Slide number estimation device, control method, and program

Abstract

A slide number estimation device (2000) obtains a slide image (30). The slide image (30) is an image of a specimen slide (20) obtained from a tissue piece (10) of a subject. The slide number estimation device (2000) uses the obtained slide image (30) to estimate the number of tumor cells contained in a region of interest of the specimen slide (20) corresponding to the slide image (30). The slide number estimation device (2000) estimates the number of specimen slides (20) to be obtained from the tissue piece (10) for conducting a predetermined test on the basis of the estimated number of tumor cells.

Description

[Technical field]

[0001] The present disclosure relates to techniques for tests performed using specimens taken from animals. [Background technology]

[0002] Tests are being conducted using specimens collected from humans and other animals. For example, Patent Document 1 discloses a method for conducting genetic testing of a fetus using blood from a pregnant woman that contains cell fragments derived from the pregnant woman and cell fragments derived from the fetus. Here, this document discloses a method for determining the number of slides to be used in the test, in which the distribution, expected value, and variance of the number of fetal-derived cells that can be collected from the slides and the cost of creating the slides are included in the fixed cost or variable cost. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-049078 A [Non-patent literature]

[0004] [Non-Patent Document 1] "Procedures for handling pathological tissue samples for genomic diagnosis," [online], March 1, 2018, Japanese Society of Pathology, Internet<URL:http: / / pathology.or.jp / genome_med / pdf / textbook.pdf> Summary of the Invention [Problem to be solved by the invention]

[0005] Patent Document 1 discloses a test using blood. Therefore, Patent Document 1 does not mention a method for determining the number of slides required for a test using a tissue slice such as a part of a tumor. The present invention has been made in consideration of this problem, and the purpose of the present invention is to provide a technology for improving the efficiency of a test using an animal tissue slice. [Means for solving the problem]

[0006] The slide number estimation device disclosed herein includes an acquisition means for acquiring a slide image, which is an image of a specimen slide obtained from a tissue slice of a subject, a first estimation means for estimating the number of tumor cells contained in a region of interest of the specimen slide using the slide image, and a second estimation means for estimating the number of specimen slides to be obtained from the tissue slice in order to perform a specified test based on the estimated number of tumor cells.

[0007] The control method of the present disclosure is executed by a computer and includes an acquisition step of acquiring a slide image, which is an image of a specimen slide obtained from a tissue slice of a subject, a first estimation step of estimating the number of tumor cells contained in a region of interest of the specimen slide using the slide image, and a second estimation step of estimating the number of the specimen slides to be obtained from the tissue slice for a predetermined test based on the estimated number of tumor cells.

[0008] The computer-readable medium of the present disclosure stores a program for causing a computer to execute the control method of the present disclosure. Effect of the Invention

[0009] According to the present disclosure, a technique for improving the efficiency of tests using animal tissue slices is provided. [Brief description of the drawings]

[0010] [Figure 1] FIG. 2 is a diagram illustrating an example of an outline of the operation of the number of slides estimation device of the first embodiment. [Diagram 2]1 is a block diagram illustrating a functional configuration of a number of slides estimation device according to a first embodiment. [Diagram 3] FIG. 2 is a block diagram illustrating a hardware configuration of a computer that realizes the number of slides estimation device. [Figure 4] 4 is a flowchart illustrating a process executed by the number of slides estimation device of the first embodiment. [Diagram 5] FIG. 1 is a diagram showing a first specific example of a model for estimating tumor cell count. [Figure 6] FIG. 2 is a diagram showing a first specific example of training data used for learning the tumor cell count estimation model. [Figure 7] FIG. 13 is a diagram showing a second specific example of a tumor cell number estimation model. [Figure 8] FIG. 13 is a diagram showing a second specific example of training data used for learning the tumor cell count estimation model. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment of the present disclosure will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are given the same reference numerals, and duplicated explanations will be omitted as necessary for clarity of explanation. Furthermore, unless otherwise specified, predetermined values ​​such as predetermined values ​​and threshold values ​​are stored in advance in a storage device accessible from a device that uses the values.

[0012] Fig. 1 is a diagram illustrating an example of an outline of the operation of the number of slides estimation device 2000 of embodiment 1. Here, Fig. 1 is a diagram for easily understanding the outline of the number of slides estimation device 2000, and the operation of the number of slides estimation device 2000 is not limited to that shown in Fig. 1.

[0013] The tissue slice 10 is a part of tissue (e.g., a part of a tumor in the body) taken by any method from the body of a human or other animal undergoing a specified examination. Hereinafter, the specified examination is referred to as a "target examination," and the person undergoing the target examination is referred to as a "subject." In the target examination, a slide (specimen slide 20) of a tissue specimen cut out from the tissue slice 10 is created, and the examination is performed using the specimen slide 20. In FIG. 1, n specimen slides 20, namely, specimen slide 20-1 to specimen slide 20-n, are created from the tissue slice 10. It is to be noted that when the specimen slides 20 are cut out from the tissue slice 10, the tissue slice 10 is previously subjected to a process such as formalin fixation.

