AUTHOR=Dong Ning , Yan Yumao , Li Yunxin , Li Guochao , Wang Ping , Li Lin , Zhang Hu , Sheng Hui , Sun Xiaoyuan 

TITLE=CT-based habitat radiomics for preoperative differentiation of adenocarcinoma in situ/minimally invasive adenocarcinoma from invasive adenocarcinoma manifesting as ground-glass nodules: a multicenter study

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1660071

DOI=10.3389/fonc.2025.1660071

ISSN=2234-943X

ABSTRACT=ObjectivesTo develop a CT-based habitat radiomics model for preoperative differentiation of adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) from invasive adenocarcinoma (IAC) manifesting as ground-glass nodules (GGNs), and to construct a combined model integrating clinical risk factors for optimizing individualized treatment decisions.MethodsWe retrospectively collected imaging and clinical data from 630 patients with pathologically confirmed ground-glass nodules (GGNs) who underwent surgical resection at two medical centers between January 2020 and December 2024. Patients from Center 1 were randomly divided into training and internal validation sets at a 7:3 ratio, while patients from Center 2 served as the external validation set. Tumor habitats were generated using K-means clustering, and radiomics features were extracted from intratumoral, peritumoral 1mm, peritumoral 2mm, and habitat regions. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and predictive models were constructed using multiple machine learning algorithms. A combined nomogram was developed by integrating the Habitat model, Intratumoral model, and Clinic model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).ResultsIn the training set, the Combined model demonstrated optimal performance (AUC = 0.928), followed by the Habitat model (AUC = 0.924), both significantly outperforming the Intratumoral model (AUC = 0.879), Peritumoral 1mm model (AUC = 0.874), Peritumoral 2mm model (AUC = 0.868), and Clinic model (AUC = 0.807) (P<0.05). In the external validation set, the Combined model maintained superior performance (AUC = 0.897), significantly exceeding all other models (P<0.05). The Habitat model showed the second-best performance in external validation (AUC = 0.840). Hosmer-Lemeshow test and calibration curves demonstrated good calibration for both the Combined and Habitat models across all cohorts. DCA indicated high net benefit for both models in clinical applications.ConclusionCT-based habitat radiomics effectively quantifies intratumoral heterogeneity, significantly improving the differentiation between AIS/MIA and IAC. The combined nomogram integrating habitat features, intratumoral features, and clinical factors demonstrates excellent diagnostic performance and generalizability, providing a reliable preoperative assessment tool for individualized treatment decision-making in ground-glass nodular lung adenocarcinoma.