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Predicting treatment response and the course of a patient’s disease is critical in selecting therapy and in helping patients to plan their lives. Despite the rich data produced by genomic and imaging platforms, the accuracy of prognostication for patients diagnosed with cancer can be highly variable, often relying on classification by only a handful of molecular biomarkers or subjective interpretation of histology. While deep learning has emerged as a powerful technology for learning from unstructured images or other high-dimensional data, its application has largely focused on classification and has not widely explored predicting the timing of disease progression, overall survival, or other time-to-event clinical outcomes. In this talk, Dr. Cooper will discuss recent advances in developing deep-learning based survival models for predicting cancer outcomes from genomic and digital pathology imaging data. He will show how conventional survival models can be combined with convolutional networks or other neural networks to learn patterns associated with patient outcomes in digital pathology images or genomic signatures. Using gliomas as a driving use case, he will describe how these models can combine histology and genomics to provide unified and highly accurate predictions of overall survival, and illustrate how these models can be deconstructed to improve validation and reveal biological insights.

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