Predicting HER2 scores from registered HER2 and H&E images

Human epidermal growth factor (HER2) is a predictive and prognostic biomarker whose degree of presence in breast cancer informs prognosis and therapeutic decision making. In clinical practice, it is routinely assessed using immunohistochemical (IHC) staining. A pathologist assigns a score from 0 to 3+ depending on the intensity and distribution of staining - 0 or 1+ scores are classified as HER2 negative, 3+ scores as HER2 positive, and 2+ as equivocal. Unfortunately, variations in HER2 staining and the subjectivity in scoring can lead to inaccuracies. Therefore, we sought to develop an automated method to predict HER2 scores from HER2 and H&E slide images. Our database consisted of 52 adjacent HER2 and H&E tissue sections. Positive regions on HER2 were segmented using a previously developed method. Using 13-fold cross-validation, a truncated Resnet18 was then trained to classify extracted patches using HER2 score as labels for positive regions and a score of 0 for negative regions. Using the same folds, attentionbased multiple instance learning was used to aggregate learned patch embeddings into overall slide-level embeddings, which were subsequently classified. The preliminary method achieved 88% accuracy on 0/1+ and 85% accuracy on 2+ and 3+. Subsequent preliminary experiments qualitatively demonstrate that identified positive regions from HER2 can successfully be transferred over to H&E via image registration. Furthermore, applying the proposed method to predict HER2 score from H&E demonstrates that attention is paid to HER2 positive regions on H&E. Results provide preliminary evidence that HER2 can be localized and therefore scored using H&E images alone.

Automated summary

This study developed an automated method to predict HER2 scores in breast cancer, using immunohistochemical staining images and tissue sections. The preliminary results showed potential for localizing and scoring HER2 using H&E images.