is a determination method with which it is possible to determine the possibility of cancer using a receptor.　The determination method involves a computer executing processing in which: a response signal is acquired for a target sample derived from a determination target, the response signal being detected by using a receptor that indicates the reactivity corresponding to the possibility of cancer in a target; and the possibility of cancer is determined in the determination target on the basis of the acquired response signal for the target sample.

Description

Determination method, determination device, and computer program

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

[0002] In recent years, research and development on biosensors using biological elements has been progressing. For example, Patent Document 1 discloses a low-cost odor sensor that includes a transistor with a gate electrode containing aluminum or aluminum oxide, an insect cell having an olfactory receptor placed on the gate electrode, and a detection device that detects the current generated in the transistor when the insect cell responds to an odor.

[0003] Japanese Patent Application Laid-Open No. 2018-113957

[0004] However, the technology described in Patent Document 1 does not use receptors to determine the possibility of cancer.

[0005] An object of the present disclosure is to provide a method for determining the possibility of cancer using a receptor.

[0006] A determination method according to one aspect of the present disclosure involves acquiring a response signal to a target sample derived from the target, detected using a receptor that exhibits reactivity according to the likelihood of cancer in the target, and having a computer perform a process to determine the likelihood of cancer in the target based on the acquired response signal to the target sample.

[0007] A determination device according to one aspect of the present disclosure includes a control unit that acquires a response signal to a target sample derived from the target to be determined, detected using a receptor that exhibits reactivity according to the possibility of cancer in the target, and executes a process of determining the possibility of cancer in the target based on the acquired response signal to the target sample.

[0008] A computer program according to one aspect of the present disclosure acquires a response signal to a subject sample derived from the subject, detected using a receptor that exhibits reactivity according to the possibility of cancer in the subject, and determines the possibility of cancer in the subject based on the acquired response signal to the subject sample.

[0009] According to the present disclosure, the receptor can be used to determine the likelihood of cancer.

[0010] 1 is a schematic diagram of a determination system. FIG. 1 is a schematic diagram showing an example of the configuration of an olfactory sensor. FIG. 2 is a block diagram showing the configuration of a determination device. FIG. 3 is a diagram showing an example of the content of information stored in a detection DB. FIG. 4 is a block diagram showing the configuration of a terminal device. FIG. 5 is a diagram showing an example of changes in luminescence intensity of a sensor cell over time. FIG. 6 is a schematic diagram showing an example of a reception screen. FIG. 7 is a diagram showing an example of a reference profile related to cells selected for cancer determination. FIG. 8 is a diagram showing an example of a reference profile related to cells not selected for disease determination. FIG. 9 is a schematic diagram showing an example of a result screen showing determination results. FIG. 10 is a schematic diagram showing another example of a result screen showing determination results. FIG. 11 is a flowchart showing an example of a reference profile generation process. FIG. 12 is a flowchart showing an example of a process procedure for determining the possibility of cancer. FIG. 13 is a flowchart showing an example of a process procedure performed by a determination device of a second embodiment. FIG. 14 is a diagram showing an example of a response profile depending on whether or not a correction process is performed. FIG. 15 is a block diagram showing an example of the configuration of a determination device of a third embodiment. FIG. 16 is an explanatory diagram showing an overview of a learning model. FIG. 17 is a flowchart showing an example of a process procedure for determining the possibility of cancer performed by a determination system of a third embodiment. FIG. 18 is a flowchart showing an example of a re-learning process for a learning model. FIG. 19 is a flowchart showing an example of a process procedure performed by a determination system of a fourth embodiment. FIG. 19 is a schematic diagram showing an example of a result screen showing the determination results of the fourth embodiment.

[0011] The present disclosure will be specifically described with reference to the drawings showing embodiments thereof.

[0012] (First embodiment) Fig. 1 is a schematic diagram of a determination system 100. The determination system 100 of this embodiment includes a determination device 1, a terminal device 2, a detection device 3, and an olfactory sensor 4. The olfactory sensor 4 is an example of a biosensor. The determination device 1 is communicably connected to the terminal device 2 and the detection device 3 via a network N such as the Internet. The determination system 100 determines the possibility of cancer in a subject based on the detection results of odor molecules in a specimen derived from the subject detected by the detection device 3 and the olfactory sensor 4, and provides a service in which the determination device 1 presents the determination results via the terminal device 2.

[0013] In the following embodiment, an example will be described in which the possibility of cancer in a subject is determined based on odor molecules detected in the subject's urine as a specimen (sample). The types of cancer to be determined are not limited, but may include, for example, lung cancer, esophageal cancer, breast cancer, stomach cancer, liver cancer, pancreatic cancer, gallbladder cancer, bile duct cancer, colon cancer, kidney cancer, bladder cancer, ovarian cancer, uterine cancer, prostate cancer, oral cancer, and pharyngeal cancer.

[0014] The determination device 1 is an information processing device capable of various information processing and information transmission and reception, such as a server computer, a personal computer, a quantum computer, etc. The determination device 1 acquires a signal indicating an odor response to urine from a subject, determines the possibility of cancer in the subject based on the acquired signal, and provides the determination result to a user via the terminal device 2. The determination device 1 may be a local computer provided in the facility where the detection device 3 is installed.

[0015] The terminal device 2 is an information processing terminal used by the subject, and is, for example, a personal computer, a smartphone, a tablet terminal, etc. The terminal device 2 displays the determination result received from the determination device 1. The subject is an example of a user who receives the determination result. The determination result is not limited to being provided to the subject, but may also be provided to, for example, medical professionals, personnel at the analytical institution that performs the analysis, etc. The number of terminal devices 2 connected to the determination device 1 may be one or three or more.

[0016] The olfactory sensor 4 and the detection device 3 are managed by, for example, an analytical institution. The analytical institution is a facility that receives samples collected from subjects and analyzes the received samples. The olfactory sensor 4 includes cells 42 (see FIG. 2 ) that express olfactory receptors as detection elements, and outputs a signal indicating the response of the olfactory receptors to odor molecules. The detection device 3 detects the response signal from the olfactory sensor 4. In addition to functioning as a detector that detects the response signal described above, the detection device 3 also functions as a computer that processes various data and communicates with external devices, and transmits detection data of the response signal obtained by detection to the determination device 1 via the network N. The detector and the computer may be provided separately and configured to be able to communicate with each other. The number of detection devices 3 connected to the determination device 1 may be one or three or more.

[0017] FIG. 2 is a schematic diagram showing an example configuration of the olfactory sensor 4. FIG. 2 is a view of the olfactory sensor 4 as seen from above. The olfactory sensor 4 includes a substrate 41 and cells 42 arranged in wells 43 formed in the substrate 41. For example, a "384-well plate" having 384 wells 43 formed therein is used as the substrate 41. For simplicity of illustration, FIG. 2 shows a "24-well plate" having 24 wells 43 formed therein. In this embodiment, cells 42 consisting of a plurality of cells of the same type are arranged in each of the multiple wells 43. That is, a large number of cells of the same type are seeded in each well 43 at high density. Note that the cells 42 may be composed of a single type of cell. Each cell has an olfactory receptor. The cells 42 function as sensor cells that output signals indicating the response of the olfactory receptor to odor molecules. The cells 42 are an example of a sensor element. The olfactory receptor responds to specific odor molecules. By detecting a response signal based on the binding between the olfactory receptors provided in the olfactory sensor 4 and the odor molecules, it is possible to selectively detect the odor molecules contained in the urine of the subject.

[0018] The substrate 41 is made of a material such as glass, silicon, ceramics, resin, or metal. The surface of the substrate 41 may be subjected to surface treatment such as plasma treatment, corona treatment, or UV-Ozone treatment, or may be coated with a polypeptide or the like. The shape of the substrate 41 may be any shape that allows odor detection using the cells 42 arranged on the substrate 41, such as a rectangular plate. The size of the substrate 41 is not particularly limited and can be set appropriately depending on the number of cells 42 arranged on the substrate 41, etc.

[0019] The olfactory receptor can be derived from an animal. Examples of animals include insects, vertebrates, and mammals, and examples of olfactory receptors that can be used include flies, mosquitoes, mice, rats, rabbits, cows, dogs, and humans. Insect olfactory receptors are preferred. Insect olfactory receptors form heterocomplexes with olfactory receptor co-receptors and function as ion channels activated by odor molecules.

[0020] The amino acid sequences and coding sequences of olfactory receptors and olfactory receptor co-receptors are known or can be easily identified by sequence identity searches based on known sequences. Furthermore, amino acid mutations relative to the known amino acid sequences can be included. Amino acid mutations include, for example, amino acid substitutions, insertions, additions, or deletions.

[0021] The cells 42 may be specific cells that naturally express an olfactory receptor, or genetically modified cells into which an olfactory receptor gene has been incorporated. Genetically modified cells can be produced by transforming cells with a vector into which an olfactory receptor gene has been incorporated. When the olfactory receptor is an insect olfactory receptor, it is preferable to further incorporate a gene for an olfactory receptor co-receptor.

