Deep learning-based prediction of HER2 status and trastuzumab treatment efficacy of gastric adenocarcinoma based on morphological features

Background First-line treatment for advanced gastric adenocarcinoma (GAC) with human epidermal growth factor receptor 2 (HER2) is trastuzumab combined with chemotherapy. In clinical practice, HER2 positivity is identified through immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH), whereas deep learning (DL) can predict HER2 status based on tumor histopathological features. However, it remains uncertain whether these deep-learning-derived features can predict the efficacy of anti-HER2 therapy. Methods We analyzed a cohort of 300 consecutive surgical specimen patients undergoing HER2 testing and 41 biopsy specimens receiving trastuzumab-based therapy for HER2-positive GAC. Results We developed a convolutional neural network (CNN) model that achieved an area under the curve (AUC) value of 0.847 in predicting HER2 amplification. Additionally, our method achieved an AUC of 0.903 in predicting HER2 status specifically in patients with HER2 2 + expression. Furthermore, our classifier was trained on 41 HER2-positive gastric biopsy specimens that had undergone trastuzumab treatment; our model demonstrated an AUC of 0.833 for the (CR + PR) / (SD + PD) subgroup. Conclusion This work explores an algorithm that utilize hematoxylin-eosin (H&E) staining to accurately predict HER2 status and assess the response to trastuzumab in GAC, potentially facilitating clinical decision-making.