[0014] Here, in the target test, a specified amount or more of a specific substance (hereinafter, the target substance) contained in the tumor cells of the subject is required. Therefore, a number of specimen slides 20 sufficient to obtain a specified amount or more of the target substance is required. For example, in order to perform a gene panel test, a certain amount of DNA needs to be obtained from the tumor cells. Therefore, in the gene panel test, a number of specimen slides sufficient to obtain the required amount of DNA is required.

[0015] Therefore, the slide number estimation device 2000 uses one or more slide images 30, which are images of the specimen slides 20, to estimate the number of specimen slides 20 to be obtained from the tissue slice 10 for the target test (in other words, the number of specimen slides 20 required to obtain at least the required amount of the target substance). Hereinafter, the number of specimen slides 20 to be obtained from the tissue slice 10 for the target test is also referred to as the "required number of specimen slides 20."

[0016] The slide image 30 is image data obtained by scanning a specimen slide 20 that has been stained in a predetermined manner using any method. Hereinafter, the specimen slide 20 scanned to obtain a certain slide image 30 will be referred to as the "specimen slide 20 corresponding to the slide image 30." Similarly, the slide image 30 obtained by scanning a certain specimen slide 20 will be referred to as the "slide image 30 corresponding to the specimen slide 20."

[0017] Here, the slide image 30 may be an image of the entire specimen slide 20 or an image of a part of the specimen slide 20. In the latter case, the slide image 30 includes at least an image area representing the attention area 22 of the specimen slide 20. For example, in the example of FIG. 1, a mark representing the attention area 22 is applied to the specimen slide 20 by any method, and then the area including the mark is scanned to generate the slide image 30. The setting of the attention area 22 may be performed after the slide image 30 is created. That is, a mark representing the attention area 22 may be applied to the slide image 30 obtained by scanning the specimen slide 20 using image processing software or the like. Hereinafter, the image area representing the attention area 22 among the image areas in the slide image 30 is referred to as the attention area image 32. When the slide image 30 includes only the attention area 22 of the corresponding specimen slide 20, the entire slide image 30 becomes the attention area image 32.

[0018] The slide number estimation device 2000 analyzes a slide image 30 to estimate the number of tumor cells contained in the region of interest 22 of the specimen slide 20 corresponding to the slide image 30. Then, the slide number estimation device 2000 estimates the required number of specimen slides 20 based on the estimated number of tumor cells.

[0019] <Example of action and effect> According to the slide number estimation device 2000 of this embodiment, the number of tumor cells contained in the region of interest 22 of the specimen slide 20 is estimated using the slide image 30 obtained by scanning the specimen slide 20. Then, based on the estimated number of tumor cells, the number of specimen slides 20 required for the target examination is estimated. According to this method, it is not necessary to analyze the slide images 30 for all specimen slides 20 obtained from the tissue piece 10, and the number of specimen slides 20 required for the target examination can be grasped by analyzing the slide images 30 for some of the specimen slides 20. Therefore, the efficiency of the examination using the specimen slides 20 can be improved.

[0020] Furthermore, for a specimen slide 20 used for an examination, it may be difficult to grasp the number of tumor cells using the slide image 30 corresponding to that specimen slide 20. This is because, while it is preferable to appropriately stain the specimen slide 20 in order to estimate the number of cells contained in the specimen slide 20 by image analysis, a specimen slide 20 stained in this manner may not be suitable as a slide to be used for an examination.

[0021] In this regard, the slide number estimation device 2000 only needs to stain some of the specimen slides 20 obtained from the tissue slices 10, so that the specimen slides 20 to be used in the examination can be reserved without staining. Therefore, the slide number estimation device 2000 can estimate the number of specimen slides 20 to be used in the examination even for an examination in which it is difficult to use stained specimen slides 20.

[0022] The slide number estimation device 2000 of this embodiment will be described in more detail below.

[0023] <Example of functional configuration> 2 is a block diagram illustrating a functional configuration of the slide number estimation device 2000 of the first embodiment. The slide number estimation device 2000 includes an acquisition unit 2020, a first estimation unit 2040, and a second estimation unit 2060. The acquisition unit 2020 acquires a slide image 30. The first estimation unit 2040 estimates the number of tumor cells included in the region of interest 22 of the specimen slide 20 corresponding to the slide image 30. The second estimation unit 2060 estimates the required number of specimen slides 20 based on the estimated number of tumor cells.

[0024] <Example of hardware configuration> Each functional component of the number of slides estimation device 2000 may be realized by hardware that realizes each functional component (e.g., a hardwired electronic circuit, etc.), or may be realized by a combination of hardware and software (e.g., a combination of an electronic circuit and a program that controls it, etc.). Below, a further description will be given of the case where each functional component of the number of slides estimation device 2000 is realized by a combination of hardware and software.

[0025] 3 is a block diagram illustrating an example of a hardware configuration of a computer 500 that realizes the number of slides estimation device 2000. The computer 500 is any computer. For example, the computer 500 is a stationary computer such as a PC (Personal Computer) or a server machine. In addition, for example, the computer 500 is a portable computer such as a smartphone or a tablet terminal. The computer 500 may be a dedicated computer designed to realize the number of slides estimation device 2000, or may be a general-purpose computer.

[0026] For example, by installing a specific application on the computer 500, each function of the number of slides estimation device 2000 is realized on the computer 500. The application is configured with a program for realizing each functional component of the number of slides estimation device 2000. Note that any method for acquiring the program is possible. For example, the program can be acquired from a storage medium (such as a DVD disk or a USB memory) in which the program is stored. Alternatively, for example, the program can be acquired by downloading the program from a server device that manages the storage device in which the program is stored.

