Pathomics in Gastrointestinal Tumors: Research Progress and Clinical Applications.

Abstract

Gastrointestinal tumors are among the malignancies with the highest global incidence and mortality rates, and their diagnosis and treatment heavily rely on histopathological examination. However, traditional pathological assessment faces challenges such as strong subjectivity, heavy workload, and low diagnostic consistency. In recent years, with advancements in high-resolution digital slide scanning technology and the rapid development of deep learning algorithms, pathomics has emerged as a novel tool for the precise diagnosis and treatment of gastrointestinal tumors. By extracting high-throughput quantitative features from whole slide images and combining machine learning and deep learning techniques, pathomics enables automated tumor typing, prognosis prediction, and treatment response evaluation. This article reviews the research progress of pathomics in gastrointestinal tumors, focusing on its applications in gene mutation prediction, prognosis assessment, and treatment response prediction, while analyzing current challenges and future directions.