Radiomics-machine learning model for predicting invasiveness of subcentimeter subsolid lung adenocarcinoma: a validation study with external cohort and SHAP interpretability

BackgroundPreoperative discrimination of invasive adenocarcinoma (IAC) from pre-invasive lesions in subcentimeter subsolid nodules (SSNs) remains challenging using conventional computed tomography (CT). We aimed to develop and validate an interpretable radiomics-machine learning (ML) model for predi...

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Autori principali:	Wenfeng Feng, Ruiting Chang, Tiezhi Li, Xiaolong Wang, Zhihong Gao, Xu Yang, Yuling Yin, Yuqiang Zuo
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Frontiers Media S.A. 2026-03-01
Serie:	Frontiers in Oncology
Soggetti:	decision curve analysis explainable artificial intelligence lung adenocarcinoma machine learning radiomics
Accesso online:	https://www.frontiersin.org/articles/10.3389/fonc.2026.1668102/full
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