Diagnostic support device, diagnostic support method, and program

Abstract

To appropriately support a diagnosis by a liquid biopsy.SOLUTION: A diagnosis support device comprises an acquisition section and a determination section. The acquisition section acquires a first measured value of a biomarker in a first liquid sample collected from a subject after physical stimulus is applied, and a second measured value of the biomarker in a second liquid sample collected from the subject after the physical stimulus is applied. The determination section performs determination on at least one of the presence / absence and a property of lesion on the basis of the first measured value and the second measured value.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to a diagnostic support device, a diagnostic support method, and a program.

Background Art

[0002] In recent years, in cancer screening such as breast cancer screening, liquid biopsy may be performed together with image diagnosis such as mammography and ultrasonic examination. It is known that the amount of biomarkers in the blood increases with physical stimulation to the site including the lesion, such as ultrasonic irradiation and breast compression in mammography.

[0003] Under such circumstances, in liquid biopsy, there is a problem that the discrimination accuracy cannot be guaranteed when the measured value of the biomarker amount is near the discrimination border line. If the discrimination accuracy cannot be guaranteed, there is a risk that the lesion cannot be properly diagnosed. [[ID=******]]

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Non-Patent Documents

[0005]

Non-Patent Document 1

Non-Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] One of the problems that the embodiments disclosed herein seek to solve is to appropriately support diagnosis by liquid biopsy. However, the problems that the embodiments disclosed herein and in the drawings seek to solve are not limited to the above problem. Problems corresponding to each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0007] The diagnostic support device according to the embodiment comprises an acquisition unit and a determination unit. The acquisition unit acquires a first measurement value of a biomarker in a first liquid sample taken from the subject before applying physical stimulation, and a second measurement value of the biomarker in a second liquid sample taken from the subject after applying the physical stimulation. The determination unit makes a determination regarding at least one of the presence or absence and nature of a lesion based on the first and second measurement values. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of the configuration of a diagnostic support system according to an embodiment. [Figure 2] Figure 2 shows an example of the configuration of a diagnostic support device according to the present invention. [Figure 3] Figure 3 is a flowchart showing an example of a diagnostic support workflow according to this embodiment. [Figure 4] Figure 4 is a flowchart showing an example of the flow of support processing performed in the diagnostic support device according to the embodiment. [Figure 5] Figure 5 is a diagram illustrating an example of the determination conditions for the primary determination of the support process according to this embodiment. [Figure 6] Figure 6 is a diagram illustrating an example of the determination conditions for the secondary determination of the support processing according to the embodiment. [Modes for carrying out the invention]

[0009] The diagnostic support devices, diagnostic support methods, and programs according to each embodiment will be described below with reference to the drawings. In the following description, components having the same or substantially the same function as those previously described in the drawings will be denoted by the same reference numerals, and will be described again only when necessary. Furthermore, even when representing the same part, the dimensions and proportions may differ between drawings. In addition, for example, from the viewpoint of ensuring the readability of the drawings, reference numerals may be denoted only for the main components in the description of each drawing, and reference numerals may not be denoted for components having the same or substantially the same function.

[0010] In recent years, liquid biopsy is sometimes performed in conjunction with imaging diagnostics such as mammography and ultrasound during cancer screenings, including breast cancer screenings. It is known that the amount of biomarkers in the blood increases with physical stimulation of the area containing the lesion, such as ultrasound irradiation or breast compression during mammography. For example, it is known that the concentrations of CTCs and ctDNA in the blood change before and after breast compression. In addition, it is known that ctDNA release from lung cancer cell lines in the blood increases with low-intensity pulsed ultrasound (LIPUS) irradiation.

[0011] In this context, liquid biopsy presented a problem where, even if the amount of biomarkers increased in response to physical stimulation, the accuracy of the discrimination could not be guaranteed if the measured value was near the borderline of the discrimination threshold. When discrimination accuracy could not be guaranteed, there was a risk that the lesion could not be properly diagnosed.

[0012] Therefore, this disclosure discloses a diagnostic support system 1 that can appropriately support diagnosis by liquid biopsy. Specifically, this disclosure discloses a diagnostic support system 1 that can improve the accuracy of determining at least one of the presence or absence and nature of lesions by liquid biopsy.

[0013] (First embodiment) Figure 1 shows an example of the configuration of a diagnostic support system 1 according to an embodiment. As shown in Figure 1, the diagnostic support system 1 includes a diagnostic support device 10, a medical image diagnostic device 30, a hospital information system (HIS) 50, a radiology information system (RIS) 70, and a picture archiving and communication system (PACS) 90. Each device of the diagnostic support system 1 is installed, for example, in a hospital, and can communicate with other devices via a network 9 such as a hospital LAN (Local Area Network). Note that the HIS 50 may be connected to an external network in addition to the hospital LAN.

[0014] HIS50 is a system for managing information generated within a hospital. This information includes patient information and test order information. Each record in patient information has items such as patient ID, patient name, age (date of birth), gender, height, weight, and blood type. Each record in test order information has items such as a test ID that can identify the test, patient ID, information indicating whether it is inpatient or outpatient, test code, medical department, test type, test site, and scheduled test date and time.

[0015] The examination ID is issued when examination order information is entered and is an identifier used to uniquely identify examination order information within a single hospital, for example. The patient ID is assigned to each patient and is an identifier used to uniquely identify a patient within a single hospital, for example. The examination code is an identifier used to uniquely identify an examination, for example, defined within a single hospital. The medical department indicates the specialization of medical practice, for example. Specifically, medical departments include internal medicine and surgery. The examination type indicates an examination using medical imaging. For example, examination types include X-ray examinations, CT (Computed Tomography) examinations, and MRI (Magnetic Resonance Imaging) examinations. The examination site includes the brain, kidneys, lungs, and liver, for example.

[0016] When inspection order information is input by, for example, a requesting doctor, HIS50 transmits the input inspection order information and the patient information specified by the inspection order information to RIS70. Also, in this case, HIS50 transmits the patient information to PACS90.

