Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer.

ABSTRACT: The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes occurring in invasive areas are still not well understood. This study aimed to explore the morphological differences between invasive and non-invasive areas in early esophageal cancer specimens that have undergone endoscopic submucosal dissection (ESD), using artificial intelligence (AI) to shed light on the underlying mechanisms. In this study, data from 75 patients with esophageal squamous cell carcinoma (ESCC) were analyzed and endoscopic assessments were conducted to determine submucosal (SM) invasion. An AI model, specifically a Clustering-constrained Attention Multiple Instance Learning model (CLAM), was developed to predict the depth of

SUBMITTER: Urabe A 

PROVIDER: S-EPMC11875758 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature