Deep learning model targeting cancer surrounding tissues for accurate cancer diagnosis based on histopathological images.

Accurate and fast histological diagnosis of cancers is crucial for successful treatment. The deep learning-based approaches have assisted pathologists in efficient cancer diagnosis. The remodeled microenvironment and field cancerization may enable the cancer-specific features in the image of non-cancer regions surrounding cancer, which may provide additional information not available in the cancer region to improve cancer diagnosis. Here, we proposed a deep learning framework with fine-tuning target proportion towards cancer surrounding tissues in histological images for gastric cancer diagnosis. Through employing six deep learning-based models targeting region-of-interest (ROI) with different proportions of no-cancer and cancer regions, we uncovered the diagnostic value of non-cancer ROI, and the model performance for cancer diagnosis depended on the proportion. Then, we constructed a model based on MobileNetV2 with the optimized weights targeting non-cancer and cancer ROI to diagnose gastric cancer (DeepNCCNet). In the external validation, the optimized DeepNCCNet demonstrated excellent generalization abilities with an accuracy of 93.96%. In conclusion, we discovered a non-cancer ROI weight-dependent model performance, indicating the diagnostic value of non-cancer regions with potential remodeled microenvironment and field cancerization, which provides a promising image resource for cancer diagnosis. The DeepNCCNet could be readily applied to clinical diagnosis for gastric cancer, which is useful for some clinical settings such as the absence or minimum amount of tumor tissues in the insufficient biopsy.