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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Detalhes bibliográficos

Principais autores:	Wenfeng Feng, Ruiting Chang, Tiezhi Li, Xiaolong Wang, Zhihong Gao, Xu Yang, Yuling Yin, Yuqiang Zuo
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2026-03-01
coleção:	Frontiers in Oncology
Assuntos:	decision curve analysis explainable artificial intelligence lung adenocarcinoma machine learning radiomics
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/fonc.2026.1668102/full
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