Evidence map›Paper›PMID 40920666›Full record

ArticlePloS one2025

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 and 4 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Wen ZhaoDepartment of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Ziqian ZhaoDepartment of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Yingxia WangDepartment of Pathology, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Haiyan YangDepartment of Ultrasound, Chongqing General Hospital, Chongqing, China.
Weiyuan ZhangDepartment of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Jianyou ChenDepartment of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Xinhui YangDepartment of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Zhijie DuanDepartment of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Fengyi LiDepartment of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Zhiquan HanDepartment of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Xin ZhangGE Healthcare, Shanghai, China.
Zhilin LiDepartment of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Dan HanDepartment of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Tengfei KeDepartment of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.ORCID https://orcid.org/0000-0001-6462-6449

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBronchiolar 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.

methodsThis 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.

resultsNodule density (solid, ground glass, and subsolid) exhibited a statistically significant difference between the BA and LAC groups (p < 0.01). The following parameters were significantly higher in the LAC group compared to the BA group (all p < 0.05): 2D long diameter, 2D short diameter, 2D average diameter, 2D maximum surface area, 3D long diameter, 3D surface area, 3D volume, and entropy. In contrast, CT value variance was significantly lower in the LAC group than in the BA group (p < 0.01). A nomogram was constructed incorporating density, 2D short diameter, and CT value variance. The area under the curve (AUC) of the nomogram in the training and validation cohorts were 0.821, 0.811.

conclusionThe AI-based nomogram, as a non-invasive preoperative tool, had the potential to enhance diagnostic accuracy for distinguishing BA from LAC.

Indexed as

Adenocarcinoma of LungAdenomaArtificial IntelligenceLung NeoplasmsTomography, X-Ray ComputedAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedNomogramsRetrospective Studies

Identifiers

PMID40920666
PMCPMC12416670

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