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Postoperative Relapse Prediction in Patients With Ewing Sarcoma Using Computed Tomography-Based Radiomics Models
Ying Liu1, Ping Yin1, Jingjing Cui2
1From the Department of Radiology, Peking University People's Hospital.
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.
