Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: A retrospective multicenter study

Purpose: To develop and externally validate an MR-based radiomics nomogram from retrospective multicenter datasets for pretreatment prediction of early relapse (<= 1 year) in osteosarcoma after surgical resection. Methods: This multicenter study retrospectively enrolled 93 patients (training cohort: 62 patients from four hospitals; validation cohort: 31 patients from two hospitals) with clinicopathologically confirmed osteosarcoma who received neoadjuvant chemotherapy and surgical resection at six hospitals between January 2009 and October 2017. Radiomics features were extracted from contrast-enhanced fat-suppressed T1-weighted (CE FS T1-w) images. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature construction. The radiomics nomogram that incorporated the radiomics signature and subjective MRI-assessed candidate predictors was developed to predict early relapse with a multivariate logistic regression model in the training cohort and validated in the external validation cohort. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness. Results: The radiomics signature comprised six selected features and achieved favorable prediction efficacy. The radiomics nomogram incorporating the radiomics signature and subjective MRI-assessed candidate predictors (joint invasion and perivascular involvement) from the multicenter datasets achieved better discrimination in the training cohort (C-index:0.907, 95 % CI: 0.838-0.977) and external validation cohort (C-index: 0.811, 95 % CI: 0.653-0.970), and good calibration. Decision curve analysis suggested that the combined nomogram was clinically useful. Conclusion: The proposed MRI-based radiomics nomogram could provide a non-invasive tool to predict early relapse of osteosarcoma, which has the potential to improve personalized pretreatment management of osteosarcoma.