[0022] The cell 42 may further contain a fluorescent protein or a luminescent protein. When an odor molecule binds to an ionotropic olfactory receptor in the cell 42, cations such as calcium ions flow into the cell. By introducing into the cell 42 a gene that expresses a fluorescent protein whose fluorescence intensity changes depending on the ion concentration or a luminescent protein whose luminescence intensity changes, the response of the cell to the odor molecule can be detected by changes in fluorescence intensity or luminescence intensity. In other words, it is possible to detect odor molecules by changes in fluorescence intensity or luminescence intensity. Examples of such proteins include aequorin, Yellow Cameleon, and GCaMP.

[0023] A calcium ion-dependent fluorescent dye may be introduced into the cells 42. By introducing the calcium ion-dependent fluorescent dye into the cells, the influx of calcium ions into the cells due to the binding of odor molecules to olfactory receptors can be detected by changes in fluorescence intensity. Examples of such calcium ion-dependent fluorescent dyes include Fura-2, Fluo-3, and Fluo-4.

[0024] The detection device 3 detects a signal indicating a response of the cells 42 based on the binding between the olfactory receptors and odor molecules. The detection device 3 detects, for example, the luminescence intensity of each well 43 in the olfactory sensor 4 as a detection target. In other words, the detection device 3 can detect a response signal for each cell 42 contained in each well 43 and for each type of olfactory receptor possessed by the cell 42. The detection device 3 includes, for example, a photomultiplier tube, and detects fluorescence or luminescence intensity based on changes in intracellular ion concentration. The response signal of the cells 42 detected by the detection device 3 is not limited to fluorescence or luminescence intensity, but may also be an electrical signal (electric potential) based on changes in intracellular ion concentration. The response signal may also be a moving or still image of the luminescence of the cells 42 captured by an imaging device such as a CCD camera.

[0025] As shown in Fig. 2, the olfactory sensor 4 of this embodiment includes a plurality of cells 42 arranged on a substrate 41. In the example shown in Fig. 2, the plurality of cells 42 are arranged in rows and columns at regular intervals on the upper surface of the substrate 41.

[0026] In general, olfactory receptors have selectivity for odor molecules. Therefore, by arranging multiple cells 42 expressing different olfactory receptors on a substrate 41 and detecting the responses of each of these cells 42, it is possible to detect multiple types of odors. A single cell 42 may have one type of olfactory receptor, or multiple types of olfactory receptors. The olfactory sensor 4 may include multiple identical cells 42. The olfactory sensor 4 of this embodiment includes different types of cells 42, each of which has one type of olfactory receptor.

[0027] The number, types, and arrangement of olfactory receptors used in the olfactory sensor 4 can be determined appropriately depending on the odor molecules to be detected and the type of cancer to be diagnosed. The olfactory receptors may be a specific combination for the odor molecules to be detected, or a comprehensive combination of multiple olfactory receptors may be used for various odor molecules. For example, the number of cells 42 mounted on one olfactory sensor 4, i.e., the total number of sensor cells mounted on one olfactory sensor 4, can be from 1 to 50,000, and the number of types of cells 42 mounted on one olfactory sensor 4 can be from 1 to 2,000.

[0028] The method for detecting the response signal is not limited to the above example, and any appropriate method can be used depending on the olfactory receptors in the olfactory sensor 4 and the types of odor molecules sensed by the olfactory receptors.

[0029] 3 is a block diagram showing the configuration of the determination device 1. The determination device 1 includes a control unit 11, a storage unit 12, and a communication unit 13. The determination device 1 may be a single computer, or may be a computer system configured by multiple computers and peripheral devices. The determination device 1 may be a virtual machine whose entity is virtualized, or may be a cloud.

[0030] The control unit 11 includes one or more arithmetic processing devices such as a central processing unit (CPU) or a graphics processing unit (GPU). The control unit 11 controls each component and executes processing using built-in memories such as a read-only memory (ROM) or a random access memory (RAM), a clock, a counter, etc. The functional units of the control unit 11 may be realized by software, or some or all of them may be realized by hardware such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0031] The storage unit 12 includes a nonvolatile memory such as a hard disk, flash memory, or SSD (Solid State Drive). The storage unit 12 may be separate from the determination device 1 and may be one or more external storage devices connected externally. The storage unit 12 stores various computer programs and data referenced by the control unit 11. The storage unit 12 stores a program 1P for causing a computer to execute processing related to determining the possibility of cancer, and a detection DB (Data Base) 121. The storage unit 12 may further store a reference profile, which will be described later.

[0032] A computer program (program product) including program 1P may be provided by a non-transitory recording medium 1A on which the computer program is readably recorded. The storage unit 12 stores the computer program read from the recording medium 1A by a reading device (not shown). The recording medium 1A may be, for example, a magnetic disk, an optical disk, or a semiconductor memory. Alternatively, the computer program may be downloaded from an external server connected to a communications network and stored in the storage unit 12. Program 1P may be a single computer program or may be composed of multiple computer programs. Program 1P may also be executed on a single computer or may be executed cooperatively by multiple computers.

[0033] The communication unit 13 includes a communication device that realizes communication via the network N. The control unit 11 transmits and receives data between the terminal device 2 and the detection device 3 via the communication unit 13.

[0034] The configuration of the determination device 1 is not limited to the above example, and may include, for example, an operation unit for accepting user operations, a display unit for displaying images, and the like.

[0035] The determination device 1 and the detection device 3 are not limited to transmitting and receiving data via the network N. The determination device 1 may, for example, be provided with an input interface for connecting to the detection device 3 and may receive data output from the detection device 3 via a signal line or the like.

[0036] 4 is a diagram showing an example of the contents of information stored in the detection DB 121. The detection DB 121 is a database that stores detection information related to each of a plurality of subjects, subject information related to subjects, and reference profile information related to reference profiles.

[0037] The detection information table stores records that link information such as subject ID, collection date, detection device ID, detection date, detection data, and determination results, using, for example, subject ID as a key. The subject ID is identification information for uniquely identifying the subject collected from the subject. The subject ID may be an ID attached to a container that contains a specific subject. The subject ID is identification information for identifying the subject. The collection date indicates the date and time the subject was collected. The detection device ID is identification information for identifying the detection device 3 used to detect a response signal for the subject. The detection date indicates the date and time the response signal for the subject was detected by the detection device 3.

[0038] The detection data includes information indicating a response signal to the subject. The detection data is, for example, a profile of the response signal detected over time. In this embodiment, the profile of the response signal as detection data is time-dependent data of luminescence intensity. The detection data is generated for each cell 42 in the olfactory sensor 4. The determination result indicates the possibility of cancer based on the detection data. The determination result may be indicated, for example, by the presence or absence of a possibility, or may be indicated by the degree of possibility classified into multiple levels. The information in the detection information table is collected, for example, via the detection device 3.

[0039] The subject information table stores records that link information such as terminal device information and subject attribute information, using, for example, the subject ID as a key. The detection information table and the subject information table are associated by the subject ID. The terminal device information is information that identifies the terminal device 2 used by the subject, and includes, for example, an address indicating the output destination of the determination result, a device ID, etc. The attribute information includes, for example, the subject's name, age, gender, health information, etc. The health information is information related to the subject's health condition, and may include information such as current symptoms, medical history, test results, and health check results. The information in the subject information table is collected, for example, through the terminal device 2.

[0040] The reference profile information table stores records linking information such as cell information, first reference profile information, and second reference profile information using, for example, cancer information as a key. The cancer information is information for identifying the cancer to be diagnosed, and includes, for example, a cancer ID and a cancer name (cancer type). The cell information is information for identifying the cells 42 in the olfactory sensor 4 and represents the cell type selected for cancer diagnosis. The first reference profile information and the second reference profile information are information related to the first reference profile and the second reference profile corresponding to the cell information. Details of the first reference profile and the second reference profile will be described later. Note that FIG. 4 is an example, and the content of the information stored in the detection DB 121 is not limited. Furthermore, the data storage method shown in FIG. 4 is an example, and other storage formats are possible as long as the data content and the relationships between the data are maintained.

[0041] 5 is a block diagram showing the configuration of the terminal device 2. The terminal device 2 includes a control unit 21, a storage unit 22, a communication unit 23, a display unit 24, and an operation unit 25.

[0042] The control unit 21 includes one or more arithmetic processing units such as a CPU, an MPU, a GPU, etc. The control unit 21 controls each component and executes processing using built-in memories such as a ROM or a RAM, a clock, a counter, etc.

[0043] The storage unit 22 includes a non-volatile memory such as a hard disk, flash memory, or SSD. The storage unit 22 stores various computer programs and data referenced by the control unit 21. The storage unit 22 stores a program 2P for causing a computer to execute processing related to obtaining the cancer possibility assessment result.

[0044] A computer program (computer program product) including the program 2P may be provided by a non-transitory recording medium 2A on which the computer program is readably recorded. The storage unit 22 stores the computer program read from the recording medium 2A by a reading device (not shown). The recording medium 2A may be, for example, a magnetic disk, an optical disk, or a semiconductor memory. Alternatively, the computer program may be downloaded from an external server connected to a communications network and stored in the storage unit 22. The program 2P may be a single computer program or may be composed of multiple computer programs. The program 2P may also be executed on a single computer or may be executed cooperatively by multiple computers.

