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AI Biotech/Diagnostics: General Diagnostics


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Patent US10650929


Issued 2020-05-12

Systems And Methods For Training A Model To Predict Survival Time For A Patient

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.



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3 Independent Claims

  • 1. A method for training a model to predict survival time for a patient, the method comprising: accessing a first plurality of annotated pathology images associated with a first group of patients in a randomized controlled clinical trial, wherein each of the first plurality of annotated pathology images is associated with survival data for a respective patient, wherein each of the first plurality of annotated pathology images includes at least one annotation describing a tissue characteristic category for a portion of the image; extracting a first plurality of values for a plurality of features from each of the first plurality of annotated pathology images; training a model based on the survival data and the first plurality of values for the plurality of features; processing, using the trained model, the first plurality of values for the plurality of features, 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 the 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 first prognostic performance of the trained model for the experimental treatment group based on the predicted survival data for the patients in the first group of patients and respective survival data; determining a second prognostic performance of the trained model for the control treatment group based on predicted survival data for the patients in the second group of patients and respective survival data; and determining a specificity of a prognostic power of the trained model by comparing the first prognostic performance of the trained model for the experimental treatment group and the second prognostic performance of the trained model for the control treatment group, 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.

  • 8. A system for training a model 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 first plurality of annotated pathology images associated with a first group of patients in a randomized controlled clinical trial, wherein each of the first plurality of annotated pathology images is associated with survival data for a respective patient, wherein each of the first plurality of annotated pathology images includes at least one annotation describing a tissue characteristic category for a portion of the image; extracting a first plurality of values for a plurality of features from each of the first plurality of annotated pathology images; training a model based on the survival data and the first plurality of values for the plurality of features; processing, using the trained model, the first plurality of values for the plurality of features, 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 the 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 first prognostic performance of the trained model for the experimental treatment group based on the predicted survival data for the patients in the first group of patients and respective survival data; determining a second prognostic performance of the trained model for the control treatment group based on predicted survival data for the patients in the second group of patients and respective survival data; and determining a specificity of a prognostic power of the trained model by comparing the first prognostic performance of the trained model for the experimental treatment group and the second prognostic performance of the trained model for the control treatment group, 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.

  • 15. 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 first plurality of annotated pathology images associated with a first group of patients in a randomized controlled clinical trial, wherein each of the first plurality of annotated pathology images is associated with survival data for a respective patient, wherein each of the first plurality of annotated pathology images includes at least one annotation describing a tissue characteristic category for a portion of the image; extracting a first plurality of values for a plurality of features from each of the first plurality of annotated pathology images; training a model based on the survival data and the first plurality of values for the plurality of features; processing, using the trained model, the first plurality of values for the plurality of features, 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 the 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 first prognostic performance of the trained model for the experimental treatment group based on the predicted survival data for the patients in the first group of patients and respective survival data; determining a second prognostic performance of the trained model for the control treatment group based on predicted survival data for the patients in the second group of patients and respective survival data; and determining a specificity of a prognostic power of the trained model by comparing the first prognostic performance of the trained model for the experimental treatment group and the second prognostic performance of the trained model for the control treatment group, 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.