[0017] RIS70 is a system that manages inspection reservation information related to radiological examination operations. For example, RIS70 receives the inspection order information transmitted from HIS50, adds various setting information to the received inspection order information and accumulates it, and manages the accumulated information as inspection reservation information. Specifically, when RIS70 receives patient information and inspection order information transmitted from HIS50, it generates inspection reservation information necessary for operating the medical imaging diagnostic device 30 based on the received patient information and inspection order information. The inspection reservation information includes information necessary for performing the inspection, such as, for example, an inspection ID, patient ID, type of inspection, and inspection site. RIS70 transmits the generated inspection reservation information to the medical imaging diagnostic device 30.

[0018] The medical imaging diagnostic device 30 is a device that generates medical image data based on data collected from a subject (patient). As the medical imaging diagnostic device 30, various medical imaging diagnostic devices such as an X-ray diagnostic device, an X-ray CT (Computed Tomography) device, an MRI (Magnetic Resonance Imaging) device, an ultrasonic diagnostic device, a SPECT (Single Photon Emission Computed Tomography) device, a PET (Positron Emission computed Tomography) device, a SPECT-CT device in which a SPECT device and an X-ray CT device are integrated, and a PET-CT device in which a PET device and an X-ray CT device are integrated can be appropriately used.

[0019] The medical imaging diagnostic apparatus 30 performs an examination based on, for example, examination reservation information transmitted from the RIS 70. The medical imaging diagnostic apparatus 30 generates examination execution information indicating the execution of the examination and transmits it to the RIS 70. In this case, the RIS 70 receives the examination execution information from the medical imaging diagnostic apparatus 30 and outputs the received examination execution information to the HIS 50 or the like as the latest examination execution information. For example, the HIS 50 receives the latest examination execution information and manages the received examination execution information. The examination execution information includes examination reservation information such as an examination ID, a patient ID, an examination type, and an examination site, and the date and time of the execution of the examination.

[0020] The medical imaging diagnostic apparatus 30 converts the generated medical image data into a format compliant with, for example, the DICOM (Digital Imaging and Communication in Medicine) standard. That is, the medical imaging diagnostic apparatus 30 generates medical image data with DICOM tags added as additional information.

[0021] The additional information includes, for example, a patient ID, an examination ID, a device ID, and an image series ID, and is standardized according to the DICOM standard. The device ID is information for identifying the medical imaging diagnostic apparatus 30. The image series ID is information for identifying one imaging by the medical imaging diagnostic apparatus 30 and includes, for example, the site of the subject imaged, the image generation time, the slice thickness, and the slice position. For example, by performing a CT examination or an MRI examination, tomographic images at each of a plurality of slice positions are obtained as medical image data.

[0022] The medical imaging diagnostic apparatus 30 transmits the generated medical image data to the PACS 90. The PACS 90 is a system for managing various medical image data.

[0023] PACS90 receives patient information transmitted from, for example, HIS50 and manages the received patient information. PACS90 is equipped with a memory circuit for managing patient information. PACS90 receives medical image data transmitted from, for example, medical imaging diagnostic device 30, associates the received medical image data with patient information, and stores it in its own memory circuit. The medical image data stored in PACS90 is accompanied by supplementary information such as patient ID, examination ID, device ID, and image series ID. Therefore, the operator can obtain the necessary patient information from PACS90 by performing a search using the patient ID, etc. The operator can also obtain the necessary medical image data from PACS90 by performing a search using the patient ID, examination ID, device ID, image series ID, etc.

[0024] Here, HIS50 receives, for example, an electronic medical record created by a clinician who is the requesting physician for the examination, and the examination implementation information corresponding to that electronic medical record. It then associates the received electronic medical record with the examination implementation information and stores it in its own memory circuit. As mentioned above, the examination implementation information includes the examination ID, patient ID, examination type, examination site, and the date and time the examination was performed. Therefore, the operator can retrieve the necessary electronic medical record from HIS50 by searching using the patient ID, examination ID, etc. In this embodiment, the electronic medical record is stored in the memory circuit of HIS50, but it may also be stored in the memory circuit of another device within the diagnostic support system 1, as long as searching by ID is possible.

[0025] Furthermore, RIS70 receives, for example, an image interpretation report created in response to input from a radiologist, along with the corresponding examination implementation information, associates the received image interpretation report with the examination implementation information, and stores it in its own memory circuit. As mentioned above, the examination implementation information includes the examination ID, patient ID, examination type, examination site, and date and time of the examination, so the operator can obtain the necessary image interpretation report from RIS70 by searching using the patient ID, examination ID, etc. In this embodiment, the image interpretation report is stored in the memory circuit of RIS70, but it may also be stored in the memory circuit of another device within the diagnostic support system 1, as long as searching by ID is possible.

[0026] The diagnostic support device 10 performs support processing. The diagnostic support device 10 acquires various medical data from the medical image diagnostic device 30, HIS50, RIS70, and PACS90 via the network 9, and performs various information processing using the acquired medical data. For example, the diagnostic support device 10 is implemented by a computer such as a workstation that has a processor and memory such as ROM and RAM as hardware resources. The diagnostic support device 10 has, for example, an integrated viewer implemented. The integrated viewer is an application that presents medical information to the user in an integrated manner. The integrated viewer may adopt any implementation form, such as a web application, a fat client application, or a thin client application.

[0027] Medical data is information that represents medical records obtained by healthcare professionals during the course of treatment, regarding the patient's physical condition, illness, and treatment. Medical data includes data acquired under various conditions, such as different manufacturers' equipment, different versions of equipment, and different settings even with the same equipment. Medical data is not limited to objective data such as numerical values, but may also include non-numerical data, such as subjective data expressed in text. Medical data includes, for example, examination history information, image information, electrocardiogram information, vital sign information, medication history information, report information, medical record information, nursing record information, referral letters, and discharge summaries. Examination history information is, for example, information representing the history of examination results obtained as a result of specimen tests and bacterial tests performed on the patient. Image information is, for example, information representing the location of medical images obtained by taking photographs of the patient. Image information includes, for example, information representing the location of medical image files generated by medical imaging diagnostic equipment as a result of examinations being performed. Electrocardiogram information is, for example, information regarding electrocardiogram waveforms measured from the patient. Vital sign information is, for example, basic information related to the patient's life. Vital sign information includes, for example, pulse rate, respiratory rate, body temperature, blood pressure, and level of consciousness. Medication history information includes, for example, information showing the history of the amount of medication administered to the patient. Report information includes, for example, information compiled by a radiologist in the radiology department after interpreting medical images such as X-ray images, CT images, MRI images, and ultrasound images in response to a request for examination from a physician in a clinical department, summarizing the patient's condition and disease. Report information includes, for example, interpretation report information representing an interpretation report created by the radiologist by referring to a medical image file stored in PACS90. Report information includes, for example, information representing the patient ID, patient name, and date of birth of the patient corresponding to the medical image file being interpreted. Medical record information includes, for example, information entered into the electronic medical record by a physician or other person. Medical record information includes, for example, the medical record at the time of admission, the patient's medical history, and the history of medication prescriptions. Nursing record information includes, for example, information entered into the electronic medical record by a nurse or other person. Nursing record information includes, for example, the nursing record at the time of admission. Nursing records may include meal records from the time of hospitalization.Furthermore, medical data may also include accounting information.

[0028] Furthermore, the diagnostic support system 1 may have a VNA (Vendor Neutral Archive) system instead of HIS50, RIS70, and PACS90. The VNA system is an integrated archiving system that centrally manages diverse medical data managed by PACS90 systems from different manufacturers and each clinical department system (HIS50, RIS70). The VNA system is connected to HIS50, RIS70, and PACS90, for example, via a hospital network such as a LAN, enabling them to communicate with each other. Furthermore, the various types of information managed and stored by the VNA system are not necessarily limited to those obtained from systems from different manufacturers, but may also be obtained from a system from a single manufacturer.

[0029] Figure 2 shows an example of the configuration of a diagnostic support device 10 according to an embodiment. As shown in Figure 2, the diagnostic support device 10 has a processing circuit 11, a storage circuit 13, a communication interface 15, an input interface 17, and a display 19. The processing circuit 11, storage circuit 13, communication interface 15, input interface 17, and display 19 are connected to each other via a bus or the like so that they can communicate with each other.

[0030] The memory circuit 13 stores various types of data. For example, the memory circuit 13 stores image data and medical data received from the medical imaging diagnostic device 30, HIS50, RIS70, and PACS90. It also stores parameters and programs, such as various threshold values, for implementing the support processing described later. The memory circuit 13 is implemented using, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, a hard disk, or an optical disc. The storage area of ​​the memory circuit 13 may be located within the diagnostic support device 10 or in an external storage device connected via a network. Here, the memory circuit 13 is an example of a storage unit.

[0031] The communication interface 15 controls the transmission and communication of various data between the medical imaging diagnostic device 30, HIS50, RIS70, and PACS90. For example, the communication interface 15 receives image data and medical data from the medical imaging diagnostic device 30, HIS50, RIS70, or PACS90, and outputs the received data to the processing circuit 11. The communication interface 15 is implemented by, for example, a network card, network adapter, or NIC (Network Interface Controller).

[0032] The input interface 17 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs them to the processing circuit 11. For example, the input interface 17 receives various input operations from the operator for various operation screens related to support processing. As an example, the input interface 17 receives input operations from the operator for liquid biopsy measurement values. Here, the input interface 17 is an example of an input unit.

[0033] The input interface 17 can be, for example, a mouse, keyboard, trackball, switch, button, joystick, touchpad, or touch panel display, as appropriate. However, in this embodiment, the input interface 17 is not limited to those equipped with these physical operating components. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to the processing circuit 11 is also included as an example of the input interface 17. Furthermore, the input interface 17 may consist of a tablet terminal or the like that can communicate wirelessly with the main body of the diagnostic support device 10.

[0034] The display 19 displays various types of information. The display 19 outputs, for example, a GUI (Graphical User Interface) generated by the processing circuit 11 for receiving various operations from the operator. The GUI for receiving various operations from the operator includes various operation screens related to support processing. For example, the display 19 outputs a display screen related to support processing generated by the processing circuit 11. As an example, the display 19 outputs a display screen including the judgment results for each judgment. Various arbitrary displays can be used as the display 19 as appropriate. For example, a liquid crystal display (LCD), a cathode ray tube (CRT) display, an organic electroluminescent display (OLED), or a plasma display can be used as the display 19. Here, the display 19 is an example of a display unit.

[0035] The display 19 may be a desktop type, or it may consist of a tablet terminal or the like that which can communicate wirelessly with the main unit of the diagnostic support device 10. In addition, one or more projectors may be used as the display 19.

[0036] The processing circuit 11 controls the overall operation of the diagnostic support device 10. The processing circuit 11 has a processor and memory such as ROM or RAM as hardware resources. The processing circuit 11 executes acquisition functions 111, detection functions 113, judgment functions 115, and display control functions 117, etc., using the processor which executes programs loaded into memory.

[0037] Here, processing circuit 11 is an example of a processing unit. Processing circuit 11 that implements the acquisition function 111 is an example of an acquisition unit. Processing circuit 11 that implements the detection function 113 is an example of a detection unit. Processing circuit 11 that implements the determination function 115 is an example of a determination unit. Processing circuit 11 that implements the display control function 117 is an example of a display control unit.

[0038] In the acquisition function 111, the processing circuit 11 acquires medical image data and clinical data from the medical imaging diagnostic device 30, HIS50, RIS70, or PACS90 via the network 9. The processing circuit 11 also acquires the results of operator input received by the input interface 17.

[0039] As an example, in the acquisition function 111, the processing circuit 11 acquires clinical data including the measured values ​​of biomarkers in each liquid sample collected before and after applying physical stimulation to the target area of ​​the subject (patient). The liquid sample is not particularly limited as long as it is a sample used in liquid biopsy, but it may be a blood sample collected from the subject, a lymph fluid sample collected from the subject, or a urine or tear fluid sample collected from the subject. In the following explanation, the measured value of the biomarker may also be simply referred to as the marker value.

[0040] Here, the liquid sample taken from the subject before applying physical stimulation and the measured values ​​of biomarkers in said liquid sample are examples of the first liquid sample and the first measured values, respectively. Furthermore, the liquid sample taken from the subject after applying physical stimulation and the measured values ​​of biomarkers in said liquid sample are examples of the second liquid sample and the second measured values, respectively.

[0041] The target area of ​​the subject to which physical stimulation is applied can be appropriately set depending on the target of diagnosis. For example, in the case of breast cancer screening, the target area is the subject's breast. The target of diagnosis is not limited to breast cancer; it may also include liver cancer, kidney cancer, prostate cancer, pancreatic cancer, bladder cancer, colorectal cancer, stomach cancer, esophageal cancer, uterine cancer, skin cancer, lung cancer, etc.