[0045] The communication unit 23 includes a communication device that realizes communication via the network N. The control unit 21 transmits and receives data to and from the determination device 1 via the communication unit 23.

[0046] The display unit 24 includes a display device such as a liquid crystal display, an organic EL (Electro Luminescence) display, etc. The display unit 24 displays various information including the determination results in accordance with instructions from the control unit 21.

[0047] The operation unit 25 is an interface that accepts user operations. The operation unit 25 includes, for example, a keyboard, a mouse, a touch panel device with a built-in display, a speaker, a microphone, etc. The operation unit 25 accepts operation input from the user and sends a control signal according to the operation content to the control unit 21.

[0048] Figure 6 shows an example of the change in luminescence intensity of a sensor cell over time. The vertical axis of the graph in Figure 6 represents luminescence intensity, and the horizontal axis represents time (s). Figure 6 shows the detection results of luminescence intensity for each concentration of odor molecules when a solution containing a specific odor molecule is added to a cell expressing an olfactory receptor that binds to the odor molecule. In Figure 6, the concentrations of odor molecules are referred to in descending order as the first concentration, the second concentration, and the third concentration.

[0049] As shown in Figure 6, the profile showing the change in luminescence intensity over time is concentration-dependent. In this embodiment, the possibility of cancer in a subject is determined by estimating the characteristics of a specific group of odor molecules in urine that can be detected by the sensor cell based on the response profile of the luminescence intensity detected from the subject's urine sample.

[0050] The flow of the determination process in the determination system 100 of this embodiment will be described below with a specific example.

[0051] The determination device 1 accepts an application for the determination service from a subject. A user (subject) who wishes to use the service applies for the service using, for example, the terminal device 2 via a reception screen 50 provided by the determination device 1.

[0052] 7 is a schematic diagram showing an example of a reception screen 50. When the determination device 1 receives a request for login using account information and a reception screen by an operation of the subject via the terminal device 2, the determination device 1 outputs the reception screen 50 as shown in FIG.

[0053] The reception screen 50 includes a subject information reception field 501 for receiving input of subject information regarding the subject to be assessed, and a cancer information reception field 502 for receiving input of the target cancer for which assessment is desired. The subject uses the operation unit 25 to input the name, etc. of the subject to be assessed in the subject information reception field 501. Note that in the case of a subject who has been registered as a user in advance, the determination device 1 may refer to the detection DB 121 to read out subject information corresponding to the logged-in account information, and display the read out subject information in the subject information reception field 501.

[0054] The cancer information reception field 502 displays a plurality of selectable cancers that can be diagnosed by the diagnosis system 100. The subject can input the designation of one or more cancers for which they wish to be diagnosed, for example, by selecting a check box associated with each cancer. In the example shown in Fig. 7, lung cancer is selected as the target cancer.

[0055] The assessment device 1 calculates the usage fee for the assessment service according to the total number of target cancers selected as the desired assessment, so that the larger the total number, the higher the fee. The usage fee may vary depending on the type of target cancer. The assessment device 1 displays the calculated usage fee at the bottom of the cancer information reception column 502.

[0056] When an application button 503 specifying an application for diagnosis is selected with various information entered in the subject information reception field 501 and the cancer information reception field 502, the terminal device 2 transmits the received subject information and target cancer to the determination device 1. The determination device 1 receives the subject information and target cancer and accepts the application for diagnosis. The determination device 1 stores the received subject information and target cancer in the memory unit 12, and, if necessary, transmits the subject information and information corresponding to the target cancer to the detection device 3 of the analytical institution that will perform the test.

[0057] The reception screen 50 may also include a payment method reception field 504 as shown in Fig. 7. The subject can specify the desired payment method by selecting a specific payment method from the payment method reception field 504. The terminal device 2 may execute processing for online payment or credit card payment with a predetermined payment server or the like, depending on the accepted payment method.

[0058] Note that the information related to the application for the assessment service is not limited to that received through the terminal device 2, and the assessment device 1 may acquire subject information, etc., by receiving input from the user, for example.

[0059] Once the application is completed, a urine collection container is sent to the subject. The urine collection container may be distributed through a predetermined store, analytical institution, etc. A label with, for example, a two-dimensional or three-dimensional code representing the subject's ID is attached to the urine collection container. The subject places the collected urine in the urine collection container and submits it to the analytical institution. The urine collection container may be submitted together with the subject's attribute information, such as the subject's name, the collection date, the application number issued when the test was applied for, etc.

[0060] The urine collection container is received at the analysis facility. The detection device 3 receives input from, for example, a person in charge, and thereby acquires the subject ID, subject ID, collection date, etc. of the subject to be tested.

[0061] Next, the analytical laboratory detects the response signal using the olfactory sensor 4. A predetermined number of cells 42, each expressing a different olfactory receptor, are arranged in an array in the olfactory sensor 4. The olfactory sensor 4 is produced, for example, by integrating DNA containing a specific insect olfactory receptor coding sequence, a specific insect olfactory receptor co-receptor coding sequence, and a calcium-sensitive photoprotein coding sequence, all of which are placed under the control of a promoter sequence, into the chromosomal genomic DNA of each cell.

[0062] A predetermined amount of a test sample (e.g., a urine sample from a test subject) is brought into contact with the olfactory sensor 4, and the luminescence intensity of each cell is detected over time by the detection device 3. This allows a response profile indicating the change in luminescence intensity over time to be acquired. The response profile is generated for each cell 42 in the olfactory sensor 4. The detection device 3 associates the acquired response profile with the test subject ID, test subject ID, collection date, detection device ID, detection date, etc. of the test subject to be tested, and transmits the response profile to the determination device 1.

[0063] The determination device 1 may generate a response profile by performing predetermined preprocessing on the detection data received from the detection device 3. Measurement of the luminescence intensity of the olfactory sensor 4 may be performed collectively on multiple arrayed sensor cells. In such cases, the raw detection data output from the detection device 3 is expected to include luminescence intensities at various times (detection times) for multiple sensor cells. Alternatively, the detection data may contain a mixture of detection values ​​from multiple urine samples derived from subjects. The determination device 1 generates a response profile by sorting the raw detection data including multiple detection values ​​for each sensor cell or each urine sample derived from the subject, arranging the luminescence intensities over time, and converting them into a predetermined data format. If the luminescence intensities in the raw detection data vary in time, the determination device 1 may unify the times of each data in the response profile by interpolating the data using a predetermined interpolation method.

[0064] The determination device 1 determines the possibility of cancer in a subject based on the response profile of the obtained urine sample from the subject. In this embodiment, the possibility of cancer is determined by comparing the response profile of the urine sample from the subject to be analyzed with a reference profile generated in advance.

[0065] The reference profile is a response profile that indicates a reference for the response signal of the sensor cell. The reference profile includes a first reference profile based on the luminescence intensity detected from a urine sample derived from a subject in good health, and a second reference profile based on the luminescence intensity detected from a urine sample derived from a subject in poor health. As an example, if the target cancer is lung cancer, a subject in good health refers to a non-cancer patient who is not suffering from cancer, and a subject in poor health refers to a lung cancer patient who is suffering from lung cancer. The reference profile is, for example, generated in advance and stored in the memory unit 12.

[0066] The first and second reference profiles are obtained by detecting the above-described odor molecules using the olfactory sensor 4 using urine collected from a non-cancer patient (hereinafter also referred to as healthy urine) and urine collected from a lung cancer patient (hereinafter also referred to as lung cancer urine) to generate a response profile. The first and second reference profiles are prepared for each cell 42.

[0067] The first reference profile is preferably generated based on the response profiles of multiple healthy urine samples obtained from multiple non-cancer patients. Similarly, the second reference profile is preferably generated based on the response profiles of multiple lung cancer urine samples obtained from multiple lung cancer patients. For example, statistical values ​​of the luminescence intensity detected from each healthy urine sample are calculated for each time period, and the first reference profile is generated based on the obtained statistical values. As the statistical value, the mean or median is preferred, with the weighted mean, geometric mean, or median being more preferred, and the geometric mean being most preferred. Using a similar method, the second reference profile is generated based on the response profiles of multiple lung cancer urine samples obtained from multiple lung cancer patients.

[0068] Furthermore, one or more cells 42 suitable for diagnosing lung cancer, which is the cancer to be diagnosed, are selected from the multiple types of cells 42 contained in the olfactory sensor 4. Cells suitable for diagnosing lung cancer are cells that exhibit reactivity according to lung cancer urine. Cells suitable for diagnosing lung cancer are preferably cells whose reactivity changes significantly depending on whether or not the subject is afflicted with lung cancer, and whose response profile shows a significant difference. Cells suitable for diagnosing lung cancer may be cells that react to odor molecules that are contained in relatively higher amounts in lung cancer urine than in healthy urine, or may be cells that react to odor molecules that are present in lower amounts in lung cancer urine than in healthy urine. In this embodiment, cells 42 that react to odor molecules that are contained in higher amounts in lung cancer urine than in healthy urine are considered to be cells 42 suitable for diagnosing lung cancer.

