Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.02.233197v1?rss=1 Authors: Diao, J. A., Chui, W. F., Wang, J. K., Mitchell, R. N., Rao, S. K., Resnick, M. B., Lahiri, A., Maheshwari, C., Glass, B., Mountain, V., Kerner, J. K., Montalto, M. C., Khosla, A., Wapinski, I. N., Beck, A. H., Taylor-Weiner, A., Elliott, H. Abstract: While computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction, lack of interpretability remains a significant barrier to clinical integration. In this study, we present a novel approach for predicting clinically-relevant molecular phenotypes from histopathology whole-slide images (WSIs) using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5,700 WSIs to train deep learning models for high-resolution tissue classification and cell detection across entire WSIs in five cancer types. Combining cell- and tissue-type models enables computation of 607 HIFs that comprehensively capture specific and biologically-relevant characteristics of multiple tumors. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment (TME) and can predict diverse molecular signatures, including immune checkpoint protein expression and homologous recombination deficiency (HRD). Our HIF-based approach provides a novel, quantitative, and interpretable window into the composition and spatial architecture of the TME. Copy rights belong to original authors. Visit the link for more info