[0042] The physical stimulus, for example, when diagnosing breast cancer, is compression of the subject's breast during mammography using the medical imaging diagnostic device 30 as an X-ray diagnostic device. Alternatively, the physical stimulus may be ultrasound irradiation to the target area of ​​the subject. The physical stimulus can be any external stimulus that causes biomarkers to leak into the bloodstream from lesions such as cancer, and other methods such as compression during mammography or ultrasound irradiation may be used.

[0043] As biomarkers, for example, when diagnosing breast cancer, circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), which are cancer-derived cells, can be used. For example, when diagnosing lung cancer, ctDNA can be used as a biomarker. In addition, as biomarkers, cancer-derived proteins, DNA, RNA, exosomes and other extracellular vesicles present in the blood, or tumor cells themselves, can be appropriately selected depending on the target of diagnosis.

[0044] As an example, in the acquisition function 111, the processing circuit 11 acquires medical data including information about the intensity of physical stimulation applied to the target area of ​​the subject (patient).

[0045] Furthermore, information regarding marker values ​​and the intensity of physical stimulation may be obtained not only as clinical data, but also based on input from the input interface 17 by examiners such as physicians and technicians. In addition, marker values ​​may be obtained from a measuring device that measures the amount of biomarkers from blood samples.

[0046] In the detection function 113, the processing circuit 11 detects lesions based on medical images obtained by photographing the subject. Specifically, the processing circuit 11 performs computer-aided diagnostic imaging (CAD) as an image diagnosis based on the medical images. The CAD in this embodiment automatically detects the presence or absence of lesions and their location on the medical images based on predetermined detection conditions.

[0047] The CAD may also automatically detect the presence or absence of potential lesions on a medical image based on predetermined detection conditions. In this case, the display control function 117 displays the medical image and potential lesions on the display 19. Furthermore, examiners such as doctors and technicians input whether or not there are lesions on the medical image via the input interface 17. The detection function 113 then detects the presence or absence of lesions and their location on the medical image based on the examiner's input.

[0048] Note that CAD is not a mandatory component. For example, medical images may be interpreted by examiners such as doctors or technicians, and the medical data including the interpretation results may be acquired by the acquisition function 111, or the interpretation results may be input by the input interface 17. It is also possible that medical data including the results of CAD performed outside the diagnostic support device 10 may be acquired by the acquisition function 111.

[0049] In the judgment function 115, the processing circuit 11 makes a determination regarding the presence or absence of a lesion based on the marker values ​​before and after the application of physical stimulation. The processing circuit 11 also makes a determination regarding the presence or absence of a lesion based on the results of image diagnosis such as CAD.

[0050] As an example, in the judgment function 115, the processing circuit 11 performs a primary judgment in which it determines that a lesion is present when the marker value after physical stimulation is greater than a predetermined threshold. Here, the primary judgment is an example of the first judgment. In the primary judgment, not only the marker value after physical stimulation, but also the marker value before physical stimulation or statistical values ​​such as the average of these may be used.

[0051] For example, in the judgment function 115, if the processing circuit 11 is not judged positive in the primary judgment, it calculates a judgment index related to the marker values ​​before and after physical stimulation, and performs a secondary judgment regarding the presence or absence of a lesion based on the calculated judgment index.

[0052] For example, in the judgment function 115, the processing circuit 11 calculates a judgment index when the primary judgment is not positive and the intensity of the physical stimulus is greater than a predetermined threshold. As will be described later, highly invasive cancers in which biomarkers leak into the bloodstream due to breast compression are highly malignant. Therefore, by applying this only when the breast compression pressure during imaging is above a certain level, it is possible to determine that the increase in marker values ​​is due to compression and improve the detection accuracy for highly malignant cancers.

[0053] In the secondary assessment, a lesion is determined to be present when the assessment index is greater than a predetermined threshold. In other words, different assessment conditions are used in the secondary assessment than in the primary assessment. To put it another way, in the assessment function 115, the processing circuit 11 changes the assessment conditions for determining the presence or absence of a lesion when the assessment index is greater than a predetermined threshold.

[0054] Here, the judgment index is, for example, the increase in marker values ​​before and after physical stimulation. Alternatively, the judgment index may be the percentage increase in marker values ​​before and after mammography. Furthermore, both the amount of increase and the percentage increase may be used as judgment indicators. When using both the amount of increase and the percentage increase, they may be compared separately to a threshold, or statistical values ​​based on the amount of increase and the percentage increase may be used as judgment indicators and compared to a threshold.

[0055] Furthermore, the processing circuit 11 performs a secondary determination regarding the presence or absence of lesions based on the results of image diagnostics such as CAD. The processing circuit 11 also changes the detection conditions for CAD prior to the image diagnostics when the determination index is greater than a predetermined threshold, i.e., when the marker value increases after physical stimulation.

[0056] One example of changing the detection conditions in CAD is to modify the detection algorithm used to detect the presence or absence of lesions from medical images, depending on the type of biomarker whose diagnostic indicators have increased before and after mammography. For example, cancers with many neovascularizations and active proliferation, i.e., highly invasive cancers, have a high risk of metastasis and are highly malignant. Such highly invasive cancers are close to blood vessels and lymphatic vessels, and biomarkers are easily leaked out by physical stimulation. Thus, it can be assumed that the types of biomarkers that increase are correlated with the tumor microenvironment. Therefore, if, for example, the diagnostic indicators related to invasive cancer increase, the detection algorithm will be changed to one that is suitable for detecting lesions around lymphatic vessels.

[0057] For example, changing the detection conditions in CAD involves adjusting the parameters of the detection algorithm that detects the presence or absence of lesions in medical images, thereby increasing the detection sensitivity or changing how contrast is applied.

[0058] As an example, changing the detection conditions for CAD means changing the detection algorithm that detects the presence or absence of lesions from medical images to an algorithm whose parameters have been determined (learned) using medical images in which the judgment index has increased before and after mammography. Preferably, the changed detection algorithm is an algorithm whose parameters have been determined (learned) using only medical images in which the number of biomarkers that were determined to have increased in the S207 process has increased.