[0069] It is not easy to identify in advance the cells 42 that are suitable for diagnosing the cancer that is the target of diagnosis at the stage of generating the olfactory sensor 4. For this reason, in this embodiment, an olfactory sensor 4 equipped with multiple types of cells 42 is prepared, and the cells 42 that are suitable for diagnosing cancer are identified from the multiple types of cells 42 for each cancer that is the target of diagnosis.

[0070] The method for identifying cells 42 suitable for cancer diagnosis is not limited, but for example, cells 42 can be identified based on the dissimilarity between the first reference profile and the second reference profile. The determination device 1 may calculate the dissimilarity between the first reference profile and the second reference profile, and identify cells 42 for which the calculated dissimilarity is equal to or greater than a predetermined first threshold as cells 42 for cancer diagnosis.

[0071] The dissimilarity between the first and second reference profiles may be, for example, the difference between the maximum luminescence intensity in the first and second reference profiles. The area enclosed by the first and second reference profiles (the integral of the intensity difference obtained by integrating the intensity difference between the first and second reference profiles) may also be used as the dissimilarity. The larger the difference between the maximum intensities or the integral of the intensity difference between the reference profiles, the lower the similarity between the first and second reference profiles, and the greater the change in reactivity depending on whether or not there is a possibility of cancer. In other words, the difference in response of cells 42 depending on whether or not there is a possibility of cancer is large, and this is considered to be highly useful in cancer diagnosis. While the dissimilarity between the first and second reference profiles is used in the above, similarity may also be used.

[0072] The selection of cells 42 for cancer diagnosis may be performed taking into account the accuracy of cancer diagnosis using the cells 42. For example, response profiles for multiple test samples are obtained for multiple cells 42 initially selected for cancer diagnosis, and the acquired response profiles are used to determine the possibility of cancer according to the diagnosis method described below. The diagnosis accuracy when using each of the initially selected cells 42 is calculated based on the diagnosis results for each cell 42 and the known characteristics of the test sample (healthy urine or lung cancer urine). The diagnosis accuracy includes, for example, the AUC, recall, specificity, accuracy, and precision of the ROC curve. Based on the calculated values ​​of diagnosis accuracy, the type and number of cells 42 to be selected for cancer diagnosis are specified so as to optimize each diagnosis accuracy (e.g., so as to maximize each diagnosis accuracy). Cells 42 for cancer diagnosis are then secondarily selected in accordance with the type and number of the specified cells 42.

[0073] 8A and 8B are diagrams showing examples of a reference profile for cells selected for cancer assessment and a reference profile for cells not selected for cancer assessment. FIG. 8A shows an example of a reference profile for cells selected for cancer assessment, and FIG. 8B shows an example of a reference profile for cells not selected for cancer assessment. In the case of cells for cancer assessment, the shapes of the response profiles are significantly different between healthy urine and pseudo-lung cancer urine (urine in which odor molecules derived from lung cancer have been added to healthy urine). On the other hand, in the case of cells not for cancer assessment, no significant difference is observed in the response profiles between healthy urine and pseudo-lung cancer urine.

[0074] In addition, if the type of cells 42 suitable for determining a specific cancer is known, the step of selecting cells 42 for cancer determination may be omitted.

[0075] The determination device 1 determines the possibility of lung cancer by determining the similarity between the response profile of the subject's urine sample corresponding to the selected cancer determination cell 42 and each of the first and second reference profiles corresponding to the cancer determination cell 42. If the similarity to the first reference profile is higher than the similarity to the second reference profile, it is determined that there is no possibility of lung cancer. If the similarity to the second reference profile is higher than the similarity to the first reference profile, it is determined that there is a possibility of lung cancer. The determination device 1 may determine the possibility of lung cancer in multiple stages, as a percentage, or the like, depending on the similarity value.

[0076] The method for determining the degree of similarity is not limited, but for example, the absolute value of the difference between the luminescence intensity in the response profile of the subject's urine sample and the luminescence intensity in the reference profile may be calculated for each elapsed time, and the sum of the absolute values ​​of the calculated differences may be used as the index for determining the degree of similarity. A smaller sum of the absolute values ​​of the luminescence intensity differences indicates a higher degree of similarity.

[0077] When multiple cells 42 for cancer assessment are selected, the assessment device 1 individually assesses the possibility of lung cancer based on each of the cells 42 for cancer assessment and integrates the individually assessed lung cancer possibilities to make an overall assessment of the possibility of lung cancer. The assessment device 1, for example, determines the overall assessment by a majority vote of the assessment results based on each cell 42. When making the overall assessment, weighting may be performed so that the weight of the assessment result based on a specific cell 42 is increased. The cells 42 to be weighted may be cells 42 that are more useful in cancer assessment, cells 42 that have a higher degree of similarity, cells 42 that have smaller variance in response signals, etc.

[0078] The possibility of lung cancer may be determined using a machine learning technique. The determination device 1 has prepared in advance a determination model that outputs the possibility of lung cancer when the similarity between the response profile of each cell 42 for cancer determination and a reference profile is input. The similarity includes at least one of the similarity between the response profile of the subject's urine sample and a first reference profile and the similarity between the response profile and a second reference profile.

[0079] The determination device 1 inputs the similarity between the response profiles of each cell 42 for cancer determination into a determination model, and obtains the possibility of lung cancer output from the determination model. The type of cell 42 may be input to the determination model along with the similarity. The determination result of the possibility of lung cancer output from the determination model corresponds to the comprehensive determination result. In this case, individual determination based on each cell 42 may be omitted.

[0080] If the type of cells 42 suitable for determining a specific cancer is known, an olfactory sensor 4 may be individually generated for each type of cancer to be determined using only the cells 42 suitable for cancer determination. In this case, luminescence intensity is detected using an individual olfactory sensor 4 corresponding to the selected target cancer, and the possibility of cancer is determined based on the detection results of each cell 42 in the individual olfactory sensor 4. The determination device 1 associates a series of obtained information, such as the subject ID, subject ID, collection date, detection device ID, detection date, response profile, and determination result, and stores them in the detection DB 121. The determination device 1 also outputs the determination result to the subject's terminal device 2.

[0081] 9 is a schematic diagram showing an example of a result screen 51 showing the determination result. The result screen 51 includes a first display section 511 that displays information about the subject to be determined, and a second display section 512 that displays the determination result.

[0082] The determination device 1 displays information about the subject and the specimen to be determined on the first display unit 511 based on the information stored in the detection DB 121. The first display unit 511 displays, for example, the subject's name, the collection date of the specimen, and the specimen ID.

[0083] The determination device 1 also displays the determination result indicating the presence or absence of the possibility of cancer on the second display unit 512. When determination results for multiple types of cancer are obtained, the result screen 51 may be configured to include multiple second display units 512 corresponding to the respective target cancers. Each second display unit 512 displays the target cancer and the determination result related to that target cancer.

[0084] Fig. 10 is a schematic diagram showing another example of the result screen 51 showing the determination result. In the example shown in Fig. 10, the second display section 512 of the result screen 51 further includes a detection result display field 513 that displays the detection result for the analyte detected by the olfactory sensor 4. The detection result display field 513 displays a response profile corresponding to the analyte.

[0085] In addition to the response profile corresponding to the subject, the criteria used in the cancer assessment process, i.e., the first and second reference profiles, may be displayed in the detection result display field 513. For ease of explanation, only one type of response profile is shown in Fig. 10, but the detection result display field 513 may also display multiple response profiles indicating the detection results for each cell 42 used for cancer assessment. The detection result display field 513 may also display an image showing the luminescence of the cells 42 in the olfactory sensor 4, captured by an imaging device.

[0086] When displaying response profiles for multiple cells 42 in the detection result display field 513, the determination device 1 may determine the display order on the screen based on the usefulness in cancer determination or the similarity between each response profile and a reference profile. The determination device 1 may, for example, preferentially display the detection results of cells 42 with high usefulness or similarity in the detection result display field 513. The determination device 1 may also display the detection results for a predetermined number of cells 42 in the detection result display field 513 in descending order of priority. The determination device 1 may determine the display order based on the contribution of input information in the above-described determination model, such that cells 42 with a higher contribution of similarity are displayed preferentially. The contribution can be calculated based on, for example, a SHAP (Shapeley Additive exPlanation) value, a Gini coefficient, a LIME (Local Interpretable Model-Agnostic Explanations), a PFI (Permutation Feature Importance), or the like.

[0087] The determination device 1 may display time-series changes in the determination results on a result screen 51 shown in Figures 9 and 10. The determination device 1 reads out determination results and response profiles relating to collection dates within a fixed period from the detection information associated with the subject ID of the subject to be displayed, based on the information stored in the detection DB 121. The determination device 1 generates a list or graph showing the determination results and response profiles corresponding to the read collection dates, and displays them on the result screen 51.

