Application US20200075169

MULTI-MODAL APPROACH TO PREDICTING IMMUNE INFILTRATION BASED ON INTEGRATED RNA EXPRESSION AND IMAGING FEATURES
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US20200075169

1. A computing device configured to generate an immune infiltration prediction score, the computing device comprising one or more processors configured to: (8) (6)

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.

4. The computing device of claim 1, wherein the gene expression data is RNA sequencing data. (0)

5. The computing device of claim 1, wherein the neural network framework comprises two neural network layers. (0)

6. The computing device of claim 1, wherein the imaging features comprise mean, standard deviation, skewness, and/or sum of image gray level, image red, green, blue layers, stain layers, optical density, hue, and/or saturation. (0)

7. The computing device of claim 1, wherein the imaging features comprise Zernike moments, threshold adjacency analysis values, local binary patterns, gray scale co-occurrence matrix, and/or difference of Gaussian statistical measures. (0)

8. The computing device of claim 1, wherein the prediction function is Softmax function. (0)

9. The computing device of claim 1, wherein immune infiltration score comprises a predicted percentage of natural killer (NK) cells, (MAC) macrophage cells, CD4 T cells, CD8 T cells, and B cells, regulatory T cells, Dendritic cells, monocytes, Mast cells, Eosinophils, and Neutrophils. (0)

10. The computing device of claim 1, wherein immune infiltration score comprises a predicted percentage of others cells, including stromal cells, vasculature cells, fat cells, tumor cells, stem cells, neural cells, progenitor cells, innate lymphoid cells, microglial cells, leukocytes, naïve B cells, memory B cells, Plasma cells, CD8 T cells, naïve CD4 T cells, memory CD4 T cells, follicular helper T cells, regulatory T cells, gamma delta T cells, Th17 T cells, unstimulated NK cells, stimulated NK cells, Macrophages MO, Macrophages M1, Macrophages M2, unstimulated Dendritic cells, stimulated Dendritic cells, unstimulated Mast cells, stimulated Mast cells. (0)

11. The computing device of claim 1, wherein the contextual data is a total immune fraction or total tumor fraction. (0)

2.-3. (canceled) (0)

12. A computer-implemented method to generate an immune infiltration prediction score, the method comprising: (3) (6)

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.

15. The computer-implemented method of claim 12, further comprising obtaining the gene expression data from an RNA sequencing data source communicatively coupled to a communication network, the gene expression data comprising RNA sequencing data. (2) (0)

18. The computer-implemented method of claim 12, wherein the gene expression data is RNA sequencing data, the method further comprising performing a gene selection on the RNA sequencing data. (0)

19. The computer-implemented method of claim 12, further comprising: tilting each of the set of stained histopathology images to generate a plurality of patches; and determining the imaging features from the plurality of patches. (0)

13.-14. (canceled) (0)

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Application US20200075169

MULTI-MODAL APPROACH TO PREDICTING IMMUNE INFILTRATION BASED ON INTEGRATED RNA EXPRESSION AND IMAGING FEATURES

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