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:
using at least one machine learning model to:
extract values for one or more features from input data representing an annotated pathology image, the annotated pathology image comprising one or more annotations for the pathology image; and
process the values for the one or more features extracted from the input data representing the annotated pathology image to predict an entity of interest, wherein the entity of interest comprises patient clinical outcomes; and
outputting output data representing the predicted entity of interest,
wherein the at least one machine learning model is trained 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.
2 . The method of claim 1 , wherein the at least one machine learning model is further trained based on clinical metadata values associated with the patients participating in the randomized controlled clinical trial.
3 . The method of claim 1 , wherein the at least one machine learning model is further trained based on one or more of: genomic data, transcriptomic data, and/or protein expression data; and wherein the machine learning model is further trained to output a survival time.
4 . 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.
5 . The method of claim 1 , wherein extracting the values for the one or more features from the input data representing the annotated pathology image comprises applying one or more functions to the input data representing the annotated pathology image to extract the values for the one or more features.
6 . The method of claim 1 , further comprising using the at least one machine learning model to determine a first value for a first feature based on a combination of at least some of the values for the one or more features.
7 . The method of claim 1 , wherein:
the input data representing the annotated pathology image includes a heat map; and
extracting the values for the one or more features from the input data representing the annotated pathology image comprises applying one or more functions to the heat map to extract the values for the one or more features.
8 . 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:
using at least one machine learning model to:
extract values for one or more features from input data representing an annotated pathology image, the annotated pathology image comprising one or more annotations for the pathology image; and
process the values for the one or more features extracted from the input data representing the annotated pathology image to predict an entity of interest, wherein the entity of interest comprises patient clinical outcomes; and
outputting output data representing the predicted entity of interest,
wherein the at least one machine learning model is trained 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.
9 . The system of claim 8 , wherein the at least one machine learning model is further trained based on clinical metadata values associated with the patients participating in the randomized controlled clinical trial.
10 . The system of claim 8 , wherein the at least one machine learning model is further trained based on one or more of: genomic data, transcriptomic data, and/or protein expression data; and wherein the machine learning model is further trained to output a survival time.
11 . The system of claim 8 , 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.
12 . The system of claim 8 , wherein extracting the values for the one or more features from the input data representing the annotated pathology image comprises applying one or more functions to the input data representing the annotated pathology image to extract the values for the one or more features.
13 . The system of claim 8 , wherein the processor-executable instructions, when executed by the at least one computer hardware processor, further cause the at least one computer hardware processor to use the at least one machine learning model to determine a first value for a first feature based on a combination of at least some of the values for the one or more features.
14 . The system of claim 8 , wherein:
the input data representing the annotated pathology image includes a heat map; and extracting the values for the one or more features from the input data representing the annotated pathology image comprises applying one or more functions to the heat map to extract the values for the one or more features.
15 . A non-transitory computer-readable medium containing instructions that, when executed, cause at least one computer hardware processor to perform:
us at least one machine learning model to:
extract values for one or more features from input data representing an annotated pathology image, the annotated pathology image comprising one or more annotations for the pathology image; and
process the values for the one or more features extracted from the input data representing the annotated pathology image to predict an entity of interest, wherein the entity of interest comprises patient clinical outcomes; and
output data representing the predicted entity of interest,
wherein the at least one machine learning model is trained 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.
16 . The non-transitory computer-readable medium of claim 15 , wherein the at least one machine learning model is further trained based on clinical metadata values associated with the patients participating in the randomized controlled clinical trial.
17 . The non-transitory computer-readable medium of claim 15 , wherein the at least one machine learning model is further trained based on one or more of: genomic data, transcriptomic data, and/or protein expression data; and wherein the machine learning model is further trained to output a survival time.