Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images

• Published in: Cancers Vol. 16; no. 11; p. 2132
• Main Authors: Ou, De-Xiang, Lu, Chao-Wen, Chen, Li-Wei, Lee, Wen-Yao, Hu, Hsiang-Wei, Chuang, Jen-Hao, Lin, Mong-Wei, Chen, Kuan-Yu, Chiu, Ling-Ying, Chen, Jin-Shing, Chen, Chung-Ming, Hsieh, Min-Shu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.06.2024; MDPI
• Subjects: Adenocarcinoma; Artificial intelligence; Candidates; Classification; Complications and side effects; Decision making; Deep learning; Development and progression; Diseases; Health aspects; Image processing; Lung cancer; Lung cancer, Non-small cell; Machine learning; Medical imaging equipment; Medical research; Medicine, Experimental; Pathology; Patients; Prediction models; Prognosis; Radiomics; Relapse; Survival; Tomography; Tumors; Taiwan; Japan; deep learning; spread through air space; pathology; digital histology; lung adenocarcinoma; whole slide image

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers o...