Preoperative risk stratification of mediastinal masses using a predictive model based on CT radiomics and clinical data

Abstract

Objective Mediastinal masses pose substantial diagnostic challenges because of their complex anatomical relationships and heterogeneous pathological characteristics. This study aimed to develop and internally validate a multiparametric predictive model integrating CT radiomics and clinical variables, including the serum tumor markers CYFRA21-1, SCC-Ag, and CA19-9, for preoperative stratification of mediastinal lesions into high- and low-risk categories according to their invasive or malignant potential. Methods This single-center retrospective study included 157 patients who underwent surgical resection of mediastinal tumors at the Third Affiliated Hospital of Soochow University between January 2010 and October 2024. Clinical, imaging, and laboratory data were collected, and two radiologists independently delineated and evaluated the lesions on preoperative contrast-enhanced CT images. Patients were randomly allocated to a training cohort (n = 110) and an internal validation cohort (n = 47). A radiomics model was developed using least absolute shrinkage and selection operator (LASSO) regression to select informative features from 852 extracted radiomics features. 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. Results 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). Conclusion 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.