[0088] The detection result is not limited to being presented to the subject via the terminal device 2. The determination device 1 may output the detection result to, for example, another computer or a predetermined printing device.

[0089] 11 is a flowchart showing an example of a reference profile generation process. The process in the flowchart below is executed by the control unit 11 in accordance with a program 1P stored in the storage unit 12 of the determination device 1.

[0090] The control unit 11 of the determination device 1 acquires, via the detection device 3, response profiles detected from healthy urine samples from multiple non-cancer patients and response profiles detected from lung cancer urine samples from multiple lung cancer patients (step S11). The response profiles are, for example, time-course data of luminescence intensity, and are generated for each cell 42 in the olfactory sensor 4. Each response profile may be linked to information indicating the corresponding cell 42.

[0091] The control unit 11 generates a first reference profile based on each response profile for the plurality of normal urine samples (step S12). For example, the control unit 11 generates the first reference profile by calculating the geometric mean of the luminescence intensity detected from each normal urine sample for each time period.

[0092] The control unit 11 generates a second reference profile based on each response profile for the plurality of lung cancer urine samples (step S13). The control unit 11 generates the second reference profile by, for example, calculating the geometric mean of the luminescence intensity detected from each lung cancer urine sample for each time period.

[0093] The control unit 11 selects one or more cells 42 to be used for lung cancer diagnosis from the multiple types of cells 42 included in the olfactory sensor 4 (step S14). The control unit 11, for example, calculates the maximum luminescence intensity or the intensity difference integral value in each of the first and second reference profiles for each cell 42 included in the olfactory sensor 4. The control unit 11 selects cells 42 for which the calculated difference in the maximum luminescence intensity or the intensity difference integral value is equal to or greater than a predetermined first threshold as cells 42 for lung cancer diagnosis. In step S14, the control unit 11 may identify the type and number of cells 42 to be selected for cancer diagnosis based on the accuracy of cancer possibility diagnosis when each cell 42 is used, so as to optimize each diagnosis accuracy, and select cells 42 for lung cancer diagnosis according to the identification results.

[0094] The control unit 11 associates the cancer information, the cell information of the selected cell 42 for lung cancer diagnosis, and the first and second reference profiles corresponding to the cell 42 and stores them in the detection DB 121 (step S15), and then completes the series of processes.

[0095] The control unit 11 executes the above-described process for all cancers that can be subject to assessment in the assessment service to generate and store first and second reference profiles for various cancer assessments. The above-described process may be executed prior to the operational stage in which the assessment service is performed.

[0096] 12 is a flowchart showing an example of a procedure for determining the possibility of cancer. The following process is executed by the control unit 11 in accordance with a program 1P stored in the storage unit 12 of the determination device 1, and by the control unit 21 in accordance with a program 2P stored in the storage unit 22 of the terminal device 2.

[0097] The control unit 21 of the terminal device 2 receives the subject information and the target cancer (cancer type) of the subject who wishes to be diagnosed based on the subject's operation using the reception screen (step S20). The control unit 21 transmits the received subject information and the target cancer to the assessment device 1 (step S21).

[0098] The control unit 11 of the determination device 1 receives the subject information and the target cancer (step S22).

[0099] The control unit 11 acquires a response profile generated from a urine sample derived from a subject through the detection device 3 (step S23). A response profile is generated for each cell 42 in the olfactory sensor 4. The response profile is associated with a subject ID, subject ID, collection date, detection device ID, detection date, etc. The control unit 11 may generate a response profile by acquiring raw detection data obtained by detection from the detection device 3 and performing various preprocessing operations on the acquired raw detection data. The control unit 11 associates the subject ID, subject ID, collection date, detection device ID, detection date, and response profile for each cell type with each other and stores them in the detection DB 121 (step S24).

[0100] The control unit 11 calculates the degree of similarity between the response profile of the urine sample derived from the subject and each of the first and second reference profiles for each cell 42 for cancer determination according to the target cancer (step S25).

[0101] The control unit 11 compares the calculated similarities and identifies the health state corresponding to the reference profile with the highest similarity as the health state of the subject, thereby individually determining whether or not the subject has a possibility of cancer (step S26). In step S26, the possibility of cancer is determined for each cell 42. The control unit 11 then makes an overall determination of the possibility of cancer, for example, by a majority vote of the individual determinations for each cell 42 (step S27). The control unit 11 stores the obtained determination result in the detection DB 121 in association with the subject ID (step S28).

[0102] The control unit 11 generates a result screen showing the obtained cancer possibility assessment result (step S29). The control unit 11 transmits the generated result screen to the terminal device 2 corresponding to the subject identified by the subject information acquired in step S23 (step S30).

[0103] The control unit 21 of the terminal device 2 receives the result screen from the determination device 1 (step S31). The control unit 21 displays the received result screen on the display unit 24 (step S32), and ends the series of processes.

[0104] In the above-described process, the subject may request the display of a result screen using the terminal device 2 and receive the result screen in response to the request, thereby being able to check the determination result at any time. The determination result may be provided through a web service provided by the determination device 1.

[0105] In the above-described process, the control unit 11 of the determination device 1 may update the reference profile based on the response profile and determination result newly stored in the detection DB 121 by the process of step S28. The control unit 11 extracts one or more newly added response profiles from the detection DB 121, for example, at appropriate intervals. The control unit 11 regenerates a first reference profile based on the extracted response profiles, to which a response profile determined to be unlikely to be cancer has been newly added. Alternatively, the control unit 11 regenerates a second reference profile based on the extracted response profiles, to which a response profile determined to be likely to be cancer has been newly added.

[0106] In the above, the possibility of cancer is determined based on detection data for a urine sample using an olfactory sensor 4 having olfactory receptors, but the receptors used to detect the response signal are not limited to olfactory receptors. The analyte to be analyzed is not limited to urine, and may be, for example, blood, sweat, saliva, tears, exhaled breath, skin gas, tissue fluid, synovial fluid, follicular fluid, cerebrospinal fluid, semen, milk, vaginal fluid, etc. Furthermore, the subject to be determined for the possibility of cancer is not limited to humans, but may also be an animal.

[0107] According to this embodiment, the possibility of cancer can be determined based on detection data obtained by a biosensor equipped with a receptor. The practicality of the biosensor can be improved, and health management services using the biosensor can be provided. By using a response profile that indicates the response of the olfactory receptors, the possibility of cancer can be determined with high accuracy. By determining the possibility of cancer by comparing with a pre-generated reference profile, the process of determining the possibility of cancer becomes easier.

[0108] The subject can obtain the assessment result by submitting the subject's sample and registering the necessary information, thereby reducing the burden required for testing and increasing the utilization of the service. The assessment result can be confirmed using the terminal device 2, so the assessment result can be reliably understood at any time. In addition to the assessment result, the detection result can be displayed in a visually recognizable manner, allowing the results to be confirmed more reliably and in detail. In addition to the subject's own detection result, the reference profile that serves as the assessment standard can be displayed, improving the explainability of the assessment result.

[0109] Second Embodiment In the second embodiment, correction is performed to eliminate individual differences in the response profile. In the following embodiments, differences from the first embodiment will be mainly described, and the same reference numerals will be used to designate components common to the first embodiment, and detailed descriptions thereof will be omitted.

[0110] The response signal detected from the test subject may vary from individual to individual due to various factors. For example, due to the influence of impurities contained in the urine sample, even if the urine sample contains the same concentration of odor compounds, the luminescence intensity detected from a urine sample containing a large amount of impurities may be greater or smaller than that detected from a urine sample containing fewer impurities. In other words, due to the influence of impurities, individual differences occur in the correlation between the concentration of odor molecules and the luminescence intensity. The response signal may also vary from individual to individual due to the influence of the balance of odor molecules. Individual differences may occur not only in luminescence intensity but also in various response signals.

[0111] When performing a determination based on a response profile, the occurrence of individual differences as described above leads to a decrease in determination accuracy. In particular, as described in the first embodiment, when determining whether a urine sample resembles a profile of healthy urine or lung cancer urine by comparing a target response profile with a reference profile, the possibility of erroneous determination due to the influence of individual differences increases. In this embodiment, a correction process is performed to eliminate individual differences in the response profile, thereby improving determination accuracy.

[0112] 13 is a flowchart showing an example of a processing procedure executed by the determination device 1 of the second embodiment. The processing of FIG. 13 is executed, for example, between step S24 and step S25 of the first embodiment.

[0113] The control unit 11 of the determination device 1 selects one or more cells 42 to be used for correcting individual differences from the multiple types of cells 42 contained in the olfactory sensor 4 (step S41). Cells 42 for correcting individual differences are preferably cells 42 whose reactivity to a target cancer (e.g., lung cancer) is lower than that of cells for cancer determination. More preferably, cells for correcting individual differences are cells whose reactivity does not change significantly depending on whether or not a subject has lung cancer, and whose response profile does not show a significant difference. Cells for correcting individual differences may be cells that respond to odor molecules present at approximately the same concentrations in both healthy urine and lung cancer urine, or cells that respond to odor molecules artificially added to urine that are not present in the urine prior to the addition.

