A Deeplearning Framework To Predict Cancer Treatment Response From Histopathology Images Through Imputed Transcriptomics Danhtai Hoang 1 by Danh-tai Hoang  1, Gal Dinstag2, Eldad D. Shulman  3, Leandro C. Hermida4, 5, instant download after payment.

Advances in artifcial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT–DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT–DeepPT successfully predicts true responders in fve independent patient cohorts involving four diferent treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specifc training on the treatment evaluation cohorts.