Current analysis of tumor proliferation, the most salient breast cancer prognostic biomarker, is limited to subjective mitosis counting by pathologists in localized regions of tissue images. This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model. Our approach achieves pathologistlevel performance on three-class categorical tumor severity prediction. It additionally pioneers prediction of molecular expression data from a tissue image, obtaining a Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated RNA expression. Furthermore, our framework is applied to identify over two hundred unprecedented biomarkers critical to the accurate assessment of tumor proliferation, validating our proposed integrative pipeline as the first to holistically and objectively analyze histopathological images.
Shah et al. REF presented the first completely data-driven model integrated numerous biologically salient classifiers for invasive breast cancer prognosis.
Deep learning assessment of tumor proliferation in breast cancer histological images
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Computer Science