Objective To investigate and compare the value of clinical, intratumoral, and peritumoral radiomic features in the preoperative differentiation of minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) of the lung, and to develop a high-performance integrated predictive model to guide surgical decision-making. MethodsWe retrospectively enrolled patients with postoperative pathologically confirmed MIA or IAC at the Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, from 2020 to 2022. Clinical data and preoperative CT images were collected. Intratumoral and peritumoral (a 3 mm extension from the tumor margin) regions of interest were delineated, and high-throughput radiomic features were extracted using PyRadiomics. After feature selection, various machine learning algorithms were employed to construct predictive models based on clinical features, intratumoral features, peritumoral features, and combined features. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), decision curve analysis, and calibration curves. Results A total of 665 patients were included. There were 208 males and 457 females with a mean age of (57.67±11.21) years. The radiomics model combining intratumoral and peritumoral features (AUC=0.924) outperformed the single-region models. After further integrating the optimal radiomics model with the clinical model, the AUC of the resulting fusion model increased to 0.935 in the validation set. Furthermore, Delong's test, the net reclassification improvement, and the integrated discrimination improvement all confirmed that the predictive efficacy of the fusion model was significantly superior to that of any individual model. Conclusion The machine learning model integrating clinical and radiomic features can effectively differentiate MIA from IAC preoperatively, providing reliable decision support for the selection of personalized surgical strategies.