Nuclear pleomorphism, defined herein as the extent of abnormalities in the overall appearance of tumor nuclei, is one of the components of the three-tiered breast cancer grading. Given that nuclear pleomorphism reflects a continuous spectrum of variation, we trained a deep neural network on a large variety of tumor regions from the collective knowledge of several pathologists, without constraining the network to the traditional three-category classification. We also motivate an additional approach in which we discuss the additional benefit of normal epithelium as baseline, following the routine clinical practice where pathologists are trained to score nuclear pleomorphism in tumor, having the normal breast epithelium for comparison. In multiple experiments, our fully-automated approach could achieve top pathologist-level performance in select regions of interest as well as at whole slide images, compared to ten and four pathologists, respectively.
@article{arxiv.2012.04974,
title = {Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer},
author = {Caner Mercan and Maschenka Balkenhol and Roberto Salgado and Mark Sherman and Philippe Vielh and Willem Vreuls and Antonio Polonia and Hugo M. Horlings and Wilko Weichert and Jodi M. Carter and Peter Bult and Matthias Christgen and Carsten Denkert and Koen van de Vijver and Jeroen van der Laak and Francesco Ciompi},
journal= {arXiv preprint arXiv:2012.04974},
year = {2020}
}