Predicting cancer outcomes from histology and genomics using convolutional networks

Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A. Gutman, Jill S. Barnholtz-Sloan, Jose Enrique Velazquez Vega, Daniel J. Brat, Lee A.D. Cooper*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Cancer histology reflects underlying molecular processes and disease progression, and contains rich phenotypic information that is predictive of patient outcomes. In this study, we demonstrate a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how this approach can integrate information from both histology images and genomic biomarkers to predict time-to-event patient outcomes, and demonstrate performance surpassing the current clinical paradigm for predicting the survival of patients diagnosed with glioma. We also provide techniques to visualize the tissue patterns learned by these deep learning survival models, and establish a framework for addressing intratumoral heterogeneity and training data deficits.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Oct 3 2017
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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