Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images
This provides an objective, holistic analysis method for breast cancer prognosis, addressing limitations in current pathological practices.
The study tackled the problem of subjective and localized tumor proliferation assessment in breast cancer by developing a data-driven integrative deep learning approach, achieving pathologist-level performance in three-class categorical severity prediction and a Spearman's correlation of 0.60 for molecular expression prediction from tissue images.
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.