Information processing device, information processing method, and program

Abstract

This information processing device 1 comprises: a storage unit 12 which stores a prognosis estimation model that has been trained to output the prognosis of a patient upon receiving input data including a spatial distribution of at least one among prescribed proteins and feature amounts pertaining to prescribed biomarkers in specimens collected from the patient; an acquisition unit 131 which acquires a spatial distribution of at least one among prescribed proteins and feature amounts pertaining to prescribed biomarkers in the specimens collected from the target patient; and a prognosis estimation unit 132 which outputs, as the estimated values of the prognosis of the target patient, information output by inputting, to the prognosis estimation model, input data including the spatial distribution acquired by the acquisition unit 131.

Description

Information processing device, information processing method, and program

[0001] The present invention relates to an information processing device, an information processing method, and a program.

[0002] It is known that CD4-positive and CD8-positive T-cell lymphocytes in particular have an impact on cancer prognosis (see, for example, Non-Patent Document 1), and research is being conducted to predict prognosis using these lymphocytes.

[0003] Rebecca Hoesli, et al.: “Proportion of CD4 and CD8 tumor infiltrating lymphocytes predicts survival in persistent / recurrent laryngeal squamous cell carcinoma”, Oral Oncology, Vol.77, pp 83-89(Feb 2018,)

[0004] In the prior art, there was a problem in that the information available for prognosis prediction was limited, and therefore the improvement in prediction accuracy was also limited.

[0005] The present invention has been made in view of these points, and aims to provide a method for improving the accuracy of disease prognosis prediction.

[0006] An information processing device according to a first aspect of the present invention includes a memory unit that stores a prognosis estimation model trained to output the prognosis of a patient when input data including the spatial distribution of at least one of features related to a predetermined biomarker and a predetermined protein in a sample collected from the patient is input; an acquisition unit that acquires the spatial distribution of at least one of features related to a predetermined biomarker and a predetermined protein in a sample collected from the target patient; and a prognosis estimation unit that inputs the input data including the spatial distribution acquired by the acquisition unit into the prognosis estimation model and outputs the information output as an estimated value of the prognosis of the target patient.

[0007] The memory unit may store the prognosis estimation model that has been trained using drugs administered to the patient as additional input, the acquisition unit may further acquire drugs to be administered to the target patient, and the prognosis estimation unit may further input the drugs to be administered to the target patient acquired by the acquisition unit into the prognosis estimation model, and output the output information as an estimated value of the prognosis of the target patient.

[0008] The spatial distribution of the feature amount related to the predetermined biomarker may be the spatial distribution of the feature amount related to high frequency microsatellite instability or BRAF gene mutation in the sample collected from the patient.

[0009] The storage unit may further store a distribution estimation model that has been trained to output a spatial distribution of features related to the specified biomarker in the image data when image data of a sample is input, the acquisition unit may acquire image data of the sample collected from the target patient, and the information processing device may further have a distribution generation unit that inputs the image data of the sample acquired by the acquisition unit into the distribution estimation model and generates the spatial distribution.

[0010] The spatial distribution of the predetermined protein may be the spatial distribution of tumor tissue and CD3-positive lymphocytes or CD20-positive lymphocytes in a specimen collected from the patient.

[0011] The memory unit may store the prognosis prediction model that has been trained using the spatial distribution of tumor tissue and a specified protein in a specimen collected from a patient as an additional input, the acquisition unit may further acquire the spatial distribution of tumor tissue and a specified protein in a specimen collected from the target patient, and the prognosis prediction unit may input input data that further includes the spatial distribution of tumor tissue and a specified protein acquired by the acquisition unit into the prognosis prediction model, and output the information output as an estimated value of the prognosis of the target patient.

[0012] The acquisition unit acquires image data of a specimen collected from the subject patient, including first specimen image data, which is image data of the specimen that has been subjected to a predetermined processing so as to enable detection of the cellular or tissue structure in the specimen, and second specimen image data, which is image data of the specimen that has been subjected to a predetermined processing so as to enable detection of a predetermined protein in the specimen, and the information processing device may further include a distribution generation unit that generates a spatial distribution of tumor tissue and a predetermined protein in the specimen based on the first specimen image data and the second specimen image data acquired by the acquisition unit.

