作者
Paul Gamble, Ronnachai Jaroensri, Hongwu Wang, Fraser Tan, Melissa Moran, Trissia Brown, Isabelle Flament-Auvigne, Emad A Rakha, Michael Toss, David J Dabbs, Peter Regitnig, Niels Olson, James H Wren, Carrie Robinson, Greg S Corrado, Lily H Peng, Yun Liu, Craig H Mermel, David F Steiner, Po-Hsuan Cameron Chen
发表日期
2021/7/14
期刊
Communications medicine
卷号
1
期号
1
页码范围
14
出版商
Nature Publishing Group UK
简介
Background
Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results.
Methods
We developed three independent deep learning systems (DLS) to directly predict ER/PR/HER2 status for both focal tissue regions (patches) and slides using hematoxylin-and-eosin-stained (H&E) images as input. Models were trained and evaluated using pathologist annotated slides from three data sources. Areas under the receiver operator characteristic curve (AUCs) were calculated for test sets at both a patch …
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