Postoperative Relapse Prediction in Patients With Ewing Sarcoma Using Computed Tomography–Based Radiomics Models Covering Tumor Per Se and Peritumoral Signatures

OBJECTIVE: We aimed to develop and validate a computed tomography (CT)–based radiomics model for early relapse prediction in patients with Ewing sarcoma (ES). METHODS: We recruited 104 patients in this study. Tumor areas and areas with a tumor expansion of 3 mm were used as regions of interest for radiomics analysis. Six different models were constructed: Pre-CT, CT enhancement (CTE), Pre-CT(+3 mm), CTE(+3 mm), Pre-CT and CTE combined (ComB), and Pre-CT(+3 mm) and CTE(+3 mm) combined (ComB(+3 mm)). All 3 classifiers used a grid search with 5-fold cross-validation to identify their optimal parameters, followed by repeat 5-fold cross-validation to evaluate the model performance based on these parameters. The average performance of the 5-fold cross-validation and the best one-fold performance of each model were evaluated. The AUC (area under the receiver operating characteristic curve) and accuracy were calculated to evaluate the models. RESULTS: The 6 radiomics models performed well in predicting relapse in patients with ES using the 3 classifiers; the ComB and ComB(+3 mm) models performed better than the other models (AUC(-best): 0.820–0.922/0.823–0.833 and 0.799–0.873/0.759–0.880 in the training and validation cohorts, respectively). Although the Pre-CT(+3 mm), CTE(+3 mm,) and ComB(+3 mm) models covering tumor per se and peritumoral CT features preoperatively forecasted ES relapse, the model was not significantly improved. CONCLUSIONS: The radiomics model performed well for early recurrence prediction in patients with ES, and the ComB and ComB(+3 mm) models may be superior to the other models.