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Purpose <p>To investigate the value of cone-beam computed tomography (CBCT)-based delta radiomics for predicting short-term radiotherapy (RT) response in nasopharyngeal carcinoma (NPC).</p> Methods <p>A&#xa0;total of 132 pathologically confirmed NPC patients receiving RT were retrospectively enrolled. Serial CBCT images during weeks 1–4 were collected. Patients were grouped by therapeutic response and randomly divided into training and test sets (7:3). Radiomic features from fractional CBCTs were extracted via Pyradiomics. Temporal delta-radiomic features were derived from interfraction differences. After applying feature normalization and dimensionality reduction, optimal features were selected using analysis of variance (ANOVA), recursive feature elimination, relevant features, and Kruskal–Wallis tests. Ten classifiers, including logistic regression (LR), were trained with 5‑fold cross-validation strategy. Predictive performance was evaluated by receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the DeLong’s test.</p> Results <p>The LR model based on the CBCT<sub>1st–3rd</sub> temporal interval achieved the optimal predictive performance (balanced accuracy 0.73, area under the curve [AUC] 0.74, sensitivity 0.64, specificity 0.81) in the cross-validation set. DeLong’s tests revealed no statistically significant differences (<i>P</i> &gt; 0.05) in AUC values within the cross-validation set between the CBCT<sub>1st–3rd</sub> model and models based on CBCT<sub>1st–4th</sub> or CBCT<sub>2nd–4th</sub> intervals. DCA indicated that the LR model based on CBCT<sub>1st–3rd</sub> temporal interval provided the highest net clinical benefit within threshold probabilities ranging from 0.2&#xa0;to 0.4 and exceeding 0.65.</p> Conclusion <p>The CBCT-based delta radiomics models can dynamically assess short-term RT response in NPC patients. This approach offers potential as an early-warning indicator during the RT course and provides a&#xa0;novel approach to guiding personalized precision radiotherapy for NPC.</p>

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Application of delta radiomics based on cone-beam computed tomography in predicting radiotherapy efficacy for nasopharyngeal carcinoma

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Zhongfan Liao,
  • Hongke Yin,
  • Dashuang Luo,
  • Jiaxing Xu,
  • Xiaozhou Zeng,
  • Xiaoyan Tang,
  • Fasheng Huang,
  • Xuhui Zhang

摘要

Purpose

To investigate the value of cone-beam computed tomography (CBCT)-based delta radiomics for predicting short-term radiotherapy (RT) response in nasopharyngeal carcinoma (NPC).

Methods

A total of 132 pathologically confirmed NPC patients receiving RT were retrospectively enrolled. Serial CBCT images during weeks 1–4 were collected. Patients were grouped by therapeutic response and randomly divided into training and test sets (7:3). Radiomic features from fractional CBCTs were extracted via Pyradiomics. Temporal delta-radiomic features were derived from interfraction differences. After applying feature normalization and dimensionality reduction, optimal features were selected using analysis of variance (ANOVA), recursive feature elimination, relevant features, and Kruskal–Wallis tests. Ten classifiers, including logistic regression (LR), were trained with 5‑fold cross-validation strategy. Predictive performance was evaluated by receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the DeLong’s test.

Results

The LR model based on the CBCT1st–3rd temporal interval achieved the optimal predictive performance (balanced accuracy 0.73, area under the curve [AUC] 0.74, sensitivity 0.64, specificity 0.81) in the cross-validation set. DeLong’s tests revealed no statistically significant differences (P > 0.05) in AUC values within the cross-validation set between the CBCT1st–3rd model and models based on CBCT1st–4th or CBCT2nd–4th intervals. DCA indicated that the LR model based on CBCT1st–3rd temporal interval provided the highest net clinical benefit within threshold probabilities ranging from 0.2 to 0.4 and exceeding 0.65.

Conclusion

The CBCT-based delta radiomics models can dynamically assess short-term RT response in NPC patients. This approach offers potential as an early-warning indicator during the RT course and provides a novel approach to guiding personalized precision radiotherapy for NPC.