Table 1_Preoperative risk stratification of mediastinal masses using a predictive model based on CT radiomics and clinical data.docx

An integrated model was subsequently developed by combining the radiomics score with relevant clinical variables, including tumor size, CYFRA21-1, SCC-Ag, and CA19-9. Model performance was evaluated in the training and internal validation cohorts. No external validation cohort was available.</p>Results<p>In the training cohort (n = 110), the integrated model achieved an area under the curve (AUC) of 0.9117 (95% CI: 0.8586–0.9648) with a sensitivity of 85.25% and a specificity of 81.63%, compared to an AUC of 0.8985 (95% CI: 0.8407–0.9562) for the radiomics-only model.

In the internal validation cohort (n = 47), the integrated model maintained stable diagnostic performance with an AUC of 0.8718 (95% CI: 0.7739–0.9697), a sensitivity of 84.62%, and a specificity of 71.43%, whereas the AUC for the radiomics-only model was 0.8599 (95% CI: 0.7488–0.9709).</p>Conclusion<p>A model integrating radiomics and clinical data may serve as a potentially effective tool for preoperative risk stratification of mediastinal tumors; this model shows great promise for optimizing individualized surgical planning.

However, as this study was a single-center, retrospective design and lacked external validation, the model's generalizability requires further confirmation through large-scale, multicenter prospective studies.</p>