Authors
Yongming Wu, Yunlu Zhang, Jue Li, Wenpeng Song, Guowei Che, Long Tian
Published in
American journal of cancer research. Volume 16. Issue 9. Pages 3841-3854. Epub Sep 15, 2026.
Abstract
This study aimed to develop and validate a nomogram based on clinical and computed tomography (CT) features for the preoperative prediction of invasiveness in patients with solitary pulmonary ground-glass nodules (GGNs). We retrospectively analyzed 846 patients with solitary GGNs who underwent surgical resection at the Lung Cancer Center of West China Hospital between November 2022 and January 2024. All nodules were pathologically confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Patients were classified into a non-invasive group (including AAH, AIS, and MIA) and an IAC group based on postoperative pathological results. Clinical data and CT features before surgery were collected. Patients were randomly assigned to a training cohort (n = 592) and a validation cohort (n = 254) in a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, followed by multivariate logistic regression to identify independent predictors and construct a nomogram model. The variance inflation factor (VIF) was used to evaluate multicollinearity among the candidate predictors. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Multivariate logistic regression analysis revealed that the maximum tumor diameter (OR = 39.82, 95% CI: 16.29-97.32), consolidation-to-tumor ratio (0.5
PMID:
42908867
Bibliographic data and abstract were imported from PubMed on 23 Oct 2026.
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