[0027] The computer 500 has a bus 502, a processor 504, a memory 506, a storage device 508, an input / output interface 510, and a network interface 512. The bus 502 is a data transmission path for the processor 504, the memory 506, the storage device 508, the input / output interface 510, and the network interface 512 to transmit and receive data to and from each other. However, the method of connecting the processor 504 and other components to each other is not limited to a bus connection.

[0028] The processor 504 is a processor such as a central processing unit (CPU), a graphics processing unit (GPU), or a field-programmable gate array (FPGA). The memory 506 is a main storage device realized using a random access memory (RAM) or the like. The storage device 508 is an auxiliary storage device realized using a hard disk, a solid state drive (SSD), a memory card, or a read only memory (ROM) or the like.

[0029] The input / output interface 510 is an interface for connecting the computer 500 to an input / output device. For example, the input / output interface 510 is connected to an input device such as a keyboard and an output device such as a display device.

[0030] The network interface 512 is an interface for connecting the computer 500 to a network. This network may be a LAN (Local Area Network) or a WAN (Wide Area Network).

[0031] The storage device 508 stores a program (a program for realizing the above-mentioned application) for realizing each functional component of the number of slides estimation device 2000. The processor 504 reads this program into the memory 506 and executes it to realize each functional component of the number of slides estimation device 2000.

[0032] The number of slides estimation device 2000 may be realized by one computer 500, or may be realized by multiple computers 500. In the latter case, the configurations of the computers 500 do not need to be the same, and may be different from each other.

[0033] <Processing flow> 4 is a flowchart illustrating a process flow executed by the slide number estimation device 2000 of the first embodiment. The acquisition unit 2020 acquires a slide image 30 (S102). The first estimation unit 2040 estimates the number of tumor cells included in the region of interest 22 of the specimen slide 20 corresponding to the slide image 30 (S104). The second estimation unit 2060 estimates the required number of specimen slides 20 based on the estimated number of tumor cells (S106).

[0034] <Acquisition of slide image 30: S102> The acquiring unit 2020 acquires the slide images 30 (S102). There are various methods for the acquiring unit 2020 to acquire the slide images 30. For example, the acquiring unit 2020 acquires the slide images 30 stored in a storage device accessible from the slide number estimation device 2000. For example, a device (hereinafter, a scanning device) that scans the sample slide 20 to generate the slide images 30 stores the generated slide images 30 in the storage device. The acquiring unit 2020 acquires a slide image 30 desired by a user of the slide number estimation device 2000 from among the slide images 30 stored in the storage device. For example, the acquiring unit 2020 accepts a user input to select a desired one from among the slide images 30 stored in the storage device, and acquires the slide image 30 selected by the user input. In another example, the acquiring unit 2020 may acquire the slide image 30 by receiving a slide image 30 transmitted from another device (for example, the above-mentioned scanning device).

[0035] <Estimation of tumor cell count: S104> The first estimation unit 2040 analyzes the slide image 30 and estimates the number of tumor cells contained in the region of interest 22 of the specimen slide 20 corresponding to the slide image 30 (S104). For example, the first estimation unit 2040 detects cells from the region of interest image 32 in the slide image 30, and classifies each of the detected cells into tumor cells and normal cells. Then, the first estimation unit 2040 estimates the number of tumor cells by counting the number of cells classified as tumor cells.

[0036] For example, the first estimation unit 2040 has a model trained to detect cells from the interest area image 32 (hereinafter, cell detection model) and a model trained to classify images of cells into either tumor cells or normal cells (hereinafter, cell type discrimination model). Any type of model, such as a neural network or a support vector machine (SVM), can be used for these models. Here, existing technology can be used for the technology to train a model to detect a predetermined type of object from an image. Also, existing technology can be used for the technology to train a model to classify images of objects by object type.

[0037] When the first estimation unit 2040 inputs the slide image 30 to the cell detection model, an image of each cell included in the area of ​​interest image 32 is obtained from the cell detection model. In addition, the images of cells obtained from the cell detection model are input to a cell type discrimination model, whereby each cell is discriminated as being a tumor cell or a normal cell. The first estimation unit 2040 calculates the number of tumor cells by counting the number of cells discriminated as being tumor cells.

[0038] Instead of using two types of models, the cell detection model and the cell type discrimination model, it is also possible to use one model trained to detect tumor cells from the region of interest image 32. Also, the number of tumor cells may be estimated without using a trained model.

[0039] <Estimated number of specimen slides 20: S106> The second estimation unit 2060 estimates the required number of specimen slides 20 based on the number of tumor cells estimated by the first estimation unit 2040 (S106). Here, in the target test, it is necessary to obtain a predetermined amount or more of the target substance contained in the tumor cells of the subject. Therefore, the second estimation unit 2060 estimates the amount of the target substance contained in the tumor cells contained in the attention area 22 of the specimen slide 20 corresponding to the slide image 30 based on the number of tumor cells estimated by the first estimation unit 2040. In other words, the second estimation unit 2060 estimates the amount of the target substance contained in the tumor cells detected from the attention area image 32 of the slide image 30. Then, the second estimation unit 2060 estimates the required number of specimen slides 20 based on the estimated amount of the target substance and the amount of the target substance required for the target test.