[0059] In the display control function 117, the processing circuit 11 displays a display screen on the display 19 that includes the results of various judgments, including primary and secondary judgments. The processing circuit 11 may also display a display screen on the display 19 that includes medical images for image diagnosis, or detection results showing lesion candidates or lesions detected by CAD.

[0060] Furthermore, the diagnostic support device 10 according to this embodiment may be mounted on a measuring device that measures the amount of biomarkers in a liquid sample during liquid biopsy. Alternatively, the diagnostic support device 10 may be configured to acquire the measured value of biomarkers output from the measuring device, for example, via wired, wireless, or external storage device.

[0061] Furthermore, each function 111, 113, 115, and 117 is not limited to being implemented in a single processing circuit. It is also possible to configure the processing circuit 11 by combining multiple independent processors, with each processor executing its respective program to realize each function 111, 113, 115, and 117. Here, each function 111, 113, 115, and 117 may be implemented by being appropriately distributed or integrated across one or more processing circuits.

[0062] While the diagnostic support device 10 is illustrated as an example of a single computer performing multiple functions, it is not limited to this configuration. The multiple functions of the diagnostic support device 10 may be performed by separate computers. For example, the functions of the processing circuits 11, such as the detection function 113 and the judgment function 115, may be distributed and executed by at least two computers.

[0063] The support processing by the diagnostic support system 1 according to this embodiment will be described below with reference to the drawings.

[0064] Figure 3 is a flowchart showing an example of a diagnostic support workflow according to the embodiment. Here, an example is given of a breast cancer examination targeting breast cancer, in which a blood sample is used as the liquid sample. Furthermore, an example is given of a case in which mammography is used as the physical stimulation to the breast, which is the examination site according to breast cancer.

[0065] First, the patient (subject) comes to a diagnostic location such as a hospital (S101). Then, a nurse or doctor or other examiner performs a blood sample on the patient upon arrival, obtaining a blood sample before the breast is compressed by mammography. The technician or doctor or other examiner also performs a liquid biopsy on the collected blood sample to measure the amount of biomarkers in the blood sample before the breast is compressed by mammography (S102). The measured value of the biomarkers (marker value) is registered in the HIS50, for example, via the hospital network or recording medium from the measuring device. Alternatively, the marker value may be input into the diagnostic support device 10 by the examiner or assistant using the input interface 17.

[0066] Technicians and doctors perform mammography on patients after blood collection (S103). In other words, physical stimulation is applied to the breasts of patients undergoing breast cancer screening after blood collection.

[0067] Nurses, doctors, and other examiners collect blood samples from patients after their breasts have been compressed by mammography. Technicians, doctors, and other examiners then perform a liquid biopsy on the collected blood samples to measure the amount of biomarkers in the blood samples after breast compression by mammography (S104). The marker values ​​can be registered in HIS50 or input into the diagnostic support device 10, similar to step S102.

[0068] Examiners such as technicians and doctors use the diagnostic support device 10 to perform support processing based on the results of liquid biopsy on blood samples taken before and after breast compression for mammography (S105). The support processing will be described later.

[0069] The examiner, such as a physician, confirms the diagnosis of breast cancer by referring to the results of the support processing (S106). As will be described later, the results of the support processing provide information on at least one of the presence or nature of a lesion in the examination site. The process shown in Figure 3 then concludes.

[0070] Figure 4 is a flowchart showing an example of the flow of support processing performed in the diagnostic support device 10 according to this embodiment.

[0071] The acquisition function 111 acquires clinical data, including the results of each liquid biopsy performed before and after mammography (S201). The acquisition function 111 also acquires medical images obtained from mammography. The detection function 113 then performs computer-aided diagnostic imaging (CAD) as an image diagnosis based on these medical images (S202).

[0072] The judgment function 115 performs a primary judgment based on the marker values ​​in the liquid biopsy and the results of the image diagnosis (S203). The judgment function 115 performs the primary judgment by referring to, for example, predetermined judgment conditions stored in the memory circuit 13. The processes in S201 to S203 or S202 to S203 may be collectively referred to as the secondary judgment.

[0073] Figure 5 is a diagram illustrating an example of the judgment conditions for the primary judgment of the support processing according to the embodiment. In Figure 5, "LB+" indicates that the marker value in the liquid biopsy is greater than a predetermined threshold (cutoff value, discrimination borderline), i.e., the liquid biopsy judgment is positive. "LB-" indicates that the measured value is less than a predetermined threshold, i.e., the liquid biopsy judgment is negative. "LB- / +" indicates that the measured value is near a predetermined threshold, i.e., the liquid biopsy judgment is near the borderline. "MG+" indicates that a lesion is detected by image diagnosis based on medical images obtained from mammography, i.e., the image diagnosis is positive. "MG-" indicates that no lesion is detected by the image diagnosis, i.e., the image diagnosis is negative. "MG- / +" indicates that a lesion or candidate lesion near a predetermined detection criterion is detected in the image diagnosis, i.e., the image diagnosis is near the borderline.

[0074] As shown in Figure 5, the judgment function 115 determines a positive (+) result in the primary assessment when at least one of the liquid biopsy and imaging diagnosis is positive. Furthermore, the judgment function 115 determines a negative (-) result in the primary assessment when one of the liquid biopsy and imaging diagnosis is negative and the other is borderline. Also, the judgment function 115 determines a borderline (- / +) result in the primary assessment when both the liquid biopsy and imaging diagnosis are near the borderline.

[0075] The judgment function 115 determines whether the initial judgment was positive or not (S204). If the initial judgment was positive (S204: Yes), the judgment function 115 outputs the judgment result (S210), and the flow shown in Figure 4 ends.

[0076] On the other hand, if the initial assessment is not positive (S204: No), the assessment function 115 determines whether the compression pressure during mammography was above a predetermined threshold (S205). Specifically, the acquisition function 111 acquires medical data including information indicating the compression pressure during mammography. The compression pressure during mammography may be defined for each imaging protocol, or it may be acquired by measurement during imaging. Here, the information indicating the compression pressure during mammography is information indicating the intensity of the physical stimulus. The predetermined threshold is, for example, predetermined and stored in the memory circuit 13. If it is determined that the compression pressure during mammography was not above the predetermined threshold (S205: No), the assessment function 115 outputs the assessment result (S210), and the flow in Figure 4 ends. Note that the processing in S205 is not mandatory, and there may be cases where it is not performed.

