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Objective <p>To investigate the value of multimodal MRI radiomics model in predicting Neoadjuvant chemotherapy (NAC) response in Nasopharyngeal carcinoma (NPC) and assess the performance differences between machine learning and deep learning models.</p> Methods <p>370 NPC patients were retrospectively enrolled, with 126 patients receiving additional Dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI). Radiomics features were extracted from T1WI, PDWI, CE-T1WI and DCE-MRI sequences. Feature selection was performed through Mutual Information Criterion (MIC), Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, and feature selection network. Machine learning models based on logistic regression, support vector machines, random forests, and extreme gradient boosting, as well as deep learning prediction models of multi-layer perceptrons were established. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves and interpreted through SHapley Additive exPlanations (SHAP) values.</p> Results <p>The deep learning model incorporating DCE-MRI features showed superior performance with an Area under the curve(AUC) of 0.885, sensitivity of 79.6%, and specificity of 83.7%. SHAP analysis revealed that DCE-MRI features, particularly from late enhancement phases, contributed most significantly to model predictions.</p> Conclusion <p>The multimodal MRI radiomics model, especially when combined with DCE-MRI, can effectively predict NAC response in NPC patients. The model’s interpretability through SHAP analysis demonstrates its potential for clinical application.</p>

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Interpretable multimodal MRI radiomics for predicting neoadjuvant chemotherapy response in nasopharyngeal carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • Tian-Cheng Lin,
  • Kai-Jie Wang,
  • Wen-Zhu Li,
  • Gang Wu,
  • Feng Chen,
  • Wei-Yuan Huang

摘要

Objective

To investigate the value of multimodal MRI radiomics model in predicting Neoadjuvant chemotherapy (NAC) response in Nasopharyngeal carcinoma (NPC) and assess the performance differences between machine learning and deep learning models.

Methods

370 NPC patients were retrospectively enrolled, with 126 patients receiving additional Dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI). Radiomics features were extracted from T1WI, PDWI, CE-T1WI and DCE-MRI sequences. Feature selection was performed through Mutual Information Criterion (MIC), Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, and feature selection network. Machine learning models based on logistic regression, support vector machines, random forests, and extreme gradient boosting, as well as deep learning prediction models of multi-layer perceptrons were established. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves and interpreted through SHapley Additive exPlanations (SHAP) values.

Results

The deep learning model incorporating DCE-MRI features showed superior performance with an Area under the curve(AUC) of 0.885, sensitivity of 79.6%, and specificity of 83.7%. SHAP analysis revealed that DCE-MRI features, particularly from late enhancement phases, contributed most significantly to model predictions.

Conclusion

The multimodal MRI radiomics model, especially when combined with DCE-MRI, can effectively predict NAC response in NPC patients. The model’s interpretability through SHAP analysis demonstrates its potential for clinical application.