[0114] The control unit 11 selects cells 42 for individual variability correction based on, for example, the dissimilarity between a first reference profile based on non-cancer urine (healthy urine) and a second reference profile based on lung cancer urine. Specifically, the control unit 11 calculates the dissimilarity between the first reference profile and the second reference profile for each cell 42 included in the olfactory sensor 4. The control unit 11 selects cells 42 for which the calculated dissimilarity is less than a predetermined second threshold as cells 42 for individual variability correction. The dissimilarity between the first reference profile and the second reference profile may be, for example, the difference between the maximum luminescence intensity in the first reference profile and the maximum luminescence intensity in the second reference profile, or the integral value of the intensity difference between the first reference profile and the second reference profile, as in the case of identifying cells 42 for cancer diagnosis. Note that although the dissimilarity between the first reference profile and the second reference profile is used in the above, similarity may also be used.

[0115] Preferably, the first and second reference profiles are each generated based on response profiles of multiple urine samples obtained from multiple individuals. The second threshold value may be the same as or smaller than the first threshold value used to select the cells 42 for cancer diagnosis. The cells 42 for correcting individual differences may be selected from among the cells 42 in the olfactory sensor 4, other than the cells 42 for cancer diagnosis. If the type of the cells 42 for correcting individual differences is known, the selection process described above may be omitted.

[0116] The control unit 11 acquires a correction profile corresponding to the selected correction cell 42 (step S42). The correction profile may be a response profile generated based on statistical values ​​of the first and second reference profiles. If the maximum value of the luminescence intensity or the integral value of the intensity difference between the first and second reference profiles is approximately zero, either the first or second reference profile may be used as the correction profile. A correction profile is acquired for each correction cell 42.

[0117] The control unit 11 calculates a correction coefficient (correction value) for correcting the response profile based on the acquired correction profile and the response profile of the subject's urine sample using the correction cells 42 (step S43).

[0118] The correction coefficient can be calculated, for example, by the following method. The area enclosed by the response profile and correction profile of the subject's urine sample from the correction cells 42 and the x-axis (time axis) is divided into sections at predetermined time intervals. For each section, the ratio between the area of ​​the first region enclosed by the response profile and the area of ​​the second region enclosed by the correction profile is calculated. The geometric mean value of the ratios in all sections is used as the correction coefficient. Note that the method for calculating the correction coefficient is not limited to the above example, and any method may be used as long as it is capable of correcting individual differences in the response profile. The correction coefficient may be adjusted depending on the type of the determination cells 42. For example, the correction coefficient for each determination cell 42 may be calculated by multiplying the above correction coefficient calculated based on the correction profile and the response profile by a predetermined coefficient that is set in advance for each determination cell 42.

[0119] When multiple cells 42 are selected as correction cells 42, the control unit 11 may calculate the above-mentioned correction coefficient for each correction cell 42 and determine the final correction coefficient by obtaining the statistical value (e.g., geometric mean, median, etc.) of each calculated correction coefficient.

[0120] The control unit 11 corrects the response profile of each cell 42 selected for cancer assessment using the calculated correction coefficient (step S44). Specifically, the control unit 11 multiplies each luminescence intensity in the response profile for cancer assessment by the correction coefficient to generate a corrected response profile. The control unit 11 uses the corrected response profile to execute the processes from step S25 onward, thereby determining the possibility of cancer based on the corrected response profile.

[0121] According to the above-described process, a correction coefficient is calculated based on the response profile and reference profile of the correction cells 42 selected from the cells 42 in the olfactory sensor 4, and the response profile of the cancer assessment cells 42 can be corrected using the calculated correction coefficient. The correction cells 42 do not exhibit different response signal properties depending on whether or not there is a possibility of cancer, and the response profile is largely independent of whether or not there is a possibility of cancer. The cancer assessment cells 42 exhibit different response signal properties depending on whether or not there is a possibility of cancer, and the response profile is strongly dependent on whether or not there is a possibility of cancer. It is expected that the effect of error factors on the luminescence intensity in the same urine sample will occur in the same way in multiple cells 42. It is predicted that the luminescence intensities of the cancer assessment cells 42 and the correction cells 42 for the same urine sample will uniformly increase or decrease due to the influence of error factors. By using the response profile of the correction cells 42, the effect of factors causing individual differences included in the response profile of the cancer assessment cells 42 can be eliminated.

[0122] Figure 14 shows examples of response profiles with and without correction processing. The vertical axis of the graph shown in Figure 14 represents luminescence intensity, and the horizontal axis represents the elapsed time (s) from the start of measurement. In Figure 14, open triangles (△) represent the response profile of disease-determining cells for a urine specimen affected by impurities before correction, and closed triangles (▲) represent the response profile of the disease-determining cells for the same urine specimen after correction. Also in Figure 14, open squares (□) represent the first reference profile generated based on the response profiles obtained from multiple healthy urine specimens, and open circles (○) represent the second reference profile generated based on the response profiles obtained from multiple lung cancer-suspected urine specimens. The corrected response profile was created by correcting the pre-correction response profile using a correction coefficient calculated based on the deviation between the response profile of the correction cells and the reference profile indicating the response standard. Figure 14A shows an example of a response profile for a lung cancer-suspected urine specimen containing impurities that weaken activity. In the example shown in Figure 14A, the response profile before correction shows a decrease in overall luminescence intensity due to the influence of impurities that weaken activity. The response profile after correction has been corrected to increase the luminescence intensity. The correction shown in Figure 14A can reduce the possibility of erroneously determining that a subject with a possible cancer does not have the possibility of cancer.

[0123] 14B shows an example of a response profile for a healthy urine sample containing contaminants that enhance activity. In the example shown in FIG. 14B, the response profile before correction shows a high overall luminescence intensity due to the influence of the contaminants that enhance activity. The response profile after correction has been corrected to have a low luminescence intensity. The correction shown in FIG. 14B can reduce the possibility of erroneously determining that a subject with no possibility of cancer is likely to have cancer.

[0124] According to this embodiment, it is possible to correct for individual differences between subjects and to suppress a decrease in determination accuracy due to individual differences. When measuring a biological sample using a biosensor that uses a receptor, it is thought that differences in response signals may occur due to various factors. According to this embodiment, it is possible to appropriately eliminate such individual differences.

[0125] According to this embodiment, cells for individual correction can be efficiently determined based on the characteristics of the response profile. By preparing a biosensor with multiple sensor cells and acquiring the response profile of each cell, appropriate cells for individual correction can be selected according to the various properties to be evaluated.

[0126] Third Embodiment In a third embodiment, a learning model is used to determine the possibility of cancer.

[0127] 15 is a block diagram showing an example of the configuration of a determination device 1 according to the third embodiment. The determination device 1 according to the third embodiment stores a learning model 122 in the storage unit 12. The learning model 122 is a machine learning model that has learned predetermined training data. The learning model 122 is expected to be used as a program module that constitutes part of artificial intelligence software.

[0128] 16 is an explanatory diagram showing an overview of the learning model 122. The learning model 122 receives a response profile of luminescence intensity detected from a urine sample of a subject as input, and outputs information indicating whether or not the subject has a target cancer corresponding to the response profile. The learning model 122 of this embodiment is composed of a plurality of individual learning models 123. In the following description, the individual learning models 123 are also referred to as a first learning model 123A, a second learning model 123B, and a third learning model 123C.

[0129] The first learning model 123A is a model for determining the possibility of lung cancer as a first cancer, and receives a response profile relating to a plurality of cells 42 as input and outputs the presence or absence of the possibility of lung cancer. The second learning model 123B is a model for determining the possibility of prostate cancer as a second cancer, and receives a response profile relating to a plurality of cells 42 as input and outputs the presence or absence of the possibility of prostate cancer. The third learning model 123C is a model for determining the possibility of colon cancer as a third cancer, and receives a response profile relating to a plurality of cells 42 as input and outputs the presence or absence of the possibility of colon cancer. In this way, the individual learning models 123 are constructed to correspond to each cancer to be determined. Note that the number of individual learning models 123 included in the learning model 122 may be four or more. Since all the individual learning models 123 have the same configuration, the configuration of the first learning model 123A will be described below.

[0130] The first learning model 123A is, for example, a convolutional neural network (CNN), which is a type of neural network. The first learning model 123A includes an input layer to which each response profile is input, an output layer that outputs the presence or absence of the possibility of lung cancer, and an intermediate layer (hidden layer). The intermediate layer may include a convolutional layer, a pooling layer, a fully connected layer, etc. The intermediate layer has multiple nodes that extract features of the response profile and passes the extracted features using various parameters to the output layer. When a response profile is input to the input layer, calculations are performed in the intermediate layer using the learned parameters, and output information indicating the classification result of the presence or absence of the possibility of lung cancer is output from the output layer.

[0131] The input data input to the first learning model 123A is a response profile of the cells 42 selected for lung cancer diagnosis from among all the cells 42 in the olfactory sensor 4. The input data of the first learning model 123A may include the type of cell 42 corresponding to the response profile.