[0013] The first sample image data and the second sample image data are image data obtained by staining a sample taken from the patient using different methods, and the information processing device may further have a registration unit that associates positions in the first sample image data with positions in the second sample image data, and the distribution generation unit may generate a spatial distribution of tumor tissue and a predetermined protein in the sample based on the first sample image data and the second sample image data that have been associated by the registration unit.

[0014] The memory unit may store the prognosis estimation model that has been trained using as an additional input the spatial distribution of features related to tumor tissue in a specimen collected from a patient, the acquisition unit may further acquire the spatial distribution of features related to tumor tissue in a specimen collected from the target patient, and the prognosis estimation unit may input input data that further includes the spatial distribution of features related to tumor tissue acquired by the acquisition unit into the prognosis estimation model, and output the information output as an estimated value of the prognosis of the target patient.

[0015] An information processing method according to a second aspect of the present invention includes the steps of: acquiring, by a computer, the spatial distribution of at least one of a feature related to a predetermined biomarker and a predetermined protein in a sample collected from a target patient; and inputting input data including the spatial distribution acquired in the acquiring step into a prognosis estimation model stored in a memory unit, and outputting the output information as an estimated value of the prognosis of the target patient.

[0016] In the program of the third aspect of the present invention, a computer is caused to execute the steps of acquiring the spatial distribution of at least one of a feature related to a predetermined biomarker and a predetermined protein in a sample collected from a target patient, and inputting input data including the spatial distribution acquired in the acquiring step into a prognosis estimation model stored in a memory unit, and outputting the output information as an estimated value of the prognosis of the target patient.

[0017] The present invention has the effect of providing a method for improving the accuracy of disease prognosis prediction.

[0018] FIG. 1 is a diagram for explaining an overview of processing in an information processing device 1 according to an embodiment. FIG. 2 is a block diagram showing the configuration of the information processing device 1. FIG. 3 is a diagram showing an example of processing in a distribution generation unit 133. FIG. 4 is a flowchart showing the flow of processing in the information processing device 1. FIG. 5 is a diagram for explaining an overview of processing in an information processing device 1 according to a first modified example. FIG. 6 is a diagram for explaining an overview of processing in an information processing device 1 according to a second modified example.

[0019] 1 is a diagram illustrating an overview of processing in an information processing device 1 according to an embodiment. The information processing device 1 is a device for estimating the prognosis of a patient to be evaluated based on the spatial distribution of predetermined indices in a sample collected from the patient. The information processing device 1 is, for example, a server or a personal computer.

[0020] The information processing device 1 inputs input information including a spatial distribution D1 to a prognosis estimation model M1 and outputs a prognosis estimation value D2. The spatial distribution D1 is information that spatially indicates the extent to which features related to a predetermined biomarker, tumor tissue, or a predetermined type of lymphocyte (protein), etc. are distributed in image data obtained by capturing an image of a specimen collected from a patient to be estimated.

[0021] The prognosis estimation model M1 is a trained model that has been trained using the spatial distribution of biomarkers, tumor tissue, a predetermined type of lymphocyte, etc. in a specimen as training data. When the spatial distribution D1 of a patient to be assessed is input, the prognosis estimation model M1 outputs an estimated prognosis value D2. The prognosis estimation model M1 may output the estimated prognosis value D2 based on the spatial distribution of multiple indices, or may output the estimated prognosis value D2 based on other input data in addition to the spatial distribution.

[0022] The prognosis estimate D2 is an estimate that indicates the prognosis of the patient being estimated. As an example, the prognosis estimate D2 indicates whether the probability of survival for a predetermined period from the time the sample is obtained is equal to or greater than a predetermined threshold. FIG. 1 shows an example in which the prognosis estimate model M1 outputs, as the prognosis estimate D2, "High" if the probability of the target patient surviving for a predetermined period is equal to or greater than a predetermined threshold, and "Low" if the probability is less than the predetermined threshold. The prognosis estimate D2 may indicate a period during which the probability of survival of the target patient from the time the sample is obtained is estimated to be equal to or greater than a predetermined threshold.

