Multicentre MRI-based machine learning model for noninvasive prediction of pulmonary metastasis in osteosarcoma integrating intra-tumoral heterogeneity features.

OBJECTIVES: To develop and externally validate a multicenter MRI-based machine learning model integrating intra-tumoral heterogeneity (ITH) index, conventional radiomics (C-radiomics) and clinical variables for predicting one-year pulmonary metastasis (PM) in osteosarcoma. MATERIALS AND METHODS: This retrospective study enrolled 320 patients with histologically confirmed osteosarcoma from four institutions, comprising internal (n = 254, Centers A-C) and external sets (n = 66, Center D). Pre-treatment contrast-enhanced T1-weighted fat-suppressed MRI was used for tumor segmentation and feature extraction. ITH features were obtained through supervoxel-based clustering, and C-radiomics features were derived conventionally. An XGBoost model integrating ITH index, C-radiomics, and clinical variables was developed. Model performance was evaluated using ROC, calibration, and decision curve analysis (DCA), with SHAP and subgroup analysis providing interpretability and robustness. RESULTS: Within one year after surgery, 39.4% of patients developed PM. The combined model achieved the highest predictive performance across sets, with an AUC of 0.843 (95% CI: 0.823-0.869), 73.8% accuracy, 78.2% sensitivity, and 81.1% specificity on the independent external test set, outperforming all single- and dual-modality models. Calibration and DCA confirmed strong model reliability and clinical utility across a broad threshold range. The ITH index (OR = 6.723, p = 0.008) and C-radiomics score (OR = 7.962, p = 0.001) were independent predictors of PM. Subgroup analysis demonstrated consistent performance across age, sex, stage, and tumor site (AUC range: 0.801-0.853). CONCLUSIONS: The MRI-based model integrating ITH, C-radiomics, and clinical variables enables accurate, noninvasive prediction of early PM in osteosarcoma, supporting personalized risk stratification and clinical decision-making.