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Related Experiment Video

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Cluster-Based MR Radiomics Model for Predicting Induction Chemotherapy Response in Nasopharyngeal Carcinoma.

Zhenhuan Huang1, Xuezhao Tu2, Lihang Cao1

  • 1Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian 364000, China (Z.H., L.C., J.Q., Q.Y., D.L.).

Academic Radiology
|March 3, 2026
PubMed
Summary

A new magnetic resonance (MR) radiomics model accurately predicts nasopharyngeal carcinoma (NPC) response to induction chemotherapy (ICT). This cluster-specific approach offers interpretable insights for identifying patients who will benefit from treatment.

Keywords:
Cluster-based radiomicsInduction chemotherapyMagnetic resonance imagingNasopharyngeal carcinomaTumor burden reduction ratio

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Nasopharyngeal carcinoma (NPC) requires effective prediction of treatment response.
  • Induction chemotherapy (ICT) is a key treatment modality for NPC.
  • Accurate prediction of ICT response is crucial for patient management.

Purpose of the Study:

  • To develop a cluster-specific MR radiomics model for predicting ICT response in NPC.
  • To enhance model interpretability using Shapley Additive exPlanations (SHAP).
  • To identify radiomics features that correlate with treatment outcomes.

Main Methods:

  • Retrospective analysis of 225 NPC patients.
  • Development of cluster-specific radiomics models from MR images.
  • Application of machine learning classifiers and SHAP for model building and interpretation.

Main Results:

  • A cluster 1-specific support vector machine model demonstrated high predictive performance (AUCs 0.812/0.800).
  • Cluster 1, the vascularized tumor periphery, showed the strongest correlation with treatment response.
  • SHAP analysis revealed key features related to intratumoral heterogeneity and voxel intensity.

Conclusions:

  • The cluster 1-specific MR radiomics model reliably predicts ICT response in NPC.
  • This approach provides interpretable prognostic insights.
  • The model may aid in identifying patients likely to benefit from ICT.