[0023] The information processing device 1 can use the spatial distribution of biomarkers or predetermined proteins in a sample to predict the prognosis of a patient, thereby achieving the effect of improving the accuracy of disease prognosis prediction compared to existing prediction techniques.

[0024] 2 is a block diagram showing the configuration of the information processing device 1. The information processing device 1 has a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 has an acquisition unit 131, a prognosis estimation unit 132, a distribution generation unit 133, a registration unit 134, and a learning unit 135.

[0025] The communication unit 11 is a communication interface for transmitting and receiving data to and from other devices via a network. The storage unit 12 is a storage medium including a read-only memory (ROM), a random access memory (RAM), a solid-state drive (SSD), a hard disk drive, etc. The storage unit 12 stores in advance a program to be executed by the control unit 13.

[0026] The memory unit 12 stores a prognosis prediction model M1 that has been trained to output a prognosis for a patient when input data including the spatial distribution of at least one of features related to a predetermined biomarker and a predetermined protein in a sample collected from the patient is input. The prognosis prediction model M1 is a trained model that has been trained using the spatial distribution of the biomarker or protein in the sample collected from the patient and the prognosis of the patient as training data. The prognosis prediction model M1 has been trained using the features related to the predetermined biomarker and the predetermined protein corresponding to the spatial distribution acquired by the acquisition unit 131 as training data.

[0027] The control unit 13 is a processor such as a CPU (Central Processing Unit), and functions as an acquisition unit 131, a prognosis estimation unit 132, a distribution generation unit 133, a registration unit 134, and a learning unit 135 by executing a program stored in the storage unit 12.

[0028] The acquisition unit 131 acquires the spatial distribution of at least one of feature amounts related to a predetermined biomarker and a predetermined protein in a sample collected from a target patient. The acquisition unit 131 may acquire the spatial distribution of either the feature amounts related to the predetermined biomarker or the predetermined protein, or may acquire the spatial distribution of both. The acquisition unit 131 may acquire the spatial distribution of feature amounts related to the predetermined biomarker from an external device (not shown). The acquisition unit 131 may acquire the spatial distribution generated by image analysis of image data of the acquired sample, as described below.

[0029] The predetermined biomarker may be, for example, high-frequency microsatellite instability or a BRAF gene mutation, but is not limited thereto. The predetermined biomarker may also be low-frequency microsatellite instability, KRAS, SYNE1 (Spectrin Repeat Containing Nuclear Envelope Protein 1), APC (antigen-presenting cells), TP53, TTN, or the like. The acquisition unit 131 may acquire the spatial distribution of each of multiple types of biomarkers. The feature amount related to the predetermined biomarker may be the distribution of the biomarker itself, or may be the distribution of information (e.g., attention weight) indicating the contribution of the biomarker expression level to the estimation result in a machine learning model that estimates the expression level of the biomarker from input image data of a sample to be determined.

[0030] The predetermined protein is, for example, but not limited to, CD3-positive lymphocytes or CD20-positive lymphocytes. The predetermined protein may also be CD4-positive lymphocytes, CD8-positive lymphocytes, Foxp3, PD-1, CD163Ave, CD155, etc. The acquisition unit 131 may acquire the spatial distribution of each of multiple types of proteins.

[0031] The prognosis estimation unit 132 inputs the input data including the spatial distribution acquired by the acquisition unit 131 into the prognosis estimation model M1, and outputs the output information as an estimated value of the prognosis of the target patient. By configuring the information processing device 1 in this way, the spatial distribution of a biomarker or a predetermined protein in a sample can be used for prediction, which has the effect of improving the accuracy of disease prognosis prediction compared to existing prediction techniques.

