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arXiv · 1610.03467

Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images

Manan Shah·Christopher Rubadue·David Suster·Dayong Wang
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Abstract

Current analysis of tumor proliferation, the most salient prognostic biomarker for invasive breast cancer, 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 pathologist-level 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.

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Manan Shah, Christopher Rubadue, David Suster, Dayong Wang. 2016-10-11. Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images. https://arxiv.org/abs/1610.03467

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