Evaluation of a predictive method for the H&amp;E-based molecular profiling of breast cancer with deep learning

Salim Arslan; Xiusi Li; Julian Schmidt; Julius Hense; Andre Geraldes; Cher Bass; Keelan Brown; Angelica Marcia; Tim Dewhirst; Pahini Pandya; Shikha Singhal; Debapriya Mehrotra; Pandu Raharja-Liu

doi:10.1101/2022.01.04.474882

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.

Competing Interest Statement

Every author was employed by Panakeia Technologies Limited at the time of the writing this paper.

Footnotes

Reordered author as per manuscript's ordering

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