Abstract
Epidermal growth factor receptor 2 (HER2) has been widely recognized as one of the targets for bladder cancer immunotherapy. The key to implementing personalized treatment for bladder cancer patients lies in achieving rapid and accurate diagnosis. To tackle this challenge, we have pioneered the application of deep learning techniques to predict HER2 expression status from H&E-stained pathological images of bladder cancer, bypassing the need for intricate IHC staining or high-throughput sequencing methods. Our model, when subjected to rigorous testing within the cohort from the People's Hospital of Wuhan University, which encompasses 106 cases, has exhibited commendable performance on both the validation and test datasets. Specifically, the validation set yielded an AUC of 0.92, an accuracy of 0.86, a sensitivity of 0.87, a specificity of 0.83, and an F1 score of 86.7%. The corresponding metrics for the test set were 0.88 for AUC, 0.67 for accuracy, 0.56 for sensitivity, 0.75 for specificity, and 77.8% for F1 score. Additionally, in a direct comparison with pathologists, our model demonstrated statistically superior performance, with a p-value less than 0.05, highlighting its potential as a powerful diagnostic tool.