Fig. 4.

GSCNN models integrate genomic and imaging data for improved performance. (A) A hybrid architecture was developed to combine histology image and genomic data to make integrated predictions of patient survival. These models incorporate genomic variables as inputs to their fully connected layers. Here, we show the incorporation of genomic variables for gliomas; however, any number of genomic or proteomic measurements can be similarly used. (B) The GSCNN models significantly outperform SCNN models as well as the WHO paradigm based on genomic subtype and histologic grading.