[0032] [Biomarker Distribution Estimation] The information processing device 1 may be configured to generate a spatial distribution of features related to a predetermined biomarker in a sample based on image data of the sample. FIG. 3 is a diagram showing an example of a process performed by the distribution generation unit 133 to estimate the spatial distribution of features related to a biomarker. First, a learning process for estimating the spatial distribution of features related to a biomarker will be described. In the learning process, the acquisition unit 131 acquires image data P11 of the sample and a correct label L assigned to the image data as training data. As an example, MIL (Multiple Instance Learning) may be used in the learning process. The correct label L is quantitative or qualitative information related to a biomarker in the entire sample to be imaged. As an example, the correct label L is information indicating the degree of microsatellite instability for the entire sample.

[0033] The acquisition unit 131 divides the acquired image data P11 of the sample into tiles. The learning unit 135 inputs multiple image data P12 obtained by dividing the image data P11 into a distribution estimation model M2 and outputs a classification result R1. The classification result R1 is information corresponding to the correct label L and is a value estimated by the distribution estimation model M2 based on the multiple image data P12. The learning unit 135 feeds back the difference between the output classification result R1 and the correct label L to the distribution estimation model M2 and updates the parameters of the distribution estimation model M2. The learning unit 135 repeats the above process until a condition for terminating learning is met, and stores the trained distribution estimation model M2 in the memory unit 12. As a result, when image data of a sample is input, the memory unit 12 stores the distribution estimation model M2 that has been trained to output the spatial distribution of features related to a predetermined biomarker in the image data.

[0034] Next, the inference process will be described. The acquisition unit 131 acquires image data P13 of a sample collected from a patient to be inferred. The distribution generation unit 133 inputs the image data of the sample acquired by the acquisition unit 131 into a distribution estimation model M2 to generate a spatial distribution. Specifically, the distribution generation unit 133 divides the acquired image data P13 into tiles and inputs the tiles into the distribution estimation model M2. The distribution generation unit 133 acquires Attention Weight (A) when the distribution estimation model M2 estimates the classification result R2 of the image data P13. The Attention Weight (A) is a value indicating the degree to which each part of the image data contributed to the classification when classifying the image data. The Attention Weight (A) value generated in this manner indicates the degree of contribution to the inference corresponding to the position in image space, and can therefore be used as the spatial distribution of features related to the biomarker.

[0035] By configuring the information processing device 1 in this manner, it is possible to generate a spatial distribution of features related to a specified biomarker in a sample, and compared to existing prediction techniques, it becomes possible to make highly accurate prognosis predictions using the spatial distribution of features related to the specified biomarker.

[0036] [Generation of Spatial Distribution of Protein] Next, the process of generating the spatial distribution of a predetermined protein will be described with reference to FIG. 4 . The acquisition unit 131 acquires first sample image data P21 and second sample image data P22 of a sample collected from a patient to be estimated. The first sample image data P21 is image data of a sample collected from a patient to be estimated, processed (e.g., stained) using a predetermined method so that the cellular or tissue structure can be detected, and then imaged. The second sample image data P22 is image data of a sample collected from a patient to be estimated, processed using a predetermined method so that a predetermined protein in the sample can be detected, and then imaged. As an example, the image data P21 and the image data P22 are image data generated by slicing a collected sample so that the cross sections are parallel and have a uniform thickness, staining the sliced ​​sample using a predetermined method, and then imaging the cross sections of the sample. The first sample image data P21 and the second sample image data P22 may be imaged using different staining methods. The method of staining the specimen is, for example, HE staining for the first specimen image data P21 and IHC staining for the second specimen image data P22, but is not limited to this. In the first specimen image data P21 and the second specimen image data P22, images of adjacent cross sections before the specimen is sliced ​​are captured.

[0037] The registration unit 134 associates corresponding positions in the image data with each other. The registration unit 134 associates positions in the first sample image data P21 with positions in the second sample image data P22, both acquired by the acquisition unit 131. As an example, the registration unit 134 associates the image data with each other by using a known non-rigid registration to convert one of the image data so that pixels in one image data match corresponding pixels in the other image data.