[0077] On the other hand, if it is determined that the compression pressure during mammography was above a predetermined threshold (S205: Yes), the judgment function 115 compares and analyzes the marker values ​​in the blood sample before and after the breast is compressed by mammography (S206). Specifically, the judgment function 115 calculates a judgment index for the marker values ​​before and after mammography. Then, the judgment function 115 determines whether or not the marker values ​​increased after mammography (S207). Specifically, the judgment function 115 determines whether or not the judgment index before and after mammography is greater than a predetermined threshold. This threshold is, for example, predetermined and stored in the memory circuit 13.

[0078] When it is determined that the marker value has increased after mammography (S207: Yes), the determination function 115 changes the detection conditions for CAD. The detection function 113 automatically detects the presence or absence and location of lesions on the medical image based on the changed detection conditions (S208).

[0079] The judgment function 115 performs a secondary judgment based on the marker value in the liquid biopsy, the judgment index, and the results of the image diagnosis (S209). The judgment function 115 performs the secondary judgment by referring to, for example, predetermined judgment conditions stored in the memory circuit 13. Note that the processes in S205-S209, S206-S209, or S207-S209 may be collectively referred to as the secondary judgment.

[0080] Figure 6 is a diagram illustrating an example of the judgment conditions for the secondary judgment of the support processing according to the embodiment. In Figure 6, "LB+", "LB-", "LB- / +", "MG+", "MG-", and "MG- / +" are the same as in the example shown in Figure 5. In Figure 6, "ΔLB+" indicates that the marker value increased after the physical stimulus, that is, the judgment index is greater than a predetermined threshold. "ΔLB-" indicates that the marker value did not increase after the physical stimulus, that is, the judgment index is less than or equal to a predetermined threshold.

[0081] As shown in Figure 6, if the marker value increases after physical stimulation, the judgment function 115 will determine it as positive (+) in the secondary judgment, even if it is near the borderline in the primary judgment. Subsequently, the judgment function 115 outputs the judgment result (S210), and the flow in Figure 4 ends.

[0082] Thus, in this embodiment of diagnostic support, if the primary determination is not positive, a secondary determination is performed according to the determination index of marker values ​​in liquid biopsy before and after mammography.

[0083] With this configuration, even if there is a possibility of a false negative in the liquid biopsy during the initial assessment, a positive result can be determined if the diagnostic index increases with physical stimulation. Therefore, even when the amount of biomarkers in the blood is small, such as in early-stage cancer, or when the measured biomarker amount is near the discrimination borderline, discrimination accuracy can be ensured and the risk of false negatives can be reduced. This reduces the risk of missing small lesions and enables early detection of malignant tumors, for which marker values ​​tend to increase with physical stimulation. Furthermore, the liquid biopsy result obtained when the diagnostic index increases contains information about the lesion at the target site where physical stimulation was applied. Therefore, for example, when using the compression pressure during mammography, it can also contribute to differentiation from cancers other than breast cancer. Accordingly, the diagnostic support according to this embodiment can appropriately support diagnosis by liquid biopsy.

[0084] Furthermore, if the initial assessment is positive or the intensity of the physical stimulus is low, a secondary assessment is not performed, thus reducing the cost associated with the secondary assessment. In addition, if the medical data includes the patient's family history, a secondary assessment may be performed if the patient is determined to be high-risk based on that family history.

[0085] (Second embodiment) In the diagnostic support according to the above embodiment, the judgment function 115 may determine a positive (+) result in the secondary judgment even if the primary judgment is negative, if the marker value increases after physical stimulation. In other words, in the diagnostic support according to this embodiment, even if the liquid biopsy is below the reference value, a positive result is determined if there is a significant increase in the marker value after physical stimulation. With this configuration, it is possible to improve the detection sensitivity for highly invasive, i.e., highly malignant cancers, where the absolute amount of biomarkers detected is small, such as when the lesion is small, but the marker value increases due to physical stimulation such as compression.

[0086] (Third embodiment) Furthermore, in the diagnostic support according to the embodiments described above, image diagnosis based on medical images is not required. Specifically, the primary and secondary assessments may be performed based solely on the results of the liquid biopsy. More specifically, in the diagnostic support according to this embodiment, if the liquid biopsy result is not positive in the primary assessment, a secondary assessment is performed according to the assessment index of the marker values ​​in the liquid biopsy before and after mammography. Even with this configuration, the same effects as in the embodiments described above can be obtained.

[0087] (Fourth embodiment) In addition, in the diagnostic support according to each embodiment described above, the judgment function 115 may perform a secondary judgment if the liquid biopsy is determined to be negative (-) even if the imaging diagnosis is determined to be positive (+). For example, in the processing of S204 in Figure 4, the judgment function 115 determines whether the liquid biopsy is positive or negative. With this configuration, false positives in imaging diagnosis can be reduced in examinations that use imaging diagnosis and liquid biopsy in combination.

[0088] (Fifth embodiment) In the embodiments described above, diagnostic support for determining the presence or absence of a lesion has been illustrated, but the invention is not limited to these. In the diagnostic support according to the embodiments described above, a determination regarding the nature of the lesion may be made instead of, or in addition to, the determination regarding the presence or absence of a lesion. Here, the nature of the lesion refers to, for example, the invasiveness or malignancy of the lesion.

[0089] As an example, in the detection function 113, the processing circuit 11 detects at least one of the presence or absence and nature of the lesion based on the medical image obtained by photographing the subject. The CAD according to this embodiment automatically detects the presence or absence of a lesion and the location of a lesion on a medical image based on predetermined detection conditions, or generates information regarding a qualitative diagnosis based on diagnostic criteria for the automatically detected lesion. The diagnostic criteria are predetermined in accordance with guidelines established by hospitals or academic societies and stored in the memory circuit 13 or the like. The CAD may also perform processing related to lesions manually entered by an examiner such as a physician.

[0090] As an example, in the judgment function 115, the processing circuit 11 makes a judgment regarding at least one of the presence or absence and nature of a lesion based on the marker values ​​before and after the application of physical stimulation. The processing circuit 11 also makes a judgment regarding at least one of the presence or absence and nature of a lesion based on the results of image diagnosis such as CAD.

