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AI Biotech/Diagnostics: Other Innovation
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Application US20200075169
Published 2020-03-05
Multi-modal Approach To Predicting Immune Infiltration Based On Integrated Rna Expression And Imaging Features
Multi-modal approaches to predict tumor immune infiltration are based on integrating gene expression data and imaging features in a neural network-based framework. This framework is configured to estimate percent composition, and thus immune infiltration score, of a patient tumor biopsy sample. Multi-modal approaches may also be used to predict cell composition beyond immune cells via integrated multi-layer neural network frameworks.
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- 1. A computing device configured to generate an immune infiltration prediction score, the computing device comprising one or more processors configured to:
obtain gene expression data from one or more gene expression datasets with the gene expression data corresponding to one or more tissue samples; obtain a set of stained histopathology images from one or more image sources and corresponding to the one or more tissue samples; determine imaging features from the set of stained histopathology images, the imaging features comprising texture and/or intensity features; in a neural network framework, transform the gene expression data using a gene expression neural network layer(s) and transform the imaging features using an imaging feature neural network layer(s); in the neural network framework, integrate an output of the gene expression neural network layer(s) and the imaging feature neural network layer(s) to produce an integrated neural network output; and apply a prediction function to the integrated neural network output and output an immune infiltration score for the one or more tissue samples.
- 2.-3. (canceled)
- 12. A computer-implemented method to generate an immune infiltration prediction score, the method comprising:
obtaining a gene expression data from one or more gene expression datasets with the gene expression data corresponding to one or more tissue samples; obtaining a set of stained histopathology images from one or more image sources and corresponding to the one or more tissue samples; determining imaging features from the set of stained histopathology images, the imaging features comprising texture and/or intensity features; in a neural network framework, transforming the gene expression data using a gene expression neural network layer(s) and transforming the imaging features using an imaging feature neural network layer(s); in the neural network framework, integrating an output of the gene expression neural network layer(s) and the imaging feature neural network layer(s) to produce an integrated neural network output; and applying a prediction function to the integrated neural network output and outputting an immune infiltration score for the one or more tissue samples.
- 13.-14. (canceled)