[0040] For example, in a test such as a gene panel test in which DNA contained in tumor cells is used, the target substance is DNA. In this case, the second estimation unit 2060 estimates the amount of DNA obtained from tumor cells based on the number of tumor cells detected from the region of interest image 32 of the slide image 30. Then, the second estimation unit 2060 estimates the required number of specimen slides 20 based on the estimated amount of DNA and the amount of DNA required for the target test.

[0041] A predetermined conversion formula can be used to convert the number of tumor cells into the amount of the target substance. For example, Non-Patent Document 1 describes the relationship between the number of tumor cells and the amount of DNA as follows: "The yield of DNA obtained from one nucleated cell is estimated to be about 6 pg." Therefore, based on this relationship, the number of tumor cells can be converted into the amount of DNA.

[0042] The method of converting the number of tumor cells into the amount of the target substance is not limited to the method using the relationship between the number of tumor cells and the amount of the target substance disclosed in the literature. For example, a conversion formula from the number of tumor cells to the amount of the target substance may be created by performing an experiment before operating the slide number estimation device 2000. In addition, for example, a model (hereinafter, conversion model) that is trained to output the amount of the target substance contained in the region of interest 22 in response to inputting the number of tumor cells contained in the region of interest 22 of the specimen slide 20 may be used. Any regression model may be used as the conversion model.

[0043] The conversion model is trained using multiple training data consisting of pairs of input data and correct answer data (output data that should be output from the model in response to input of corresponding input data). The input data indicates the number of tumor cells contained in the region of interest 22. The correct answer data indicates the amount of the target substance contained in the region of interest 22.

[0044] The second estimation unit 2060 calculates the amount of the target substance contained in the region of interest 22 of the specimen slide 20 corresponding to the slide image 30, from the number of tumor cells estimated by the first estimation unit 2040, using the above-mentioned conversion formula or conversion model. Furthermore, the second estimation unit 2060 calculates the required number of specimen slides 20 based on the relationship between the calculated amount of the target substance and the amount of the target substance required for the target test. Here, information indicating the amount of the target substance required for the target test is stored in advance in a storage unit accessible from the slide number estimation device 2000.

[0045] For example, the second estimation unit 2060 calculates the required number of specimen slides 20 on the assumption that the same amount of the target substance is obtained from the region of interest 22 in all specimen slides 20 obtained from the tissue slice 10. In this case, the required number of specimen slides 20 can be calculated, for example, by the following formula (1).

number

[0046] Alternatively, for example, the second estimation unit 2060 may estimate the number of tumor cells contained in the region of interest 22 of another specimen slide 20 from the number of tumor cells estimated for the specimen slide 20 corresponding to the slide image 30. In this case, the required number M of specimen slides 20 is, for example, the smallest integer k that satisfies the following formula (2).

number

[0047] Here, in formula (2), i is set to 2 or more. This is because it is assumed that the specimen slide 20-1 is stained to obtain the slide image 30, and is not used for the target examination.

[0048] The required number of specimen slides 20 may be estimated based on the number of tumor cells estimated for the specimen slide 20 corresponding to the slide image 30 and the number of tumor cells required for the target examination. For example, the amount of the target substance required for the target examination is converted into the number of tumor cells to calculate the number of tumor cells required for the target examination in advance, and the value is stored in a storage device accessible from the slide number estimation device 2000. This conversion can be performed, for example, by using an inverse function of the conversion formula f(). In addition, in the learning of the conversion model described above, a conversion model that converts the amount of a predetermined substance into the number of tumor cells can be generated by reversing the relationship between the input data and the correct answer data.

[0049] If the number of tumor cells required for a target test is z, then equations (1) and (2) can be replaced by equations (3) and (4), respectively.

number

number

[0050] To estimate the required number of specimen slides 20 using the above-mentioned method, the second estimation unit 2060 estimates the number of tumor cells contained in the region of interest 22 of each of the other specimen slides 20 obtained from the tissue slice 10 based on the number of tumor cells estimated for the region of interest 22 of the specimen slide 20 corresponding to the slide image 30. For this estimation, for example, a trained tumor cell number estimation model is used. Any model such as a neural network or SVM can be used as the tumor cell number estimation model.

[0051] The tumor cell count estimation model outputs output data in response to input data. The input data includes one or more pairs of "slide image 30, tumor cell count estimated for that slide image 30." The output data indicates the number of tumor cells estimated to be included in the region of interest 22 of each of multiple specimen slides 20 obtained from a tissue slice 10 from which a slide image 30 included in the input data was obtained. Note that when the input data includes multiple slide images 30, all of these are obtained from the same tissue slice 10.

[0052] Below, some more specific examples of the tumor cell count estimation model will be described. Here, in the following description, it is assumed that n specimen slides 20 are cut out from a tissue piece 10.

[0053] Fig. 5 is a diagram showing a first specific example of a tumor cell count estimation model. Input data 42 input to the tumor cell count estimation model 40 in Fig. 5 includes a pair of "slide image 30 of specimen slide 20-1, number of tumor cells included in region of interest 22 of specimen slide 20-1". In addition, output data 44 output from the tumor cell count estimation model 40 in Fig. 5 indicates the number of tumor cells estimated to be included in region of interest 22 of each of n-1 specimen slides 20 other than the specimen slide 20 used for the input data 42.