[0038] The memory unit 12 stores a tumor region extraction model M31 and a protein extraction model M32. The tumor region extraction model M31 is a trained model that has been trained to output a tumor region occurring in the specimen imaged in the first specimen image data P21 when the first specimen image data P21 is input. The learning unit 135 trains the tumor region extraction model M31 in advance using the first specimen image data and the tumor region for training as training data.

[0039] The protein extraction model M32 is a trained model that has been trained so that, when second sample image data P22 is input, it outputs a region in which a predetermined protein is expressed in the sample imaged in the image data. The training unit 135 trains the protein extraction model M32 in advance using the second sample image data for training and the region in the image data in which the predetermined protein is expressed as training data.

[0040] Based on the first and second sample image data acquired by the acquisition unit 131, the distribution generation unit 133 generates a spatial distribution of tumor tissue and a predetermined protein in the sample. Specifically, the distribution generation unit 133 inputs the first and second sample image data P21 and P22 acquired by the acquisition unit 131 into a tumor region extraction model M31 and a protein extraction model M32, respectively, and outputs a tumor region D11 and a region D12 in which the predetermined protein is expressed. The tumor region D11 and the region D12 in which the predetermined protein is expressed output by the tumor region extraction model M31 and the protein extraction model M32, respectively, correspond to positions in the image data. Therefore, the tumor region D11 and the region D12 in which the predetermined protein is expressed respectively indicate the spatial distribution of the tumor tissue and the predetermined protein in the image data. The distribution generation unit 133 outputs the output spatial distribution of the tumor tissue and the predetermined protein to the acquisition unit 131.

[0041] The storage unit 12 stores a prognosis prediction model M1 trained using as an additional input the spatial distribution of tumor tissue and a predetermined protein in a specimen collected from a patient. The acquisition unit 131 further acquires the spatial distribution of tumor tissue and a predetermined protein in a specimen collected from a target patient. The acquisition unit 131 may acquire the spatial distribution of tumor tissue and a predetermined protein generated by the distribution generation unit 133 based on first specimen image data and second specimen image data obtained by capturing an image of the specimen of the target patient.

[0042] The prognosis estimation unit 132 inputs the input data further including the spatial distribution of tumor tissue acquired by the acquisition unit 131 into the prognosis estimation model M1, and outputs the output information as an estimated value of the prognosis of the target patient. By configuring the information processing device 1 in this way, it becomes possible to predict the prognosis using information that associates the distribution of tumor tissue with the distribution of a predetermined protein, making it possible to make highly accurate predictions.

[0043] [Processing Flow in Information Processing Device 1] Fig. 5 is a flowchart showing an example of the processing flow in the information processing device 1. The flowchart shown in Fig. 5 starts when an instruction to start estimation processing is received from an external device.

[0044] The acquisition unit 131 acquires image data of a plurality of samples (S01). The registration unit 134 registers and associates each of the acquired image data (S02).

[0045] The distribution generation unit 133 generates a spatial distribution of features related to a predetermined biomarker based on the acquired image data (S03), a spatial distribution of a tumorous region based on the acquired image data (S04), and a spatial distribution of a predetermined protein based on the acquired image data (S05).

[0046] The prognosis estimation unit 132 inputs each spatial distribution generated by the distribution generation unit 133 to the prognosis estimation model M1 (S06). The prognosis estimation unit 132 outputs the estimated value output by the prognosis estimation model M1 as the prognosis estimated value (S07). The information processing device 1 then ends the process.

[0047] <Modification 1> In the above explanation, an example of predicting the prognosis of a patient with a predetermined disease has been described, but the information processing device 1 may also be configured as a device that estimates whether a predetermined drug is effective for a patient with a predetermined disease. In the following, the same reference numerals as those already described will be used, and descriptions thereof will be omitted.

