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article · BMC Medical Imaging

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

2026Open accessUniversity of Abuja

Abstract

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. 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 2 ), multicollinearity, and bilateral asymmetry were assessed. 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 norm -, AUC lc -, and variance-based kinetics showed high longitudinal reliability (ICC/CCC > 0.86; R 2 0.78–0.83), revealing week-4 as a potentially informative treatment phase. 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.

Research topics

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Radiotherapy Techniques
  • Head and Neck Cancer Studies

Sustainable Development Goals

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DOI: 10.1186/s12880-026-02573-z

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