[0054] The tumor cell count estimation model 40 in Fig. 5 is trained using multiple training data each consisting of a pair of input data and correct answer data. One training data 50 is generated using one tissue slice 10. Fig. 6 is a diagram showing a first specific example of training data used for training the tumor cell count estimation model 40. In Fig. 6, the input data 52 is composed of a pair of a slide image of a specimen slide 20-1 cut out from the tissue slice 10 and the number of tumor cells contained in the region of interest 22 of the specimen slide 20-1. The correct answer data 54 indicates the number of tumor cells contained in the region of interest 22 of each of the specimen slides 20-2 to 20-n.

[0055] Fig. 7 is a diagram showing a second specific example of the tumor cell count estimation model 40. The input data 42 input to the tumor cell count estimation model 40 in Fig. 7 includes two pairs of "slide image 30, tumor cell count". Moreover, the output data 44 output from the tumor cell count estimation model 40 in Fig. 7 indicates the number of tumor cells estimated to be included in the region of interest 22 of each of the n-2 specimen slides 20 other than the specimen slide 20 used for the input data 42.

[0056] These n-2 specimen slides 20 are all specimen slides 20 obtained from a portion of the tissue slice 10 between the specimen slide 20 corresponding to the first pair of slide images 30 in the input data 42 and the specimen slide 20 corresponding to the second pair of slide images 30 in the input data 42. That is, the tumor cell number estimation model 40 in Fig. 7 estimates the number of tumor cells contained in each of the specimen slides 20 between the first and last specimen slides 20 cut out successively from the tissue slice 10, based on the images of the specimen slide 20 cut out first and last and the numbers of tumor cells estimated for these.

[0057] FIG. 8 is a diagram showing a second specific example of the training data 50 used for learning the tumor cell count estimation model 40. The input data 52 is generated using the first and last specimen slides 20 among n specimen slides 20 consecutively cut out from the tissue piece 10. That is, the first pair included in the input data 52 is composed of "a slide image of the specimen slide 20-1, and the number of tumor cells included in the region of interest 22 of the specimen slide 20-1". The second pair included in the input data 52 is composed of "a slide image of the specimen slide 20-n, and the number of tumor cells included in the region of interest 22 of the specimen slide 20-n". The correct answer data 54 indicates the number of tumor cells included in the region of interest 22 of each of the specimen slides 20-2 to 20-(n-1).

[0058] In the tumor cell count estimation model 40, n, which is the number of specimen slides 20 cut out from the tissue piece 10, may be fixed or may be input to the tumor cell count estimation model 40. In the latter case, the input data 42 and the input data 52 further include the number of specimen slides 20 cut out from the tissue piece 10.

[0059] The input data 42 and the input data 52 may further include additional information that is data other than the slide image 30 and the number of tumor cells. For example, the additional information indicates one or more of the shape of the region of interest 22 of the specimen slide 20 corresponding to the slide image 30, the size of the region of interest 22, the density of tumor cells in the region of interest 22, the distribution of tumor cells in the region of interest 22, the method by which the tissue slice 10 was collected, the type of organ that contains the tissue slice 10, and the tissue type of the tumor cells.

[0060] Instead of inputting the additional information into tumor cell count estimation model 40, a different tumor cell count estimation model 40 may be prepared for each value of the additional information. For example, a different tumor cell count estimation model 40 may be prepared for each collection method of tissue piece 10. In this case, second estimation unit 2060 identifies, from among the multiple tumor cell count estimation models 40, a tumor cell count estimation model 40 that corresponds to the collection method of tissue piece 10 indicated by the additional information, and inputs the remaining data included in input data 42 to that tumor cell count estimation model 40.

[0061] Instead of using the tumor cell number estimation model 40, an estimation model that estimates the amount of the target substance contained in the region of interest 22 of each specimen slide 20 may be used. In this case, the correct answer data 54 of the training data 50 indicates the amount of the target substance contained in the region of interest of each specimen slide 20, instead of indicating the number of tumor cells contained in the region of interest of each specimen slide 20. The input data 42 and the input data 52 may indicate either the number of tumor cells or the amount of the target substance contained in the region of interest of the specimen slide 20.

[0062] The method of estimating the number of tumor cells contained in each specimen slide 20 obtained from the tissue slice 10 is not limited to the method using the tumor cell number estimation model 40. For example, a function expressing the relationship between the numbers of tumor cells contained in the regions of interest 22 of n specimen slides 20 successively cut out from the tissue slice 10 may be determined in advance. In this case, the second estimation unit 2060 estimates the number of tumor cells contained in the regions of interest 22 of each of the other specimen slides 20 from the number of tumor cells contained in the regions of interest 22 of the specimen slide 20 corresponding to the slide image 30 using the function.

