Scholarlink

Background and purpose <p>Radiation-induced xerostomia remains a significant concern in head-and-neck cancer radiotherapy, inadequately predicted, with unidentified kinetic signatures of parotid injury. This study aimed to identify predictive metrics and reproducible bilateral parotid delta-radiomic kinetics and variance features for late xerostomia.</p> Materials and methods <p>This retrospective study included 131 HNC patients treated with VMAT-based radiochemotherapy. Weekly CBCT images (weeks 1–7) were collected (2020–2023). Twelve-month xerostomia was graded using CTCAE v5.0. IBSI-compliant bilateral parotid radiomics were extracted and converted to delta-radiomic kinetics. Patient-level data were split (training n: 91; testing n: 40), and feature selection combined univariate filtering with recursive feature elimination. Machine-learning models with stratified 5-fold cross-validation integrated clinical-dosimetric (CD), clinical-dosimetric + delta-radiomic (CDR), and clinical-dosimetric + delta-radiomic + kinetics (CDRK). Performance was evaluated using AUC (95% CI) and other metrics, with SHAP interpretability. Reliability (ICC, CCC, R<sup>2</sup>), multicollinearity, and bilateral asymmetry were assessed.</p> Results <p>The prevalence of 12-month grade ≥ 2 xerostomia was nearly identical between cohorts (76.9%, 77.5%; SMD: 0.014), with no significant difference observed. Predictive performance enhanced with feature incorporation and peaked mid-treatment. Clinical-dosimetric models demonstrated limited early discrimination (AUC 0.38–0.45). Delta-radiomics improved performance at week-3, while combined kinetic models achieved optimal predictive performance at week-4 (SVM-CDRK AUC 0.81, p: 0.002, sensitivity 100%, specificity 86%, Brier score 0.06, F1-score 91%). LASSO- and MLP-CDRK yielded AUCs of 0.75 (p: 0.009) and 0.73 (p: 0.011) and F1-score (86%; 79%). Texture-based GLCM features (AUC 0.79, p: 0.003) outperformed first-order and morphological descriptors. S<sub>norm</sub>-, AUC<sub>lc</sub>-, and variance-based kinetics showed high longitudinal reliability (ICC/CCC &gt; 0.86; R<sup>2</sup> 0.78–0.83), revealing week-4 as a potentially informative treatment phase.</p> Conclusions <p>Longitudinal bilateral parotid delta-radiomic kinetics significantly enhance late xerostomia prediction, identify mid-treatment as the optimal risk-stratification window, and support adaptive radiotherapy and personalized toxicity mitigation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Longitudinal bilateral parotid delta-radiomic kinetics and variance signatures for prediction of late xerostomia following radiotherapy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月9日

查看全文
  • Damilola Oluwafemi Samson,
  • Mahayu Ismail,
  • Mohd Ariff Mohamed Hanifa,
  • Eznal Izwadi Mohd Mahidin,
  • Hanani Abdul Manan,
  • Noorazrul Yahya

摘要

Background and purpose

Radiation-induced xerostomia remains a significant concern in head-and-neck cancer radiotherapy, inadequately predicted, with unidentified kinetic signatures of parotid injury. This study aimed to identify predictive metrics and reproducible bilateral parotid delta-radiomic kinetics and variance features for late xerostomia.

Materials and methods

This retrospective study included 131 HNC patients treated with VMAT-based radiochemotherapy. Weekly CBCT images (weeks 1–7) were collected (2020–2023). Twelve-month xerostomia was graded using CTCAE v5.0. IBSI-compliant bilateral parotid radiomics were extracted and converted to delta-radiomic kinetics. Patient-level data were split (training n: 91; testing n: 40), and feature selection combined univariate filtering with recursive feature elimination. Machine-learning models with stratified 5-fold cross-validation integrated clinical-dosimetric (CD), clinical-dosimetric + delta-radiomic (CDR), and clinical-dosimetric + delta-radiomic + kinetics (CDRK). Performance was evaluated using AUC (95% CI) and other metrics, with SHAP interpretability. Reliability (ICC, CCC, R2), multicollinearity, and bilateral asymmetry were assessed.

Results

The prevalence of 12-month grade ≥ 2 xerostomia was nearly identical between cohorts (76.9%, 77.5%; SMD: 0.014), with no significant difference observed. Predictive performance enhanced with feature incorporation and peaked mid-treatment. Clinical-dosimetric models demonstrated limited early discrimination (AUC 0.38–0.45). Delta-radiomics improved performance at week-3, while combined kinetic models achieved optimal predictive performance at week-4 (SVM-CDRK AUC 0.81, p: 0.002, sensitivity 100%, specificity 86%, Brier score 0.06, F1-score 91%). LASSO- and MLP-CDRK yielded AUCs of 0.75 (p: 0.009) and 0.73 (p: 0.011) and F1-score (86%; 79%). Texture-based GLCM features (AUC 0.79, p: 0.003) outperformed first-order and morphological descriptors. Snorm-, AUClc-, and variance-based kinetics showed high longitudinal reliability (ICC/CCC > 0.86; R2 0.78–0.83), revealing week-4 as a potentially informative treatment phase.

Conclusions

Longitudinal bilateral parotid delta-radiomic kinetics significantly enhance late xerostomia prediction, identify mid-treatment as the optimal risk-stratification window, and support adaptive radiotherapy and personalized toxicity mitigation.