Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.

ABSTRACT: Most classification efforts for primary subtypes of lung adenocarcinoma (LUAD) have not yet been integrated into clinical practice. This study explores the feasibility of combining deep learning and pathomics to identify tumor invasiveness in LUAD patients, highlighting its potential clinical value in assisting junior and intermediate pathologists. We retrospectively analyzed whole slide image (WSI) data from 289 patients with surgically resected ground-glass nodules (GGNs). First, three ResNet deep learning models were used to identify tumor regions. Second, features from the best-performing model were extracted to build pathomics using machine learning classifiers. Third, the accuracy of pathomics in predicting tumor invasiveness was compared with junior and intermediate pathologists' di

SUBMITTER: Du H 

PROVIDER: S-EPMC11810995 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature