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

Abstract

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.