Machine-learning-based on multimodality radiomics analysis for the Preoperative Prediction for local relapse in osteosarcoma

PURPOSE: This study aimed to identify patients with local relapse (≤ 2 years) in osteosarcoma after surgical resection and make better clinical decisions by constructing a preoperative predictive model based on radiograph and multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS: A retrospective study of 92 consecutive patients (training set, n = 61; testing set, n = 31) with extremity high-grade osteosarcoma were enrolled. The imaging features for each patient were extracted from radiograph, multiparametric MRI (T1WI, T2WI and T1WI-CE). In order to select features, three steps including minimal-redundancy-maximum-relevance (mRMR), least absolute shrinkage and selection operator (LASSO) regression and the random forest recursive feature elimination (RF-RFE) were performed. The classification performance was evaluated with four classifiers: extreme gradient boosting (XGB), logistic regression (LR), support vector machine (SVM) and random forest (RF). The receiver-operating characteristic curve (ROC) and the area under the curve (AUC) were used to evaluate the performance of the classifiers. DeLong’s test was utilized for comparing the AUCs. RESULTS: The performance (AUC, sensitivity, specificity, and accuracy) of four classifiers (RF, SVM, LR and XGB) using radiograph-MRI as image inputs were stable (all Hosmer–Lemeshow index > 0.05) with the fair to good prognosis efficacy. The RF classifier using radiograph-MRI features as training inputs exhibited better performance (AUC = 0.806, 0.868) than that using MRI-only (AUC = 0.774, 0.771) and radiograph-only (AUC = 0.613 and 0.627) in the training and testing sets (p < 0.05) while the other three classifiers showed no difference between MRI only and radiograph-MRI models. CONCLUSION: The tumoral radiograph and multiparametric MRI radiomics model can promisingly predict local relapse in extremity high-grade osteosarcoma. Our results highlighted the potential value of the tumoral radiomic model in osteosarcoma management.