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’ diagnoses. Performance was evaluated using the area under receiver operator characteristic curve (AUC). On the test cohort, ResNet18 achieved the highest AUC (0.956) and sensitivity (0.832) in identifying tumor areas, with an accuracy of 0.904; Random Forest provided high accuracy and AUC values of 0.814 and 0.807 in assessing tumor invasiveness. Pathology assistance improved diagnostic accuracy for junior and intermediate pathologists, with AUC values increasing from 0.547 to 0.759 and 0.656 to 0.769. This study suggests that deep learning and pathomics can enhance diagnostic accuracy, offering valuable support to pathologists.