[0048] Fig. 6 is a diagram showing an overview of the processing of the information processing device 1 according to Modification 1. The information processing device 1 according to Modification 1 differs from the information processing device 1 shown in Fig. 1 in that it further acquires drug information D3 and inputs the acquired drug information into a prognosis estimation model M11 to obtain an estimated prognosis value D2.

[0049] Specifically, the acquisition unit 131 further acquires drugs to be administered to the target patient. For example, the acquisition unit 131 acquires drug information D3 indicating drugs to be administered to the patient from an external device (not shown). The drug information D3 may be information indicating one type of drug, or may be information indicating multiple types of drugs. Furthermore, the drug information D3 may be information including the type of drug and information indicating the usage, dosage, etc. of the drug to be administered.

[0050] The storage unit 12 may store a prognosis estimation model M11 that has been trained using a drug administered to the patient as an additional input. That is, in this case, the prognosis estimation model M11 stored in the storage unit 12 is a trained model that has been trained using, as training data, the spatial distribution of features, etc. related to predetermined biomarkers in a sample from a patient for training, drug information indicating the drug administered to the patient, and information indicating the prognosis of the patient. When the prognosis estimation model M11 stored in the storage unit 12 receives as input the spatial distribution of features, etc. related to predetermined biomarkers in a sample collected from the patient to be assessed and drug information D3 indicating the drug administered to the patient, it outputs a prognosis estimate D2 indicating the prognosis of the patient.

[0051] The prognosis estimation unit 132 further inputs the drug information D3 indicating the drug to be administered to the target patient, which has been acquired by the acquisition unit 131, into the prognosis estimation model M11, and outputs the output information as an estimated prognosis value D2 for the target patient. The prognosis estimation unit 132 inputs, in addition to the spatial distribution, information indicating the drug to be administered to the patient, which has been acquired by the acquisition unit 131, into the prognosis estimation model M11 stored in the storage unit 12, and outputs the estimated prognosis value D2 output from the prognosis estimation model M11.

[0052] [Effects of Information Processing Device 1 According to Modification 1] By configuring the information processing device 1 in this way, it is possible to improve the accuracy of estimating whether or not a drug is effective for a patient with a predetermined disease.

[0053] <Modification 2> It is known that effective drugs vary depending on the heterogeneity of tumor tissue (the existence of various types of tissue). Therefore, by configuring the information processing device 1 to further input the distribution of features related to tumor tissue and predict the prognosis of a target patient, it is possible to make an estimation that takes into account the difference in prognosis due to the heterogeneity of tumor tissue.

[0054] 7 is a diagram showing an example of processing in the information processing device 1 according to the modified example. In this case, the storage unit 12 stores a prognosis estimation model M12 that has been trained using as an additional input the spatial distribution of features related to tumor tissue in a sample collected from a patient. The distribution generation unit 133 generates tumor region image data P32 based on the first sample image data P31 and the spatial distribution of the tumor region in the first sample image data P31. The tumor region image data P32 is image data that includes information on only the region of the first sample image data P31 where the tumor is present.

[0055] The distribution generation unit 133 inputs the tumor region image data P32 into the tumor region classification model M41 and outputs feature values ​​D21 for each sub-region (hereinafter referred to as a "patch") obtained by dividing the tumor region into small parts in the tumor region image data P32. The feature values ​​D21 are, for example, labels indicating the cell density of tumor cells or similar images on a patch-by-patch basis. The tumor region classification model M41 is a trained model trained by the learning unit 135 to output feature values ​​for each patch using the tumor region image data as training data.

[0056] The acquisition unit 131 acquires a spatial distribution of features related to tumor tissue in a specimen collected from a target patient. As an example, the acquisition unit 131 acquires feature D21 for each patch obtained by finely dividing the tumor region generated by the distribution generation unit 133 as the spatial distribution of features related to tumor tissue in the specimen collected from the target patient. The acquisition unit 131 may acquire the spatial distribution of features related to tumor tissue in the specimen collected from the target patient from an external device.

