A Hybrid Pipeline to Assess Oestrogen Receptor Stained Nuclei in Invasive Breast Cancer

Hammam M. AlGhamdi, Maryam Althobiti, Talha Qaiser, Andrew R. Green, Shan E Ahmed Raza, Emad A. Rakha, and Nasir Rajpoot

Keywords: Computational Pathology, Nuclear detection and classification, Oestrogen (ER) receptors, Breast cancer

TL;DR: We propose a novel hybrid pipeline that combines deep learning (DL) and relatively inexpensive colour histogram features in order to recognise and assess different cell types, including ER positive (ER+) and negative (ER-) tumour cells.

Abstract: Oestrogen Receptor (ER) expression status in invasive breast cancer not only determines the use of endocrine therapy but its level of expression also provides critical prognostic and predictive information. Digital pathology opens new avenues for applications of computational algorithms to provide objective and accurate assessment of ER status. In this study, we propose a novel hybrid pipeline that combines deep learning (DL) and relatively inexpensive colour histogram features in order to recognise and assess different cell types, including ER positive (ER+) and negative (ER-) tumour cells. Our pipeline consists of a deep neural network for simultaneous detection and classification (SimNuc-Net) of nuclei, followed by unsupervised hierarchical clustering. First, the SimNuc-Net classifies ER+ and ER- invasive tumour nuclei and nuclei of other cell types. We then classify all ER+ nuclei into four categories based on staining intensity. We show that the proposed pipeline outperforms the DL only pipeline and other existing techniques.