Evaluation of a predictive method for the H&E-based molecular profiling of breast cancer with deep learning
Salim Arslan, Xiusi Li, Julian Schmidt, Julius Hense, André Geraldes, Cher Bass, Keelan Brown, Angelica Marcia, Tim Dewhirst, Pahini Pandya, Shikha Singhal, Debapriya Ghosh Mehrotra, Pandu Raharja-Liu · bioRxiv (Cold Spring Harbor Laboratory) · 2022
Abstract We present a public validation of PANProfiler (ER, PR, HER2), an in-vitro medical device (IVD) that predicts the qualitative status of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) by analysing the hematoxylin and eosin (H&E)-stained tissue scan. In public validation on 648 (ER), 648 (PR) and 560 (HER2) unseen cases with known biomarker status, the device achieves an accuracy of 87% (ER), 83% (PR) and 87% (HER2). The validation offers early evidence of the ability to predict clinically relevant breast biomarkers from an H&E slide in a relevant clinical setting.