[0063] For example, it is assumed that the number of tumor cells changes linearly in n specimen slides 20 successively cut out from a tissue piece 10. In this case, it is assumed that the second estimation unit 2060 uses the number of tumor cells estimated for one slide image 30 to estimate the number of tumor cells contained in the region of interest 22 of the other n-1 specimen slides 20. In this case, for example, the number of tumor cells contained in the region of interest 22 of the specimen slide 20-i can be calculated by the following formula (5). Formula ( 5 ) is a formula for estimating the number of tumor cells contained in the region of interest 22 of each of the other n-1 specimen slides 20 using the number of tumor cells b estimated for the slide image 30 of the specimen slide 20-1 cut from one end of the tissue piece 10.

number

[0064] Alternatively, for example, the second estimation unit 2060 estimates the number of tumor cells included in the region of interest 22 of the other n-2 specimen slides 20 using the tumor cells estimated for each of the two slide images 30. In this case, for example, the number of tumor cells included in the region of interest 22 of the specimen slide 20-i can be calculated by the following formula (6). Formula (6) is a formula for estimating the number of tumor cells included in the region of interest 22 of each of the n-2 specimen slides 20 located between the slide image 30 of the specimen slide 20-1 cut out from the tissue piece 10 and the slide image 30 of the specimen slide 20-n, using the numbers b and c of tumor cells estimated for each of them.

number

[0065] In the above description, it is assumed that the number of tumor cells changes linearly. However, the change in the number of tumor cells is not limited to a linear change, and may be a nonlinear change. In this case, for example, a nonlinear template function is prepared, and the template function is fitted to the number of tumor cells estimated for one or more slide images 30, thereby dynamically generating a function representing the change in the number of tumor cells. Then, the second estimation unit 2060 uses this function to estimate the number of tumor cells included in the region of interest 22 of the specimen slide 20 other than the specimen slide 20 corresponding to the slide image 30.

[0066] <Output by slide number estimation device 2000> The number of slides estimation device 2000 outputs information indicating the required number of sample slides 20. Hereinafter, this information will be referred to as output information. The output information may be output in any manner. For example, the number of slides estimation device 2000 stores the output information in any storage device accessible from the number of slides estimation device 2000. As another example, the number of slides estimation device 2000 displays the output information on a display device accessible from the number of slides estimation device 2000. As another example, the number of slides estimation device 2000 may transmit the output information to any device accessible from the number of slides estimation device 2000.

[0067] The output information may include information other than the required number of specimen slides 20. For example, this information may include: Tissue piece 10 The output information indicates which of the n specimen slides 20 extracted from the image should be used for the target test. For example, if the required number of specimen slides 20 is M, the top M specimen slides 20 in order of the number of tumor cells are the specimen slides 20 to be used for the target test. Therefore, for example, the output information includes information indicating the identifiers of each of the M specimen slides 20.

[0068] Here, it may be possible that even if all of the specimen slides 20 obtained from the tissue slice 10 are used, the amount of target substance required for the target test cannot be obtained. This is the case, for example, when the value of M calculated by formula (1) or the value of k that satisfies formula (2) exceeds n, which is the number of specimen slides 20 cut out from the tissue slice 10. In such a case, the slide number estimation device 2000 may output a message indicating that the required amount of target substance cannot be obtained from the tissue slice 10. For example, it may be possible to output a message such as "The required amount of DNA cannot be obtained from the current sample alone" or "An additional sample is required."

[0069] Although the present invention has been described above with reference to the embodiment, the present invention is not limited to the above embodiment. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0070] In the above example, the program can be stored and provided to the computer using various types of non-transitory computer readable media. The non-transitory computer readable medium includes various types of tangible storage media. Examples of the non-transitory computer readable medium include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs, CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs). The program may also be provided to the computer by various types of transitory computer readable media. Examples of the transitory computer readable medium include electrical signals, optical signals, and electromagnetic waves. The transitory computer readable medium can supply the program to the computer via a wired communication path such as an electric wire or optical fiber, or via a wireless communication path.

