Intratumoral and peritumoral radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in rhabdomyosarcoma: a multicenter retrospective cohort study.
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BACKGROUND: Pediatric rhabdomyosarcoma (RMS), the most common soft-tissue sarcoma in children, exhibits heterogeneous responses to neoadjuvant chemotherapy (NAC), necessitating reliable biomarkers for early prediction. This multicenter study evaluates MRI-derived radiomic features of intratumoral and peritumoral regions to predict NAC response in the largest pediatric RMS cohort to date. MATERIALS AND METHODS: A retrospective analysis included 519 RMS patients from three Chinese centers. Radiologists manually segmented tumors and 2-mm peritumoral regions on standardized T1-weighted contrast-enhanced (T1CE) and T2-weighted fat-saturated (T2Fs) MRI sequences. PyRadiomics extracted 1015 radiomic features, with robustness ensured (ICC ≥ 0.80) and predictive features selected via LASSO regression. Twelve XGBoost models (intra-/peritumoral, multisequence) were developed, validated internally/externally, and compared using DeLong's test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). SHAP analysis interpreted feature contributions. Clinical variables (age, fusion gene) were assessed for incremental value. RESULTS: The T1CE-based combined intratumoral-peritumoral model (T1CE_IntraPeri2mm) demonstrated the best generalizability, achieving AUCs of 0.917 (training), 0.760 (internal validation), 0.837 (external test1) and 0.843 (external test2). It significantly outperformed intratumoral-only and multisequence fusion models in DeLong, NRI, and IDI analyses (all p < 0.05). The combined clinical-radiomic model did not provide incremental benefit (AUC: 0.843 vs. 0.838, p = 0.891). SHAP analysis indicated that features reflecting peritumoral structural irregularity and enhancement heterogeneity were key predictors of NAC resistance. CONCLUSION: T1CE-based peritumoral radiomics robustly predicts NAC response in pediatric RMS, emphasizing tumor-microenvironment interactions. This approach offers a non-invasive tool for personalized therapy stratification. CRITICAL RELEVANCE STATEMENT: This study establishes peritumoral MRI radiomics as a critical predictor of chemotherapy response in pediatric rhabdomyosarcoma, addressing the unmet need for non-invasive biomarkers and advancing precision oncology through tumor-microenvironment interaction analysis in clinical radiology practice. KEY POINTS: Integrated tumor/peritumoral MRI features enhance neoadjuvant chemotherapy (NAC) response prediction. T1CE MRI best captures tumor-microenvironment treatment interactions. Non-invasive radiomics model outperforms clinical factors for therapy adjustment.