[0132] The first learning model 123A can be generated by preparing training data in which labels indicating the possibility of lung cancer are associated with response profiles, and using the training data to train an untrained neural network. For example, a diagnosis by an experienced physician is used as the correct label. The training data includes response profiles detected from multiple subjects who are likely to have lung cancer, and response profiles detected from multiple subjects who are not likely to have lung cancer. The learning model 122 learns the relationship between these response profiles and the possibility of lung cancer.

[0133] The determination device 1 inputs multiple response profiles contained in training data into the input layer of a pre-learning neural network model, undergoes arithmetic processing in the intermediate layer, and obtains the presence or absence of a possibility of lung cancer output from the output layer. The determination device 1 compares the presence or absence of a possibility of lung cancer output from the output layer with the presence or absence of a possibility of lung cancer included in the training data, and optimizes parameters such as the weights between neurons using, for example, backpropagation so that the presence or absence of a possibility of lung cancer output from the output layer approaches a correct value. Note that the learning model 122 may be constructed by an external device and deployed to the determination device 1.

[0134] The judgment device 1 provides a response profile of cells 42 for cancer judgment corresponding to the target cancer to each individual learning model 123 corresponding to one or more cancers selected as the judgment target, and obtains the possibility of the target cancer output from each individual learning model 123.

[0135] The input data to the individual learning model 123 is not limited to an image representing a response profile of the emission intensity, but may also be a value of the emission intensity over time. Of course, the input to the individual learning model 123 may also be a response signal other than the emission intensity.

[0136] The input data input to the individual learning model 123 may further include subject information about the subject (i.e., subject information about the target) as shown in Fig. 16. The subject information that serves as an input element to the individual learning model 123 includes, for example, attributes such as age and gender of the subject corresponding to the subject, and health information such as current symptoms, medical history, test results, and health check results.

[0137] The individual learning model 123 is not limited to estimating whether there is a possibility of cancer or not, but may output, for example, a probability level classified into multiple categories according to the degree of probability, or may output a numerical value indicating the probability as a percentage.

[0138] The individualized learning model 123 may be configured to receive response profiles for samples collected from the subject on multiple collection dates, i.e., time-series response profiles, as input, and output the possibility of cancer. In this case, the individualized learning model 123 may receive the most recent response profile and the response profiles from the past few times, and output the current possibility of cancer in the subject, or the possibility of cancer in the future.

[0139] The configuration of the learning model 122 is not limited to the above example, and may be any model that can identify the possibility of a target cancer based on the time-series data of the response signal. The learning model 122 may be a model constructed using other learning algorithms, such as a recurrent neural network (RNN), a graph neural network (GNN), a transformer, a support vector machine (SVM), logistic regression, or eXtreme Gradient Boosting (XGBoost).

[0140] As the individual learning model 123, one model may be constructed for each type of cell 42. In this case, the determination device 1 may derive an overall determination result for one cancer based on the individual determination results output from each individual learning model 123 corresponding to the cell type. The determination device 1 may make an overall determination of the possibility of cancer using a determination model, as in the first embodiment. In this case, the determination model may be configured to input the possibility of cancer for each type of cell 42 output from the learning model 122 and output the possibility of cancer. Note that the learning model 122 may be configured to output the possibility of various cancers using a single learning model 122.

[0141] FIG. 17 is a flowchart showing an example of a procedure for determining the possibility of cancer, which is executed by the determination system 100 according to the third embodiment.

[0142] The control unit 21 of the terminal device 2 executes the same processes as steps S20 to S21, receives the subject information and the target cancer (step S50), and transmits them (step S51).

[0143] The control unit 11 of the judgment device 1 performs processing similar to steps S22 to S24, receives subject information and target cancer (step S52), acquires a response profile (step S53), and stores the acquired information in the detection DB 121 (step S54).

[0144] The control unit 11 selects an individual learning model 123 corresponding to the received target cancer and the cancer diagnosis cells 42 corresponding to the target cancer from among the multiple individual learning models 123 included in the learning model 122 stored in the memory unit 12 (step S55).

[0145] The control unit 11 inputs the response profile of the corresponding cancer assessment cell 42 into each selected individual learning model 123 (step S56). Based on the information stored in the detection DB 121, the control unit 11 may read out subject information of the subject identified by the subject ID corresponding to the response profile, and input the subject's attributes and health information corresponding to the read response profile into the individual learning model 123. The control unit 11 obtains the possibility of cancer output from the individual learning model 123 (step S57). The individual learning model 123 outputs the presence or absence of the possibility of cancer, for example, for each type of cancer. The control unit 11 then performs the same processes as steps S28 to S32.

[0146] The determination device 1 may execute relearning of the above-described learning model 122. FIG.

[0147] The control unit 11 of the determination device 1 acquires a doctor's diagnosis of cancer in the subject (step S61). The diagnosis may be acquired, for example, by receiving an input from the subject via the subject's terminal device 2, or by communication with a computer at a medical institution.

[0148] The control unit 11 retrains the learning model 122 using the probability of cancer indicated by the acquired doctor's diagnosis result, and updates the learning model 122 (step S62). Specifically, the control unit 11 retrains the individual learning model 123 using the response profile input into the individual learning model 123 corresponding to the cancer type for which the diagnosis result was obtained and the probability of cancer indicated by the diagnosis result as training data, and updates the individual learning model 123. The control unit 11 optimizes the parameters so that the probability of cancer output from the individual learning model 123 approximates the diagnosis result, and regenerates the individual learning model 123.

[0149] According to this embodiment, the possibility of cancer can be easily and accurately determined using the learning model 122. By constructing an individual learning model 123 according to the type of cancer, the accuracy of determining the possibility of cancer can be improved. By using the subject's attributes and health information as input elements to the learning model 122, the possibility of cancer can be determined taking into account a wider variety of information, and improved accuracy is expected.

[0150] By re-learning the learning model 122 based on the doctor's diagnosis results, the learning model 122 can be optimized through the operation of this system.

[0151] Fourth Embodiment In a fourth embodiment, the possibility of cancer is determined using a plurality of determination methods. The determination device 1 of the fourth embodiment not only determines the possibility of cancer based on sensor data obtained by the olfactory sensor 4, but also acquires determination results of the possibility of cancer determined using other determination methods.

[0152] Other determination methods are not particularly limited as long as they are capable of determining the possibility of cancer, but examples include methods using image data obtained by an imaging inspection device, detection data obtained by a physical sensor, detection data obtained by a chemical sensor, and detection data obtained by a biosensor using biological elements other than cells with olfactory receptors.

[0153] Examples of methods using image data include determination based on image data obtained by an X-ray inspection device, a CT inspection device, an MRI inspection device, a PET inspection device, an ultrasound inspection device, etc. Examples of methods using detection data of physical quantities include determination based on detection data of physical quantities such as body temperature, pulse, heart rate, intravascular pressure, and intraocular pressure. Examples of methods using detection data from biosensors equipped with biological elements such as enzymes, antibodies, nucleic acids, microorganisms, sugar chains, and lipid membranes include determination based on detection data of chemical substance amounts such as urea, glucose, monoamines, sucrose, phospholipids, total cholesterol, triglycerides, amino acids, IgI, IgA, IgM, and albumin. Other determination methods may include determination based on test data obtained from genetic testing, chromosome testing, cell surface marker testing, and biomarker (e.g., amino acids, microRNA, nucleic acids, proteins, etc.) testing. The determination process using each method may be performed by the determination device 1 or externally. Determination of the possibility of cancer using other determination methods may be a secondary determination performed based on the results of the primary determination, with the determination using the olfactory sensor 4 being the primary determination.

[0154] 19 is a flowchart showing an example of a processing procedure executed by the determination system 100 of the fourth embodiment. After performing a comprehensive determination of the possibility of cancer by the processing up to step S27 of the first embodiment, the determination system 100 executes the following processing.

[0155] The control unit 11 of the determination device 1 acquires a result of the determination of the possibility of cancer determined by a determination method different from the determination method using the olfactory sensor 4 (step S71). The result of the determination by the different determination method may be acquired, for example, by communication with a computer of another testing institution. The determination result may be associated with the subject's identification information.

[0156] The control unit 11 derives a final judgment result based on the acquired judgment results from the different judgment methods and the previously acquired judgment result from the judgment method using the olfactory sensor 4 (step S72). The control unit 11 determines the final judgment result as, for example, a majority vote of the judgment results from the judgment methods. When making the final judgment, weighting may be performed so that a judgment result based on a specific judgment method is given a greater weight. Thereafter, the control unit 11 executes the same processes as steps S28 to S32.

[0157] Fig. 20 is a schematic diagram showing an example of a result screen 51 showing the determination result of the fourth embodiment. In the example shown in Fig. 20, the result screen 51 includes a first display section 511 that displays information about the subject to be determined, and a plurality of second display sections 512 that correspond to a plurality of target cancers selected by the subject.

[0158] Each second display unit 512 displays the cancer type of the target cancer and the assessment result for that target cancer. The assessment device 1 displays the assessment results of the possibility of cancer by each assessment method for each type of assessment method based on the assessment results by each assessment method on the second display unit 512. The assessment device 1 also displays the final assessment result based on each assessment result on the second display unit 512.