[0057] The prognosis estimation unit 132 inputs input data including the spatial distribution of features related to tumor tissue acquired by the acquisition unit 131 into the prognosis estimation model M12, and outputs the output information as an estimated value of the prognosis of the target patient. In addition to the spatial distribution of features related to tumor tissue, the prognosis estimation unit 132 may further input the spatial distribution of features related to a predetermined biomarker, etc., into the prognosis estimation model M12, and output an estimated prognosis value D2.

[0058] By configuring the information processing device 1, it is possible to make estimations that take into account differences in prognosis due to heterogeneity of tumor tissue.

[0059] The present invention has been described above using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. For example, all or part of the device can be configured by functionally or physically distributing or integrating in any unit. Furthermore, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effects of the new embodiments resulting from the combination also have the effects of the original embodiments.

[0060] REFERENCE SIGNS LIST 1 Information processing device 11 Communication unit 12 Storage unit 13 Control unit 131 Acquisition unit 132 Prognosis estimation unit 133 Distribution generation unit 134 Registration unit 135 Learning unit

Claims

1. A storage unit that stores a prognosis estimation model learned to output the prognosis of a patient when inputting input data including at least one of a feature amount related to a predetermined biomarker and a spatial distribution of a predetermined protein in a specimen collected from the patient; An acquisition unit that acquires at least one of a feature amount related to a predetermined biomarker and a spatial distribution of a predetermined protein in a specimen collected from a target patient; A prognosis estimation unit that outputs, as an estimated value of the prognosis of the target patient, information output by inputting the input data including the spatial distribution acquired by the acquisition unit into the prognosis estimation model; An information processing apparatus having the above.

2. The storage unit stores the prognosis estimation model learned with the drug administered to the patient as an additional input; The acquisition unit further acquires the drug to be administered to the target patient; The prognosis estimation unit further inputs the drug to be administered to the target patient acquired by the acquisition unit into the prognosis estimation model, and outputs the output information as an estimated value of the prognosis of the target patient. The information processing apparatus according to claim 1.

3. The spatial distribution of the feature amount related to the predetermined biomarker is the spatial distribution of the feature amount related to high-frequency microsatellite instability or BRAF gene mutation in the specimen collected from the patient. The information processing apparatus according to claim 1 or 2.

4. The storage unit further stores a distribution estimation model learned to output the spatial distribution of the feature amount related to the predetermined biomarker in the image data when inputting the image data of the specimen; The acquisition unit acquires the image data of the specimen collected from the target patient; The information processing apparatus further includes: A distribution generation unit that inputs the image data of the specimen acquired by the acquisition unit into the distribution estimation model and generates the spatial distribution. The information processing apparatus according to claim 1 or 2.

5. The spatial distribution of the predetermined protein is the spatial distribution between the tumor tissue and CD3-positive lymphocytes or CD20-positive lymphocytes in the specimen collected from the patient. The information processing apparatus according to claim 1 or 2.

6. The storage unit stores the prognosis estimation model learned with the spatial distribution between the tumor tissue and the predetermined protein in the specimen collected from the patient as an additional input; The acquisition unit further acquires the spatial distribution between the tumor tissue and the predetermined protein in the specimen collected from the target patient. The prognosis estimation unit outputs, as an estimated value of the prognosis of the target patient, information output by inputting input data further including the spatial distribution of the tumor tissue and a predetermined protein acquired by the acquisition unit into the prognosis estimation model. The information processing apparatus according to claim 1 or 2.

7. The acquisition unit acquires first specimen image data, which is image data of a specimen collected from the target patient and obtained by imaging the specimen that has been subjected to a predetermined process so as to detect the composition of cells or tissues in the specimen, and second specimen image data, which is image data of the specimen obtained by imaging the specimen that has been subjected to a predetermined process so as to detect a predetermined protein in the specimen. The information processing apparatus is further includes a distribution generation unit that generates a spatial distribution of a tumor tissue and a predetermined protein in the specimen based on the first specimen image data and the second specimen image data acquired by the acquisition unit. The information processing apparatus according to claim 6.

8. The first specimen image data and the second specimen image data are image data obtained by imaging specimens collected from the patient by different staining methods. The information processing apparatus is further includes a registration unit that associates a position in the first specimen image data with a position in the second specimen image data. The distribution generation unit generates a spatial distribution of a tumor tissue and a predetermined protein in the specimen based on the first specimen image data and the second specimen image data associated by the registration unit. The information processing apparatus according to claim 7.

9. The storage unit stores the prognosis estimation model learned by further using, as an input, the spatial distribution of feature amounts related to a tumor tissue in a specimen collected from a patient. The acquisition unit further acquires the spatial distribution of feature amounts related to a tumor tissue in a specimen collected from the target patient. The prognosis estimation unit outputs, as an estimated value of the prognosis of the target patient, information output by inputting input data further including the spatial distribution of the feature amounts related to the tumor tissue acquired by the acquisition unit into the prognosis estimation model. The information processing apparatus according to claim 1 or 2.

10. The prognosis estimation model is learned based on teacher data associating the spatial distribution of a biomarker or protein in a specimen collected from a patient with the prognosis of the patient indicating whether the patient has survived for a predetermined period from the time when the specimen was acquired. The information processing apparatus according to claim 1.

11. When the memory unit inputs the image data of the specimen collected from the patient, it is a distribution estimation model learned to output the spatial distribution of the feature amount related to the predetermined biomarker in the image data. The distribution estimation model is learned based on the teacher data associating the image data of the specimen for learning and the correct answer data indicating the degree of the predetermined biomarker in the whole specimen shown by the image data, and further stores the distribution estimation model, The acquisition unit further acquires the image data of the specimen collected from the target patient, The information processing apparatus further has a distribution generation unit that inputs the image data into the distribution estimation model and generates the spatial distribution, The information processing apparatus according to claim 1.

12. The memory unit, (1) A tumor region extraction model that is a learned model learned to output the tumor region occurring in the specimen imaged in the image data when the first specimen image data is input. The tumor region extraction model is learned based on the teacher data associating the specimen image data for learning and the information indicating the tumor region in the specimen image data, (2) A protein extraction model that is a learned model learned to output the information indicating the region where a predetermined protein is expressed in the specimen imaged in the image data when the second specimen image data is input. The protein extraction model is learned based on the teacher data associating the specimen image data for learning and the information indicating the region where a predetermined protein is expressed in the image data, further stores the, The acquisition unit further acquires the first specimen image data, which is the image data of the specimen collected from the target patient and obtained by imaging the specimen subjected to predetermined processing so as to detect the cell or tissue composition in the specimen, and the second specimen image data, which is the image data of the specimen collected from the target patient and obtained by imaging the specimen subjected to predetermined processing so as to detect a predetermined protein in the specimen, The information processing apparatus further has a distribution generation unit that inputs the first specimen image data and the second specimen image data acquired by the acquisition unit into the tumor region extraction model and the protein extraction model respectively, and generates the spatial distribution of the tumor tissue and the predetermined protein in the specimen based on the output results, The prognosis estimation unit outputs, as an estimated value of the prognosis of the target patient, information output by inputting input data including the spatial distribution of the tumor tissue and the spatial distribution of the predetermined protein into the prognosis estimation model. The information processing apparatus according to claim 1.

13. Performed by a computer A step of obtaining at least one of a feature amount related to a predetermined biomarker and a spatial distribution of a predetermined protein in a specimen collected from a target patient; A step of outputting, as an estimated value of the prognosis of the target patient, information output by inputting input data including the spatial distribution obtained in the obtaining step into a prognosis estimation model stored in a storage unit; An information processing method comprising:

14. Causing a computer to A step of obtaining at least one of a feature amount related to a predetermined biomarker and a spatial distribution of a predetermined protein in a specimen collected from a target patient; A step of outputting, as an estimated value of the prognosis of the target patient, information output by inputting input data including the spatial distribution obtained in the obtaining step into a prognosis estimation model stored in a storage unit; A program for causing the above to be executed.