Comparison of the classification of HER2 from whole-slide images between pathologists and a deep learning model

Abstract

AbstractHER2 (human epidermal growth factor receptor 2) is a protein that is found on the surface of some cells, including breast cells. HER2 plays a role in cell growth, division, and repair, and when it is overexpressed, it can contribute to the development of certain types of cancer, particularly breast cancer. HER2 overexpression occurs in approximately 20% of cases, and it is associated with more aggressive tumor phenotypes and poorer prognosis. This makes its status an important factor in determining treatment options for breast cancer. While HER2 expression is typically diagnosed through a combination of immunohistochemistry (IHC) and/or fluorescence in situ hybridization (FISH) testing on breast cancer tissue samples, we sought to determine to what extent it is possible to diagnose from H&E-stained specimens. To this effect we trained a deep learning model to classify HER2-positive image patches using a dataset of 10 whole-slide images (5 HER2-positive, 5 HER2-negative). We evaluated the model on a different test set consisting of patches extracted from 10 WSIs (5 HER2-positive, 5 HER2-negative), and we compared the performance against two pathologists on 100 512×512 patches (50 HER2-positive, 50 HER2-negative). Overall, the model achieved an accuracy of 73% while the pathologists achieved 58% and 47%, respectively.