Resumen
Initial management approaches for prevascular mediastinal tumors (PMTs) can be divided into two categories: direct surgery and core needle biopsy (CNB). Although the gold standard diagnostic method is histopathological examination, the selection of the initial management between direct surgery and CNB is more urgent for patients with PMTs, compared with the definite diagnosis of PMT subtypes. The study aimed to develop clinical?radiomics machine learning (ML) classification models to differentiate patients who needed direct surgery from patients who needed CNB, among the patients with PMTs. An ensemble learning model, combining five ML models, had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (86.1%; p < 0.05), which may be used as clinical decision support system to facilitate the selection of the initial management of PMT.