Interpretable deep learning model to predict lymph node metastasis in early gastric cancer using whole slide images.

ABSTRACT: In early gastric cancer (EGC), the presence of lymph node metastasis (LNM) is a crucial factor for determining the treatment options. Endoscopic resection is used for treatment of EGC with minimal risk of LNM. However, owing to the lack of definitive criteria for identifying patients who require additional surgery, some patients undergo unnecessary additional surgery. Considering that histopathologic patterns are significant factor for predicting lymph node metastasis in gastric cancer, we aimed to develop a machine learning algorithm which can predict LNM status using hematoxylin and eosin (H&E)-stained images. The images were obtained from several institutions. Our pipeline comprised two sequential approaches including a feature extractor and a risk classifier. For the feature extractor,

SUBMITTER: Sung YN 

PROVIDER: S-EPMC11301296 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature