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US 11,657,505 B1Granted
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
Estimating the term…
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, comprising:
- processing, using a trained model, a first plurality of values for a plurality of features extracted from a first plurality of annotated pathology images associated with a first group of patients, to predict survival data for patients in the first group of patients, wherein the first group of patients belongs to an experimental treatment group of a randomized controlled clinical trial;
- processing, using the trained model, a second plurality of values for the plurality of features extracted from a second plurality of annotated pathology images associated with a second group of patients, to predict survival data for patients in the second group of patients, wherein the second group of patients belongs to a control treatment group of the randomized controlled clinical trial;
- and
- determining a specificity of a prognostic power of the trained model based on:
- a first prognostic performance of the trained model for the experimental treatment group determined based on the predicted survival data for the patients in the first group of patients and respective survival data, and
- a second prognostic performance of the trained model for the control treatment group determined based on predicted survival data for the patients in the second group of patients and respective survival data,
- wherein the specificity of the prognostic power of the trained model includes a likelihood that the model will correctly identify of a subset of patients that benefit from experimental treatment.
4 more elements
A 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:
- processing, using a trained model, a first plurality of values for a plurality of features extracted from a first plurality of annotated pathology images associated with a first group of patients, to predict survival data for patients in the first group of patients, wherein the first group of patients belongs to an experimental treatment group of a randomized controlled clinical trial;
- processing, using the trained model, a second plurality of values for the plurality of features extracted from a second plurality of annotated pathology images associated with a second group of patients, to predict survival data for patients in the second group of patients, wherein the second group of patients belongs to a control treatment group of the randomized controlled clinical trial;
- determining a specificity of a prognostic power of the trained model based on:
- a first prognostic performance of the trained model for the experimental treatment group determined based on the predicted survival data for the patients in the first group of patients and respective survival data, and
- a second prognostic performance of the trained model for the control treatment group determined based on predicted survival data for the patients in the second group of patients and respective survival data,
- wherein the specificity of the prognostic power of the trained model includes a likelihood that the model will correctly identify of a subset of patients that benefit from experimental treatment.
6 more elements
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:
- processing, using a trained model, a first plurality of values for a plurality of features extracted from a first plurality of annotated pathology images associated with a first group of patients, to predict survival data for patients in the first group of patients, wherein the first group of patients belongs to an experimental treatment group of a randomized controlled clinical trial;
- processing, using the trained model, a second plurality of values for the plurality of features extracted from a second plurality of annotated pathology images associated with a second group of patients, to predict survival data for patients in the second group of patients, wherein the second group of patients belongs to a control treatment group of the randomized controlled clinical trial;
- determining a specificity of a prognostic power of the trained model based on:
- a first prognostic performance of the trained model for the experimental treatment group determined based on the predicted survival data for the patients in the first group of patients and respective survival data, and
- a second prognostic performance of the trained model for the control treatment group determined based on predicted survival data for the patients in the second group of patients and respective survival data,
- wherein the specificity of the prognostic power of the trained model includes a likelihood that the model will correctly identify of a subset of patients that benefit from experimental treatment.
3 more elements
Dependencies are inferred from the claim text.