[0091] For example, in the judgment function 115, if the processing circuit 11 is not judged positive in the primary judgment, it calculates a judgment index related to the marker value before and after physical stimulation, and performs a secondary judgment regarding at least one of the presence or absence and nature of a lesion based on the calculated judgment index. For example, in the secondary judgment, the processing circuit 11 determines that a lesion is present when the judgment index is greater than a predetermined threshold. For example, in the secondary judgment, the processing circuit 11 determines that the lesion is highly malignant when the judgment index is greater than a predetermined threshold. This is based on the fact that cancers in which biomarkers easily leak into the bloodstream due to breast compression are cancers that are highly invasive, leaking a lot into blood vessels and lymphatic vessels, such as those close to blood vessels and lymphatic vessels, or cancers that grow actively with a lot of neovascularization, have a high risk of metastasis, and are highly malignant. For example, in the secondary judgment, the processing circuit 11 determines that the lesion is less malignant when the judgment index is below a predetermined threshold. This is based on the fact that calcified lesions with minimal biomarker leakage are often non-invasive cancers that remain within the milk ducts and are therefore less malignant.

[0092] Thus, the diagnostic support according to this embodiment makes a determination regarding at least one of the presence or absence and nature of a lesion based on the marker values ​​before and after the application of physical stimulation. With this configuration, it is possible to determine the malignancy and invasiveness of the lesion, i.e., the nature of the lesion, based on the increase in marker values ​​accompanying physical stimulation. Furthermore, based on the increase in marker values ​​accompanying physical stimulation, it is possible to obtain information about the microenvironment (nature) of the lesion, such as whether the lesion is close to blood vessels or lymphatic vessels.

[0093] (Sixth embodiment) In addition, in the diagnostic support according to the embodiments described above, the collection of liquid samples after physical stimulation and liquid biopsy may not be performed.

[0094] For example, liquid sample collection and liquid biopsy after physical stimulation are performed if the primary assessment is not positive, or if the intensity of the physical stimulation exceeds a predetermined threshold. In other words, in the acquisition function 111, the processing circuit 11 acquires the results of the liquid biopsy performed after physical stimulation if the primary assessment is not positive, or if the intensity of the physical stimulation exceeds a predetermined threshold. Then, in the judgment function 115, the processing circuit 11 calculates a judgment index once the results of the liquid biopsy performed after physical stimulation have been acquired, and performs a secondary judgment based on the calculated judgment index.

[0095] With this configuration, if the initial test is positive or the intensity of the physical stimulus is low, not only is the secondary test not performed, but also the collection of liquid samples after the physical stimulus and liquid biopsy are not performed, thus reducing the cost of the test and the burden on the patient.

[0096] In the above description, the term "processor" refers to circuits such as CPUs, GPUs, ASICs, and Programmable Logic Devices (PLDs). PLDs include Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs). A processor functions by reading and executing programs stored in memory circuits. The memory circuit storing the program is a computer-readable, non-temporary recording medium. Alternatively, instead of storing the program in a memory circuit, the processor may be configured to directly incorporate the program into its circuitry. In this case, the processor functions by reading and executing the program incorporated into the circuitry. Furthermore, instead of executing the program, the processor may implement the function corresponding to the program through a combination of logic circuits. In this embodiment, each processor is not limited to being configured as a single circuit; multiple independent circuits may be combined to form a single processor, and its functions may be implemented from there. Additionally, the multiple components shown in Figure 2 may be integrated into a single processor to implement its functions.

[0097] According to at least one embodiment described above, liquid biopsy can be adequately supported in diagnosis.

[0098] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents.

[0099] With respect to the above embodiments, the following additional notes are disclosed as aspects of the invention and selective features.

[0100] (Note 1-1) An acquisition unit that acquires a first measurement value of the biomarker in a first liquid sample taken from the subject before applying physical stimulation, and a second measurement value of the biomarker in a second liquid sample taken from the subject after applying the physical stimulation, A determination unit that makes a determination regarding at least one of the presence or absence and nature of a lesion based on the first measurement value and the second measurement value. A diagnostic support device equipped with the following features.

[0101] (Appendix 1-2) The aforementioned diagnostic support device, A measuring device that measures the amount of the biomarker in the first liquid sample to obtain the first measurement value, and measures the amount of the biomarker in the second liquid sample to obtain the second measurement value, A diagnostic support system that includes this.

[0102] (Appendix 1-3) Before applying physical stimulation, a first liquid sample is taken from the subject, After applying the aforementioned physical stimulus, a second liquid sample is taken from the subject, To obtain a first measurement of the biomarker in the first liquid sample and a second measurement of the biomarker in the second liquid sample, Based on the first and second measurements, a determination is made regarding at least one of the presence or absence and nature of a lesion. Diagnostic support methods, including those mentioned above.

[0103] (Appendix 1-4) Obtaining a first measurement of the biomarker in a first liquid sample taken from the subject before applying physical stimulation, and a second measurement of the biomarker in a second liquid sample taken from the subject after applying the physical stimulation, Based on the first and second measurements, a determination is made regarding at least one of the presence or absence and nature of a lesion. A program that causes a computer to execute something.

[0104] (Note 2) A determination index may be calculated which is at least one of the increase amount and the rate of increase for the second measurement relative to the first measurement, and the determination may be made based on the calculated determination index.

[0105] (Note 3) If the aforementioned determination index is greater than a predetermined threshold, the determination conditions related to the determination may be changed.

[0106] (Note 4) Information regarding the intensity of the aforementioned physical stimulus may be obtained. The judgment index may be calculated when the intensity of the physical stimulus is greater than a predetermined threshold.

[0107] (Note 5) The aforementioned physical stimulus may be breast compression during mammography.

[0108] (Note 6) The physical stimulus may be ultrasound irradiation to the subject.

[0109] (Note 7) Based on the medical images obtained by photographing the subject, at least one of the presence or absence and nature of the lesion may be detected. Further acquisition of the aforementioned medical images is also possible. The determination may be made based on the results of the detection described above.

[0110] (Note 8) A determination index is calculated which is at least one of the increase amount and the rate of increase for the second measurement value relative to the first measurement value. If the determination index is greater than a predetermined threshold, the detection conditions related to the detection may be changed.

[0111] (Note 9) The change in the detection conditions may involve changing the detection algorithm, which detects at least one of the presence or absence and nature of a lesion from the medical image, to a detection algorithm that corresponds to the type of biomarker that increased after the physical stimulus.

