Objective To assess the predictive value of a combined multiclassification model for computed tomography (CT) in the pathological analysis of ground-glass nodules (GGN).

Methods  Pulmonary GGN lesions that were pathologically confirmed as invasive adenocarcinoma (IAC), minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and preinvasive lesions (PILs), were collected from patients who were treated at The First Affiliated Hospital of Xinxiang Medical University between February 2019 and March 2023. A total of 324 nodules were retrospectively collected from 285 patients, and divided into three groups: infiltrating IAC, MIA, and PILs. Radiomics and clinical-CT features were selected through recursive feature elimination and univariate Logistic regression. Seven models were constructed using Logistic regression (LR), support vector machine (SVM), random forest (RF), and integrative learning (stacking).

Results  The hybrid model combining clinical-CT-radiomics features and an integrative strategy showed superior predictive performance, with an accuracy of 0.791, precision of 0.788, specificity of 0.857, recall of 0.790, and F1-Score of 0.789.

Conclusions  The multiclassification joint model based on CT-radiomics is effective in predicting pathological classification of pulmonary GGNs. This model aids in accurate imaging diagnosis and can provide a basis for optimizing treatment plans.