TY - JOUR T1 - Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning JF - bioRxiv DO - 10.1101/645143 SP - 645143 AU - Korsuk Sirinukunwattana AU - Enric Domingo AU - Susan Richman AU - Keara L Redmond AU - Andrew Blake AU - Clare Verrill AU - Simon J Leedham AU - Aikaterini Chatzipli AU - Claire Hardy AU - Celina Whalley AU - Chieh-Hsi Wu AU - Andrew D Beggs AU - Ultan McDermott AU - Philip Dunne AU - Angela A Meade AU - Steven M Walker AU - Graeme I Murray AU - Leslie M Samuel AU - Matthew Seymour AU - Ian Tomlinson AU - Philip Quirke AU - Tim Maughan AU - Jens Rittscher AU - Viktor H Koelzer AU - on behalf of S:CORT consortium Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/05/23/645143.abstract N2 - Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi- omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation. ER -