[0071] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes. (Appendix 1) An acquisition means for acquiring a slide image which is an image of a specimen slide obtained from a tissue slice of a subject; a first estimation means for estimating the number of tumor cells contained in a region of interest of the specimen slide using the slide image; and a second estimation means for estimating the number of specimen slides to be obtained from the tissue slice for performing a specified examination based on the estimated number of tumor cells. (Appendix 2) The second estimation means is Among the plurality of specimen slides obtained from the tissue slice, for each of the specimen slides other than the specimen slide from which the slide image was acquired, the number of tumor cells contained in the region of interest is estimated; The slide number estimation device according to claim 1, which estimates the number of specimen slides to be obtained from the tissue slice based on the number of tumor cells estimated for the region of interest of each of the specimen slides. (Appendix 3) The acquiring means acquires the slide image of the first specimen slide and the slide image of the second specimen slide, The first estimation means estimates the number of tumor cells contained in each of the regions of interest of the first specimen slide and the second specimen slide; The second estimation means estimates the number of tumor cells contained in the region of interest for each of a plurality of specimen slides obtained from a portion between the first specimen slide and the second specimen slide. (Appendix 4) The second estimation means is a model that has been trained to output, in response to input data indicating an image of the specimen slide and the number of tumor cells contained in the region of interest of the specimen slide, output data indicating the number of tumor cells contained in the region of interest of each of the specimen slides other than the specimen slide from which the slide image was obtained, among the plurality of specimen slides obtained from the tissue slice; the acquired slide image and the estimated number of tumor cells are input into the model, and the number of specimen slides to be obtained from the tissue section is estimated using the output data obtained from the model in response to the input. (Appendix 5) 5. The slide number estimation device according to claim 4, wherein the input data further includes information representing one or more of a shape of the region of interest, a size of the region of interest, a density of tumor cells in the region of interest, a distribution of tumor cells in the region of interest, a method for collecting the tissue slice, a type of organ containing the tissue slice, and a tissue type of the tumor cells. (Appendix 6) The second estimation means is estimating the amount of a predetermined substance contained in the region of interest from the estimated number of tumor cells; 6. The slide number estimation device according to any one of claims 1 to 5, wherein the device estimates the number of specimen slides to be obtained from the tissue slice based on the estimated amount of the specified substance and the amount of the specified substance required for the specified test. (Appendix 7) The predetermined test is a gene panel test, 7. The slide number estimation device of claim 6, wherein the specified substance is DNA. (Appendix 8) 1. A computer-implemented control method comprising: acquiring a slide image, which is an image of a specimen slide obtained from a tissue slice of a subject; a first estimation step of estimating the number of tumor cells contained in a region of interest of the specimen slide using the slide image; A control method comprising: a second estimation step of estimating the number of specimen slides to be obtained from the tissue slice for performing a specified examination based on the estimated number of tumor cells. (Appendix 9) In the second estimation step, Among the plurality of specimen slides obtained from the tissue slice, for each of the specimen slides other than the specimen slide from which the slide image was acquired, the number of tumor cells contained in the region of interest is estimated; The control method of claim 8, further comprising estimating the number of specimen slides to be obtained from the tissue section based on the estimated number of tumor cells for the region of interest of each of the specimen slides. (Appendix 10) In the acquiring step, the slide image of the first specimen slide and the slide image of the second specimen slide are acquired, In the first estimation step, the number of tumor cells contained in each of the regions of interest of the first specimen slide and the second specimen slide is estimated; The control method of claim 9, wherein in the second estimation step, the number of tumor cells contained in the region of interest of each of a plurality of specimen slides obtained from the portion between the first specimen slide and the second specimen slide is estimated. (Appendix 11) the computer has a model trained to, in response to input of an image of the specimen slide and input data indicating the number of tumor cells included in the region of interest of the specimen slide, output data indicating the number of tumor cells included in the region of interest of each of the specimen slides other than the specimen slide from which the slide image was obtained, among the plurality of specimen slides obtained from the tissue slice; The control method according to claim 9 or 10, wherein in the second estimation step, the acquired slide image and the estimated number of tumor cells are input into the model, and the number of specimen slides to be obtained from the tissue section is estimated using the output data obtained from the model in response to the input. (Appendix 12) 12. The control method of claim 11, wherein the input data further includes information representing one or more of a shape of the region of interest, a size of the region of interest, a density of tumor cells in the region of interest, a distribution of tumor cells in the region of interest, a method by which the tissue slice was collected, a type of organ containing the tissue slice, and a tissue type of the tumor cells. (Appendix 13) In the second estimation step, estimating the amount of a predetermined substance contained in the region of interest from the estimated number of tumor cells; A control method according to any one of appendix 8 to 12, further comprising estimating the number of specimen slides to be obtained from the tissue section based on the estimated amount of the specified substance and the amount of the specified substance required for the specified test. (Appendix 14) The predetermined test is a gene panel test, The control method described in Appendix 13, wherein the specified substance is DNA. (Appendix 15) On the computer, acquiring a slide image, which is an image of a specimen slide obtained from a tissue slice of a subject; a first estimation step of estimating the number of tumor cells contained in a region of interest of the specimen slide using the slide image; A non-transitory computer-readable medium having stored thereon a program for executing a second estimation step of estimating the number of specimen slides to be obtained from the tissue section to perform a specified test based on the estimated number of tumor cells. (Appendix 16) In the second estimation step, Among the plurality of specimen slides obtained from the tissue slice, for each of the specimen slides other than the specimen slide from which the slide image was acquired, the number of tumor cells contained in the region of interest is estimated; 16. The computer-readable medium of claim 15, further comprising estimating the number of specimen slides to be obtained from the tissue section based on the estimated number of tumor cells for the region of interest of each of the specimen slides. (Appendix 17) In the acquiring step, the slide image of the first specimen slide and the slide image of the second specimen slide are acquired, In the first estimation step, the number of tumor cells contained in each of the regions of interest of the first specimen slide and the second specimen slide is estimated; The computer-readable medium of claim 16, wherein in the second estimation step, the number of tumor cells contained in the region of interest of each of a plurality of specimen slides obtained from a portion between the first specimen slide and the second specimen slide is estimated. (Appendix 18) the program includes a model that has been trained to output, in response to input data indicating an image of the specimen slide and the number of tumor cells contained in the region of interest of the specimen slide, output data indicating the number of tumor cells contained in the region of interest of each of the specimen slides other than the specimen slide from which the slide image was obtained, among the multiple specimen slides obtained from the tissue slice; 18. The computer-readable medium of claim 16, wherein in the second estimation step, the acquired slide image and the estimated number of tumor cells are input into the model, and the number of specimen slides to be obtained from the tissue section is estimated using the output data obtained from the model in response to the input. (Appendix 19) 19. The computer-readable medium of claim 18, wherein the input data further comprises information representing one or more of a shape of the region of interest, a size of the region of interest, a density of tumor cells in the region of interest, a distribution of tumor cells in the region of interest, a method by which the tissue slice was collected, a type of organ containing the tissue slice, and a tissue type of the tumor cells. (Appendix 20) In the second estimation step, estimating the amount of a predetermined substance contained in the region of interest from the estimated number of tumor cells; 20. The computer-readable medium of any one of claims 15 to 19, further comprising estimating the number of specimen slides to be obtained from the tissue section based on the estimated amount of the predetermined material and the amount of the predetermined material required for the predetermined test. (Appendix 21) The predetermined test is a gene panel test, 21. The computer-readable medium of claim 20, wherein the predetermined substance is DNA.

