ArticleFrontiers in surgery2026
Preoperative risk stratification of mediastinal masses using a predictive model based on CT radiomics and clinical data.
Article in Frontiers in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 ( Results: In the training cohort ( 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.
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