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Background <p>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.</p> Materials and methods <p>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.</p> Results <p>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 <i>p</i> &lt; 0.05). The combined clinical-radiomic model did not provide incremental benefit (AUC: 0.843 vs. 0.838, <i>p</i> = 0.891). SHAP analysis indicated that features reflecting peritumoral structural irregularity and enhancement heterogeneity were key predictors of NAC resistance.</p> Conclusion <p>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.</p> Critical relevance statement <p>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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Integrated tumor/peritumoral MRI features enhance neoadjuvant chemotherapy (NAC) response prediction.</p> </ItemContent> <ItemContent> <p>T1CE MRI best captures tumor-microenvironment treatment interactions.</p> </ItemContent> <ItemContent> <p>Non-invasive radiomics model outperforms clinical factors for therapy adjustment.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Intratumoral and peritumoral radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in rhabdomyosarcoma: a multicenter retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月5日

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  • Ge Zhang,
  • Yun Peng,
  • Yan Su,
  • Lin Mei,
  • Jugao Fang,
  • Yuanhu Liu,
  • Huanming Wang,
  • Hongcheng Song,
  • Dong Guo,
  • Guoxia Yu,
  • Shengcai Wang,
  • Xin Ni

摘要

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.

Graphical Abstract