H&E-stained Whole Slide Deep Learning Predicts SPOP Mutation State in Prostate Cancer

Abstract The genetic basis of histological phenotype is well known in molecular pathology, such as CDH1 loss leading to a lobular rather than ductal phenotype in breast cancer. Unfortunately, these phenotypes are qualitative. Moreover, a single genetic alteration may evidence multiple and non-unique histologic features, such as TMPRSS2-ERG fusion giving rise to macronuclei, blue-tinged mucin, and cribriform pattern. A quantitative model to genetically interpret the histology is desirable to guide downstream immunohistochemistry, genomics, and precision medicine. We constructed a statistical model that predicts whether or not SPOP is mutated in prostate cancer, given only the digital whole slide after standard hematoxylin and eosin [H&E] staining. Using a cohort of 177 prostate cancer patients where 20 had mutant SPOP, we trained multiple ensembles of residual networks, accurately distinguishing SPOP mutant from…


Link to Full Article: H&E-stained Whole Slide Deep Learning Predicts SPOP Mutation State in Prostate Cancer