[Intelligent prediction of HER2 status based on breast histopathology]

Objective: To study the association between histopathological features and HER2 overexpression/amplification in breast cancers using deep learning algorithms. Methods: A total of 345 HE-stained slides of breast cancer from 2012 to 2018 were collected at the China-Japan Friendship Hospital, Beijing, China. All samples had accurate diagnosis results of HER2 which were classified into one of the 4 HER2 expression levels (0, 1+, 2+, 3+). After digitalization, 204 slides were used for weakly supervised model training, and 141 used for model testing. In the training process, the regions of interest were extracted through cancer detected model and then input to the weakly supervised classification model to tune the model parameters. In the testing phase, we compared performance of the single- and double-threshold strategies to assess the role of the double-threshold strategy in clinical practice. Results: Under the single-threshold strategy, the deep learning model had a sensitivity of 81.6% and a specificity of 42.1%, with the AUC of 0.67 [95% confidence intervals (0.560,0.778)]. Using the double-threshold strategy, the model achieved a sensitivity of 96.3% and a specificity of 89.5%. Conclusions: Using HE-stained histopathological slides alone, the deep learning technology could predict the HER2 status using breast cancer slides, with a satisfactory accuracy. Based on the double-threshold strategy, a large number of samples could be screened with high sensitivity and specificity.