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Predicting cancer outcomes from histology and genomics using convolutional networks

2018; National Academy of Sciences; Volume: 115; Issue: 13 Linguagem: Inglês

10.1073/pnas.1717139115

1091-6490

Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A. Gutman, Jill S. Barnholtz‐Sloan, José E. Velázquez Vega, Daniel J. Brat, Lee Cooper,

Cell Image Analysis Techniques

Significance Predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment. Advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. We developed a computational approach based on deep learning to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers. This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. Our approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes.