Systems and methods for training a statistical model to predict tissue characteristics for a pathology image
In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.
1. A method for predicting an entity of interest for a pathology image, the method comprising:
receiving an annotated pathology image including one or more annotations for the pathology image;
extracting values for one or more features from the annotated pathology image;
retrieving a machine learning model from at least one storage device; processing, using the machine learning model, the values for the one or more features extracted from the annotated pathology image to predict an entity of interest, wherein the entity of interest is selected from a group comprising survival time, drug response, patient level phenotype/molecular characteristics, and patient clinical outcomes; and
storing the predicted entity of interest on the at least one storage device.
2. The method of claim 1 , further comprising:
training the machine learning model from extracted values for the one or more features from a plurality of annotated training pathology images, wherein the training pathology images are collected from patients participating in a randomized controlled clinical trial; and
storing the machine learning model in the at least one storage device.
3. The method of claim 2 , wherein training the machine learning model is further based on clinical metadata values associated with the patients participating in the randomized controlled clinical trial.
4. The method of claim 2 , wherein training the machine learning model is further based on one or more of: genomic data, transcriptomic data, and/or protein expression data; and wherein the machine learning model is trained to output the survival time.
5. The method of claim 1 , wherein the one or more features include one or more of area of epithelium, area of stroma, area of necrosis, area of cancer cells, area of macrophages, area of lymphocytes, number of mitotic figures, average nuclear grade, average distance between fibroblasts and lymphocytes, average distance between immunohistochemistry-positive macrophages and cancer cells, standard deviation of nuclear grade, and average distance between blood vessels and tumor cells.
6. The method of claim 1 , wherein extracting the values for the one or more features from the annotated pathology image comprises applying one or more functions to the annotated pathology image to extract the values for the one or more features.
7. The method of claim 1 , further comprising determining a first value for a first feature based on a combination of at least some of the values for the one or more features.
8. The method of claim 1 , wherein:
the annotated pathology image includes a heat map; and
extracting the values for the one or more features from the annotated pathology image comprises applying one or more functions to the heat map to extract the values for the one or more features.
9. A system for predicting an entity of interest for a pathology image, the system comprising:
at least one computer hardware processor; and
at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
receiving an annotated pathology image including one or more annotations for the pathology image;
extracting values for one or more features from the annotated pathology image;
retrieving a trained machine learning model from at least one storage device;
processing, using the trained machine learning model, the values for the one or more features extracted from the annotated pathology image to predict an entity of interest, wherein the entity of interest is selected from a group comprising survival time, drug response, patient level phenotype/molecular characteristics, and patient clinical outcomes; and
storing the predicted entity of interest on the at least one storage device.
10. The system of claim 9 , the processor-executable instructions are further configured to cause the at least one computer hardware processor to perform:
training the machine learning model from extracted values for the one or more features from a plurality of annotated training pathology images, wherein the training pathology images are collected from patients participating in a randomized controlled clinical trial; and
storing the machine learning model in the at least one storage device.
11. The system of claim 10 , wherein training the machine learning model is further based on
clinical metadata values associated with the patients participating in the randomized controlled clinical trial.
12. The system of claim 10 , wherein training the machine learning model is further based on one or more of: genomic data, transcriptomic data, and/or protein expression data; and wherein the machine learning model is trained to output the survival time.
13. The system of claim 9 , wherein the one or more features include one or more of area of epithelium, area of stroma, area of necrosis, area of cancer cells, area of macrophages, area of lymphocytes, number of mitotic figures, average nuclear grade, average distance between fibroblasts and lymphocytes, average distance between immunohistochemistry-positive macrophages and cancer cells, standard deviation of nuclear grade, and average distance between blood vessels and tumor cells.
14. The system of claim 9 , wherein extracting the values for the one or more features from the annotated pathology image comprises applying one or more functions to the annotated pathology image to extract the values for the one or more features.
15. The system of claim 9 , wherein the processor-executable instructions further cause the at least one computer hardware processor to perform determining a first value for a first feature based on a combination of at least some of the values for the one or more features.
16. The system of claim 9 , wherein:
the annotated pathology image includes a heat map; and
extracting the values for the one or more features from the annotated pathology image comprises applying one or more functions to the heat map to extract the values for the one or more features.
17. A non-transitory computer-readable medium containing instructions that, when executed, cause at least one computer hardware processor to perform:
receiving an annotated pathology image including one or more annotations for the pathology image;
extracting values for one or more features from the annotated pathology image;
retrieving a trained machine learning model from at least one storage device;
processing, using the trained machine learning model, the values for the one or more features extracted from the annotated pathology image to predict an entity of interest, wherein the entity of interest is selected from a group comprising survival time, drug response, patient level phenotype/molecular characteristics, and patient clinical outcomes; and
storing the predicted entity of interest on the at least one storage device.
18. The non-transitory computer-readable medium of claim 17 , wherein the instructions are further configured to cause the at least one computer hardware processor to perform:
training the machine learning model from extracted values for the one or more features from a plurality of annotated training pathology images, wherein the training pathology images are collected from patients participating in a randomized controlled clinical trial; and
storing the machine learning model in the at least one storage device.
19. The non-transitory computer-readable medium of claim 18 , wherein training the machine learning model is further based on clinical metadata values associated with the patients participating in the randomized controlled clinical trial.
20. The non-transitory computer-readable medium of claim 18 , wherein training the machine learning model is further based on one or more of: genomic data, transcriptomic data, and/or protein expression data; and wherein the machine learning model is trained to output the survival time.