[0112] (Note 10) The aforementioned lesions may include breast cancer, liver cancer, kidney cancer, prostate cancer, liver cancer, pancreatic cancer, bladder cancer, colorectal cancer, stomach cancer, esophageal cancer, uterine cancer, skin cancer, or lung cancer.

[0113] (Note 11) The aforementioned determination is, A first determination is made in which the presence of the lesion is determined when the first measurement value or the second measurement value is greater than a predetermined threshold, A second determination is made in which the presence of the lesion is determined when the aforementioned determination index is greater than a predetermined threshold. It may include. If the lesion is not determined to be present in the first determination, the determination index may be calculated, and the second determination may be made based on the determination index.

[0114] (Note 12) The aforementioned determination is, A first determination is made in which the presence of the lesion is determined when the first measurement value or the second measurement value is greater than a predetermined threshold, A second determination is made in which the lesion is judged to be highly malignant when the aforementioned determination index is greater than a predetermined threshold. It may include. If the lesion is not determined to be present in the first determination, the determination index may be calculated, and the second determination may be made based on the determination index.

[0115] (Note 13) The determination may include a first determination in which the presence of the lesion is determined when the first measured value is greater than a predetermined threshold. If the lesion is not determined to be present in the first determination, the second measurement value may be obtained. If the presence of the lesion is not determined in the first determination, the determination index may be calculated, and a second determination may be made regarding at least one of the presence or absence and nature of the lesion based on the calculated determination index. [Explanation of symbols]

[0116] 1. Diagnostic support system 9 Network 10. Diagnostic support devices (medical information processing devices) 11 Processing Circuit 13 Memory circuit 15 Communication Interfaces 17. Input Interface (Input Section) 19. Display (Display Unit) 30 Medical imaging diagnostic equipment 50 HIS 70 RIS 90 PACS 111 Acquisition function (acquisition part) 113 Detection function (detection unit) 115 Judgment function (judgment section) 117 Display control function (display control unit)

Claims

1. An acquisition unit that acquires a first measurement value of a biomarker in a first liquid sample taken from a subject before breast compression in mammography, a second measurement value of the biomarker in a second liquid sample taken from the subject after breast compression, and a medical image obtained from mammography, A detection unit that detects at least one of the presence or absence and characteristics of a lesion based on the aforementioned medical image, Based on the detection results, a determination unit makes a determination regarding at least one of the presence or absence and nature of the lesion, based on the first and second measured values. Equipped with, The aforementioned lesion is breast cancer. The biomarker is ctDNA (Circulating Tumor DNA), The determination unit calculates a determination index which is at least one of the increase amount and the increase rate of the second measurement relative to the first measurement, and changes the detection conditions related to the detection when the determination index is greater than a predetermined threshold. Diagnostic support device.

2. The diagnostic support device according to claim 1, wherein the determination unit performs the determination based on the determination index.

3. The diagnostic support device according to claim 2, wherein the determination unit changes the determination conditions related to the determination when the determination index is greater than a predetermined threshold.

4. The acquisition unit acquires information regarding the intensity of compression of the breast, The determination unit calculates the determination index when the intensity of the breast compression is greater than a predetermined threshold. The diagnostic support device according to claim 2 or claim 3.

5. The diagnostic support device according to claim 1, wherein the change in the detection conditions is to change the detection algorithm that detects at least one of the presence or absence and nature of the lesion from the medical image to a detection algorithm that corresponds to the ctDNA.

6. The aforementioned determination is, A first determination is made in which the presence of the lesion is determined when the first measurement value or the second measurement value is greater than a predetermined threshold, A second determination is made in which the presence of the lesion is determined when the aforementioned determination index is greater than a predetermined threshold. Includes, If the determination unit does not determine that the lesion is present in the first determination, it calculates the determination index and performs the second determination based on the determination index. The diagnostic support device according to claim 2 or claim 3.

7. The aforementioned determination is, A first determination is made in which the presence of the lesion is determined when the first measurement value or the second measurement value is greater than a predetermined threshold, A second determination is made in which the lesion is determined to be highly malignant when the aforementioned determination index is greater than a predetermined threshold. Includes, If the determination unit does not determine that the lesion is present in the first determination, it calculates the determination index and performs the second determination based on the determination index. The diagnostic support device according to claim 2 or claim 3.

8. The determination includes a first determination in which the presence of the lesion is determined when the first measured value is greater than a predetermined threshold, If the lesion is not determined to be present in the first determination described above, The acquisition unit acquires the second measurement value, The determination unit calculates the determination index and makes a second determination regarding at least one of the presence or absence and nature of the lesion based on the calculated determination index. The diagnostic support device according to claim 2 or claim 3.

9. A diagnostic support device comprising at least one processor and at least one memory, wherein the at least one processor enables: To obtain a first measurement value of the biomarker in a first liquid sample taken from the subject before breast compression during mammography, a second measurement value of the biomarker in a second liquid sample taken from the subject after breast compression, and a medical image obtained from the mammography. Based on the aforementioned medical images, at least one of the following is detected: the presence or absence and the nature of the lesion. Based on the detection results, a determination is made regarding at least one of the presence or absence and nature of the lesion, based on the first and second measurement values. Includes, The aforementioned lesion is breast cancer. The biomarker is ctDNA (Circulating Tumor DNA), The method further includes calculating a determination index which is at least one of the increase amount and the rate of increase of the second measurement with respect to the first measurement, and changing the detection conditions related to the detection when the determination index is greater than a predetermined threshold. Diagnostic support methods.

10. To obtain a first measurement value of a biomarker in a first liquid sample taken from a subject before breast compression during mammography, a second measurement value of the biomarker in a second liquid sample taken from the subject after breast compression, and a medical image obtained from the mammography, Based on the aforementioned medical images, at least one of the following is detected: the presence or absence and the nature of the lesion. Based on the detection results, a determination is made regarding at least one of the presence or absence and nature of the lesion, based on the first and second measurement values. A program that causes a computer to execute, The aforementioned lesion is breast cancer. The biomarker is ctDNA (Circulating Tumor DNA), The method further includes calculating a determination index which is at least one of the increase amount and the rate of increase of the second measurement with respect to the first measurement, and changing the detection conditions related to the detection when the determination index is greater than a predetermined threshold. program.

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