Systems and methods for training a model to predict survival time for a patient
AssigneePathAI, Inc.
InventorsAndrew H. Beck, Aditya Khosla
Life of this patent
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Abstract
In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.
Claims20
A method for training one or more models to predict survival time for a patient, the method comprising:
- accessing a plurality of annotated pathology images associated with a group of patients in a randomized controlled clinical trial, wherein each of the plurality of annotated pathology images is associated with survival data for a respective patient, wherein each of the plurality of annotated pathology images includes at least one annotation describing a tissue characteristic category for a portion of the image, wherein the plurality of annotated pathology images includes:
- a first plurality of annotated pathology images associated with a first group of patients belonging to a first treatment group of the randomized controlled clinical trial, and
- a second plurality of annotated pathology images associated with a second group of patients belonging to a second treatment group of the randomized controlled clinical trial;
- training one or more models based on the survival data and the plurality of annotated pathology images, wherein the one or more trained models include a first plurality of features based in part on the first plurality of annotated pathology images and a second plurality of features based in part on the second plurality of annotated pathology images;
- and
- storing the one or more trained models on at least one storage device.
A system for training one or more models to predict survival time for a patient, 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:
- accessing a plurality of annotated pathology images associated with a group of patients in a randomized controlled clinical trial, wherein each of the plurality of annotated pathology images is associated with survival data for a respective patient, wherein each of the plurality of annotated pathology images includes at least one annotation describing a tissue characteristic category for a portion of the image, wherein the plurality of annotated pathology images includes:
- a first plurality of annotated pathology images associated with a first group of patients belonging to a first treatment group of the randomized controlled clinical trial, and
- a second plurality of annotated pathology images associated with a second group of patients belonging to a second treatment group of the randomized controlled clinical trial;
- training one or more models based on the survival data and the plurality of annotated pathology images, wherein the one or more trained models include a first plurality of features based in part on the first plurality of annotated pathology images and a second plurality of features based in part on the second plurality of annotated pathology images;
- and
- storing the one or more trained models on at least one storage device.
A non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform:
- accessing a plurality of annotated pathology images associated with a group of patients in a randomized controlled clinical trial, wherein each of the plurality of annotated pathology images is associated with survival data for a respective patient, wherein each of the plurality of annotated pathology images includes at least one annotation describing a tissue characteristic category for a portion of the image, wherein the plurality of annotated pathology images includes:
- a first plurality of annotated pathology images associated with a first group of patients belonging to a first treatment group of the randomized controlled clinical trial, and
- a second plurality of annotated pathology images associated with a second group of patients belonging to a second treatment group of the randomized controlled clinical trial;
- training one or more models based on the survival data and the plurality of annotated pathology images, wherein the one or more trained models include a first plurality of features based in part on the first plurality of annotated pathology images and a second plurality of features based in part on the second plurality of annotated pathology images;
- and
- storing the one or more trained models on at least one storage device.
Dependencies are inferred from the claim text.