Artificial intelligence-based CT histogram parameters differentiating bronchiolar adenoma and lung adenocarcinomas: A two-center study

Wen Zhao, Ziqian Zhao, Yingxia Wang, Haiyan Yang, Weiyuan Zhang, Jianyou Chen, Xinhui Yang, Zhijie Duan, Fengyi Li, Zhiquan Han, Xin Zhang, Zhilin Li, Dan Han and Tengfei Ke

PLOS ONE, 2025, vol. 20, issue 9, 1-13

Abstract: Purpose: Bronchiolar adenoma (BA) is a rare benign pulmonary neoplasm originating from the bronchial mucosal epithelium and mimics lung adenocarcinoma (LAC) both radiographically and microscopically. This study aimed to develop a nomogram for distinguishing BA from LAC by integrating clinical characteristics and artificial intelligence (AI)-derived histogram parameters across two medical centers. Methods: This retrospective study included 215 patients with diagnoses confirmed by postoperative pathology from two medical centers. Medical center 1 provided 151 patients (68 BA and 83 LAC nodules) as the training cohort, while medical center 2 contributed 64 patients (28 BA and 36 LAC nodules) as the external validation cohort. Risk predictors and the nomogram were developed using clinical characteristics and AI-derived histogram parameters. Results: Nodule density (solid, ground glass, and subsolid) exhibited a statistically significant difference between the BA and LAC groups (p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0331336

DOI: 10.1371/journal.pone.0331336

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