[0072] This application claims priority based on Japanese Patent Application No. 2021-051351, filed on March 25, 2021, the disclosure of which is incorporated herein in its entirety. [Explanation of symbols]

[0073] 10 tissue pieces 20 Specimen Slides 22 Areas of Interest 30 Slide images 32 Area of ​​interest images 40 Tumor cell number estimation model 42 Input Data 44 Output Data 50 training data 52 Input Data 54 Correct Data 500 Computers 502 Bus 504 Processor 506 Memory 508 Storage Devices 510 Input / Output Interface 512 Network Interface 2000 Slide Number Estimation Device 2020 Acquisition Department 2040 1st estimation part 2060 Second Estimation Department

Claims

1. An acquisition means for acquiring a slide image which is an image of a specimen slide obtained from a tissue slice of a subject; a first estimation means for estimating the number of tumor cells contained in a region of interest of the specimen slide using the slide image; and a second estimation means for estimating the number of specimen slides to be obtained from the tissue slice for performing a predetermined examination based on the estimated number of tumor cells, The predetermined test is a test that utilizes nucleic acid contained in tumor cells obtained from the tissue slice.

2. The second estimation means For each of the plurality of specimen slides obtained from the tissue slice, other than the specimen slide from which the slide image was acquired, estimate the number of tumor cells contained in the region of interest; The slide number estimation device according to claim 1 , wherein the number of specimen slides to be obtained from the tissue slice is estimated based on the number of tumor cells estimated for the region of interest of each of the specimen slides.

3. the acquiring means acquires the slide image of the first specimen slide and the slide image of the second specimen slide, The first estimation means estimates the number of tumor cells contained in each of the regions of interest of the first specimen slide and the second specimen slide; 3. The slide number estimation device according to claim 2, wherein the second estimation means estimates the number of tumor cells contained in a region of interest for each of a plurality of specimen slides obtained from a portion between the first specimen slide and the second specimen slide.

4. The second estimation means a model that has been trained to output, in response to input data indicating an image of the specimen slide and the number of tumor cells contained in the region of interest of the specimen slide, output data indicating the number of tumor cells contained in the region of interest of each of the specimen slides other than the specimen slide from which the slide image was obtained, among the plurality of specimen slides obtained from the tissue slice; 4. The slide number estimation device according to claim 2 or 3, wherein the acquired slide image and the estimated number of tumor cells are input to the model, and the number of specimen slides to be obtained from the tissue slice is estimated using the output data obtained from the model in response to the input.

5. 5. The slide number estimation device according to claim 4, wherein the input data further includes information indicating one or more of a shape of the region of interest, a size of the region of interest, a density of tumor cells in the region of interest, a distribution of tumor cells in the region of interest, a method for collecting the tissue slice, a type of organ containing the tissue slice, and a tissue type of the tumor cells.

6. The second estimation means estimating the amount of nucleic acid contained in the region of interest from the estimated number of tumor cells; 6. The slide number estimation device according to claim 1, further comprising: an estimation unit that estimates the number of specimen slides to be obtained from the tissue slice based on the estimated amount of nucleic acid and the amount of nucleic acid required for the specified test.

7. The slide number estimation device according to claim 6 , wherein the predetermined test is a gene panel test.

8. 1. A computer-implemented control method comprising: acquiring a slide image, which is an image of a specimen slide obtained from a tissue slice of a subject; a first estimation step of estimating the number of tumor cells contained in a region of interest of the specimen slide using the slide image; and a second estimation step of estimating the number of specimen slides to be obtained from the tissue slice for performing a predetermined examination based on the estimated number of tumor cells, A control method, wherein the specified test is a test that utilizes nucleic acid contained in tumor cells obtained from the tissue slice.

9. In the second estimation step, For each of the plurality of specimen slides obtained from the tissue slice, other than the specimen slide from which the slide image was acquired, estimate the number of tumor cells contained in the region of interest; The control method according to claim 8 , further comprising estimating the number of specimen slides to be obtained from the tissue section based on the estimated number of tumor cells in the region of interest of each of the specimen slides.

10. On the computer, acquiring a slide image, which is an image of a specimen slide obtained from a tissue slice of a subject; a first estimation step of estimating the number of tumor cells contained in a region of interest of the specimen slide using the slide image; a second estimation step of estimating the number of specimen slides to be obtained from the tissue slice for a predetermined examination based on the estimated number of tumor cells; The predetermined test is a program in which nucleic acid contained in tumor cells obtained from the tissue slice is used.

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