Background:The presence of visceral pleural invasion (VPI) is associated with increased risk of recurrence and reduced overall survival following surgical resection. We aimed to develop machine learning (ML)-based classification models that integrate clinical variables and both tumoral and peritumoral radiomic features to predict VPI in patients with lung adenocarcinoma before surgery. Methods: We retrospectively enrolled 118 patients, including 80 (68%) without VPI and 38 (32%) with histologically confirmed VPI. All patients underwent preoperative contrast-enhanced CT scans. Tumor volumes were manually segmented, and isotropic expansions of 3, 5, and 10 mm were automatically generated to define peritumoral regions. The dataset was randomly split into training (70%) and validation (30%) cohorts. Radiomic features and clinical data were used to train multiple ML algorithms. Results: Pleural Tag Sign and the Worst Histotype were identified as the strongest clinical predictors of VPI. The combined model, integrating radiomics from the lesion and clinical variables, achieved the highest training accuracy of 0.88 (95% CI: 0.80–0.92) and validation accuracy of 0.83 (95% CI: 0.68–0.92). Conclusions: VPI is associated with detectable alterations in both tumoral and peritumoral microenvironment on contrast-enhanced CT. Incorporating radiomic features with clinical data enabled improved model performance compared to clinical-only models, yielding very good accuracies. This approach may support surgical planning and patient risk stratification. Further prospective studies are needed to validate these findings and assess their clinical impact.

Background: The presence of visceral pleural invasion (VPI) is associated with increased risk of recurrence and reduced overall survival following surgical resection. We aimed to develop machine learning (ML)-based classification models that integrate clinical varia-bles and both tumoral and peritumoral radiomic features to predict VPI in patients with lung adenocarcinoma before surgery. Methods: We retrospectively enrolled 118 patients, including 80 (68%) without VPI and 38 (32%) with histologically confirmed VPI. All pa-tients underwent preoperative contrast-enhanced CT scans. Tumor volumes were manu-ally segmented, and isotropic expansions of 3, 5, and 10 mm were automatically generat-ed to define peritumoral regions. The dataset was randomly split into training (70%) and validation (30%) cohorts. Radiomic features and clinical data were used to train multiple ML algorithms. Results: Pleural Tag Sign and the Worst Histotype were identified as the strongest clinical predictors of VPI. The combined model, integrating radiomics from the lesion and clinical variables, achieved the highest training accuracy of 0.88 (95% CI: 0.80–0.92) and validation accuracy of 0.83 (95% CI: 0.68–0.92). Conclusion: VPI is associated with detectable alterations in both tumoral and peritumoral microenvironment on con-trast-enhanced CT. Incorporating radiomic features with clinical data enabled improved model performance compared to clinical-only models, yielding very good accuracies. This approach may support surgical planning and patient risk stratification. Further prospec-tive studies are needed to validate these findings and assess their clinical impact.