Histological subtypes of lung cancers are critical for clinical treatment decision. The aim of this study is to compare the diagnostic performance of multiple radiomics models in differentiating PGL and MIA in pulmonary GGN, in order to identify the most optimal diagnostic model.Patients presenting with GGNs on lung CT, confirmed as PGL or MIA through surgical pathology between October 2015 and June 2023, were included. The GGNs were randomly divided into training and validation sets at a 7:3 ratio. Clinical imaging characteristics were analyzed by univariate and multivariate logistic regression to identify independent risk factors for predicting MIA, leading to the development of a clinical model. ITK-SNAP and Pyradiomics were employed for segmentation and radiomics feature extraction. Subsequently, radiomics and combined models were established. The diagnostic performance of the three models was compared using ROC curves and quantitatively assessed by AUC, accuracy, specificity, and sensitivity.A total of 116 cases of GGNs with pathologically confirmed PGLs and MIAs were included. The clinical model identified three independent predictors. The radiomics model identified seven distinct radiomic features. A combined model was constructed by integrating clinical imaging features with radiomic features. In the training set, the combined model demonstrated a higher AUC than the radiomics model, with AUCs of 0.87 and 0.85 respectively. In the validation set, the radiomics model outperformed the combined model with an AUC of 0.83 versus 0.82. Notably, the radiomics model achieved the highest accuracy and specificity, while the combined model demonstrated the highest sensitivity. However, both models performed significantly better than the clinical model.The independent radiomics model can serve as a rapid, non-invasive diagnostic tool for differentiating between the PGL and MIA.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.