[0159] According to this embodiment, by combining a plurality of determination methods, it is possible to further improve the accuracy of determining the possibility of cancer. By providing the subject with the determination results for each determination method, it becomes possible to understand the determination results in detail.

[0160] The following supplementary notes are further disclosed with respect to the above embodiments. (Supplementary Note 1) A determination method in which a computer executes a process of acquiring a response signal to a target sample derived from a subject to be determined, detected using a receptor that exhibits a reactivity corresponding to the possibility of cancer in the subject, and determining the possibility of cancer in the subject based on the acquired response signal to the target sample. (Supplementary Note 2) The determination method according to Supplementary Note 1, in which the receptor exhibits a reactivity corresponding to the possibility of a specific cancer, acquiring response signals to a plurality of the target samples detected using a plurality of the receptors corresponding to each of a plurality of types of cancer, and determining the possibility and type of cancer in the subject to be determined based on the acquired response signals from the plurality of receptors. (Supplementary Note 3) The determination method according to Supplementary Note 1 or Supplementary Note 2, in which the response signals are detected using a biosensor including a plurality of the receptors. (Supplementary Note 4) The determination method according to any one of Supplements 1 to 3, in which the receptor is a receptor that exhibits a reactivity corresponding to the possibility of a first cancer among a plurality of cancers, but does not exhibit a reactivity corresponding to the possibility of a second cancer. (Supplementary Note 5) The method according to any one of Supplementary Notes 1 to 4, further acquiring a response signal to the subject sample, detected using a receptor that does not show reactivity according to the possibility of cancer in the subject. (Supplementary Note 6) The method according to any one of Supplementary Notes 1 to 5, determining the possibility of cancer in the subject based on a comparison of response signals to samples derived from cancer patients and non-cancer individuals associated with the receptor with a response signal to the subject sample. (Supplementary Note 7) The method according to Supplementary Note 6, determining the possibility of cancer and the type of cancer in the subject based on a comparison of response signals to samples derived from cancer patients and non-cancer individuals associated with each of the receptors for each cancer type with a response signal to the subject sample. (Supplementary Note 8) The method according to Supplementary Note 6 or Supplementary Note 7, wherein the response signals to the samples derived from cancer patients and non-cancer individuals are stored in advance. (Supplementary Note 9) The method according to any one of Supplementary Notes 6 to 8, outputting information that distinguishably represents the response signals to the samples derived from the cancer patient and non-cancer individuals from the response signal to the subject sample.(Supplementary Note 10) The method of any one of Supplementary Notes 1 to 9, which determines the possibility of cancer in a subject by inputting an acquired response signal to a subject sample derived from the subject, detected using a receptor that exhibits a reactivity according to the possibility of cancer in the subject, into a learning model that outputs the possibility of cancer in the subject when a response signal to the subject sample is input. (Supplementary Note 11) The method of Supplementary Note 10, which determines the possibility of cancer in a subject by acquiring subject information including attributes or health status of the subject, and inputting the acquired subject information and the acquired response signal to the subject sample into the learning model that outputs the possibility of cancer in the subject when the subject information related to the subject and a response signal to the subject sample are input. (Supplementary Note 12) The method of any one of Supplementary Notes 1 to 11, which acquires response signals to samples derived from cancer patients and cancer-free individuals for each of a plurality of receptors, and specifies the type or number of receptors from the plurality of receptors to be used for determining the possibility of cancer based on each acquired response signal. (Supplementary Note 13) The method according to any one of Supplementary Notes 1 to 12, which acquires a determination accuracy of the possibility of cancer based on response signals for samples derived from cancer patients and cancer-free individuals, for each of the plurality of receptors, and specifies the type or number of receptors to be used for determining the possibility of cancer from the plurality of receptors based on the acquired determination accuracy. (Supplementary Note 14) The method according to any one of Supplementary Notes 1 to 13, which acquires response signals for the subject samples derived from the subject on a plurality of collection dates, and outputs the acquired response signals for the subject samples for each collection date in association with the determination result of the possibility of cancer based on the response signals.

[0161] The embodiments disclosed herein are illustrative in all respects and should not be considered limiting. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and equivalents thereto. The sequences shown in each embodiment are not limited, and within the scope of no contradiction, each processing step may be executed in a different order, or multiple processes may be executed in parallel. The entity that performs each process is not limited, and within the scope of no contradiction, the process of each device may be executed by another device.

[0162] The matters described in each embodiment can be combined with each other. Furthermore, the independent claims and dependent claims described in the claims can be combined with each other in any and all combinations, regardless of the reference format. Furthermore, the claims use a format in which a claim references two or more other claims (multiple claim format), but this is not limited to this. A multiple claim (multi-multi claim) that references at least one other multiple claim may also be used.

[0163] REFERENCE SIGNS LIST 100 Determination system 1 Determination device 11 Control unit 12 Memory unit 13 Communication unit 1A Recording medium 1P Program 121 Detection DB 122 Learning model 2 Terminal device 21 Control unit 22 Memory unit 23 Communication unit 24 Display unit 25 Operation unit 2A Recording medium 2P Program 3 Detection device 4 Olfactory sensor 42 Cell

Claims

1. A method for determining the possibility of cancer in a subject by a computer, which acquires a response signal to a target sample derived from the subject, detected using a receptor that exhibits reactivity according to the possibility of cancer in the subject, and determines the possibility of cancer in the subject based on the acquired response signal to the target sample.

2. The method of claim 1, wherein the receptor exhibits reactivity according to the possibility of a specific cancer, and response signals are obtained for a plurality of the target samples detected using a plurality of the receptors corresponding to each of a plurality of types of cancer, and the possibility and type of cancer in the subject are determined based on the response signals of the plurality of receptors obtained.

3. The determination method according to claim 1 or 2, wherein the response signal is detected using a biosensor having a plurality of the receptors.

4. A determination method described in any one of claims 1 to 3, wherein the receptor exhibits reactivity according to the possibility of a first cancer among multiple cancers, and does not exhibit reactivity according to the possibility of a second cancer.

5. A determination method described in any one of claims 1 to 4, further comprising obtaining a response signal to the subject sample detected using a receptor that does not exhibit reactivity according to the possibility of cancer in the subject.

6. A method for determining the possibility of cancer in the subject of determination described in any one of claims 1 to 5, which determines the possibility of cancer in the subject of determination based on a comparison of the response signal for samples derived from cancer patients and non-cancer patients related to the receptor with the response signal for the subject sample.

7. A method for determining the possibility of cancer and type of cancer in the subject of determination based on a comparison of the response signals for samples derived from cancer patients and non-cancer individuals for each of the receptors for each type of cancer with the response signals for the subject sample.

8. The method of claim 6 or 7, wherein response signals to samples derived from cancer patients and non-cancer patients are stored in advance.

9. A determination method according to any one of claims 6 to 8, which outputs information that distinguishably represents the response signals to samples derived from cancer patients and non-cancer patients from the response signal to the target sample.

10. A method for determining the possibility of cancer in a subject described in any one of claims 1 to 9, which determines the possibility of cancer in the subject by inputting a response signal obtained for a subject sample derived from the subject into a learning model that outputs the possibility of cancer in the subject when a response signal for the subject sample derived from the subject is input, the response signal being detected using a receptor that shows reactivity according to the possibility of cancer in the subject.

11. A method for determining the possibility of cancer in a subject described in claim 10, which acquires subject information including the attributes or health condition of the subject, and inputs the acquired subject information and a response signal to the subject sample into a learning model that outputs the possibility of cancer in the subject when the subject information related to the subject and a response signal to the subject sample are input.

12. A method for determining the possibility of cancer according to any one of claims 1 to 11, which comprises obtaining a response signal for each of a plurality of receptors in response to samples derived from cancer patients and non-cancer individuals, and identifying the type or number of receptors from the plurality of receptors to be used to determine the possibility of cancer based on each of the obtained response signals.

13. A method of determining the possibility of cancer described in any one of claims 1 to 12, which obtains the accuracy of determining the possibility of cancer based on response signals for samples derived from cancer patients and non-cancer individuals for each of the plurality of receptors, and identifies the type or number of receptors from the plurality of receptors to be used to determine the possibility of cancer based on the obtained accuracy of determination.

14. A determination method according to any one of claims 1 to 13, which acquires response signals for the target sample derived from the subject on multiple collection dates, and outputs the response signals for the target sample on each of the acquired collection dates in association with the determination result of the possibility of cancer based on the response signals.

15. A determination device comprising a control unit that acquires a response signal to a target sample derived from a subject to be determined, detected using a receptor that exhibits reactivity according to the possibility of cancer in the subject, and executes a process to determine the possibility of cancer in the subject to be determined based on the acquired response signal to the target sample.

16. A computer program that causes a computer to execute a process of acquiring a response signal to a target sample derived from a subject to be evaluated, detected using a receptor that exhibits reactivity according to the possibility of cancer in the subject, and determining the possibility of cancer in the subject to be evaluated based on the acquired response signal to the target sample.

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