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Updated: May 26, 2026
A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Advancing lung adenocarcinoma diagnosis using peri-nodular features: a CT radiomics study for determining
Cuiping Han1, Gaofeng Shi2, Jialiang Ren3
1Department of CT/MR, Xingtai People's Hospital, Xingtai, China.
Background:
For sub-centimetre pure ground-glass nodules (pGGNs) in the lungs, accurately predicting their invasiveness remains a clinical challenge. This study aimed to assess the diagnostic value of peri-nodular radiomics features on enhanced computed tomography (CT) for predicting invasiveness, and develop a combined radiomics-clinical model to improve preoperative evaluation in early-stage lung adenocarcinoma (LUAD).
Methods:
This retrospective study analyzed patients with pathologically confirmed pGGNs from The Fourth Hospital of Hebei Medical University (training/internal validation: 309 nodules) and Xingtai People's Hospital (external validation: 38 nodules). Radiomics features were extracted from the nodule core and its surrounding 0-3 and 3-5 mm regions on CT scans. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression was used to build predictive models for distinguishing non-invasive from invasive lesions. The dataset was split in a 7:3 ratio for training and internal validation. Model performance was assessed using the area under the receiver operating characteristic (ROC) curves (AUC) and decision curve analysis (DCA). A combined nomogram integrating radiomics and clinical features was also developed.
Results:
The combined intra-nodular and peri-nodular 0-3 mm radiomics model achieved the highest diagnostic performance in the validation set [AUC =0.847, 95% confidence interval (CI): 0.752-0.943], outperforming models based solely on intra-nodular (AUC =0.828) or peri-nodular features (AUC =0.800). The combined model further improved diagnostic accuracy (AUC =0.857), and DCA demonstrated its added clinical utility. A personalized nomogram incorporating RadScore and air bronchogram signs demonstrated potential clinical utility.
Conclusions:
Radiomics features from the peri-nodular 0-3 mm region significantly enhance the prediction of invasiveness in subcentimetric pGGNs. The combined radiomics-clinical model offers a promising tool for individualized decision-making in early-stage LUAD.
