Development and Validation of a CT-Based Radiomics Nomogram in Patients With Anterior Mediastinal Mass: Individualized Options for Preoperative Patients - Zhou, Zhou; Qu, Yanjuan; Zhou, Yurong; Wang, Binchen; Hu, Weidong; Cao, Yiyuan (Frontiers in Oncology, 2022)
Background: To improve the preoperative diagnostic accuracy and reduce the nontherapeutic thymectomy rate, we established a comprehensive predictive nomogram based on radiomics data and computed tomography (CT) features and further explored its potential use in clinical decision-making for anterior mediastinal masses (AMMs). Methods: A total of 280 patients, including 280 with unenhanced CT (UECT) and 241 with contrast-enhanced CT (CECT) scans, all of whom had undergone thymectomy for AMM with confirmed histopathology, were enrolled in this study. A total of 1288 radiomic features were extracted from each labelled mass. The least absolute shrinkage and selection operator model was used to select the optimal radiomic features in the training set to construct the radscore. Multivariate logistic regression analysis was conducted to establish a combined clinical-radiographic-radscore model, and an individualized prediction nomogram was developed. Results: In the UECT dataset, radscore and UECT ratio were selected for the nomogram. The combined model achieved higher accuracy (AUC: 0.870) than the clinical model (AUC: 0.752) for prediction of therapeutic thymectomy probability. In the CECT dataset, the clinical and combined models achieved higher accuracy (AUC: 0.851 and 0.836, respectively) than the radscore (AUC: 0.618) for prediction of therapeutic thymectomy probability. Conclusions: In patients who underwent UECT only, a nomogram integrating the radscore and UECT ratio achieved good accuracy in predicting therapeutic thymectomy in AMMs. However, the use of radiomics in patients with CECT scans did not improve prediction performance; therefore, a clinical model is recommended.