Radiation-induced xerostomia (RIXM) remains a common toxicity in head-and-neck cancer (HNC) patients receiving radiotherapy, with currently used static dosimetric models exhibiting limited predictive accuracy. This study evaluates delta-radiomics from longitudinal VMAT-CBCT imaging, combined with explainable machine learning classifiers (MLCs), to capture treatment-related changes in the parotid gland across seven weeks and improve RIXM prediction. A retrospective cohort of 131 HNC patients (101 males, 30 females; aged 23–87) treated with VMAT-based chemoradiotherapy (60–70 Gy over 6–7 weeks) from 2020 to 2023 was analyzed. RIXM was graded using CTCAE v5. 0 at 3, 6, and 12-months post-treatment, representing early, intermediate, and late endpoints. Radiomic features were extracted from weekly CBCT-derived parotid segmentations (6–7 per patient) following IBSI guidelines, and delta-radiomics was computed across multiple preprocessing pipelines. The dataset was split into training/validation (n = 91; 69. 5%) and testing (n = 40; 30. 5%) sets using stratified patient-level splitting. A hybrid feature selection method combining univariate filtering and recursive feature elimination with cross-validation was applied, followed by SMOTE. Seven MLCs and a dosimetric NTCP model were developed using Python (v7. 0. 8, scikit-learn). Model interpretability was assessed with SHAP; feature significance and predictive performance were evaluated using paired t-tests, PPV, and NPV. Early, intermediate, and late RIXM occurred in 90. 8% (3-months), 91. 6% (6-months), and 77. 1% (12-months) of patients, respectively. The strongest predictions were found for week-4 and 6-month endpoint. SVM achieved the highest predictive performance (AUC: 0. 79 ± 0. 01, 95% CI: 0. 65–0. 83; PR-AUC: 0. 98; sensitivity: 0. 92, CI: 0. 90–0. 92; specificity: 0. 69, CI: 0. 67–0. 70; F1-score: 0. 90 ± 0. 01; BAcc: 0. 81; MCC: 0. 51; Brier score: 0. 11 ± 0. 01), with balanced PPV (0. 81 ± 0. 04) and NPV (0. 64 ± 0. 02). Similar results were observed with the NTCP model (AUC: 0. 82 ± 0. 01, 95% CI: 0. 67–0. 89; PR-AUC: 0. 97; sensitivity: 1. 00, CI: 0. 96–1. 00; specificity: 0. 72, CI: 0. 72–0. 74; F1-score: 0. 92 ± 0. 01) and calibration (BAcc: 0. 86; MCC: 0. 48; Brier score: 0. 08 ± 0. 01; PPV: 0. 83 ± 0. 01; NPV: 0. 67 ± 0. 02). SHAP analysis identified comorbidities (mean: 1. 204; p: 0. 007), delta-radiomic features (strength- (IBSI: 1 × 9X) (mean: 1. 179; p: 0. 041), histogram gradient- (IBSI: RHQZ) (mean: 1. 021; p: 0. 043), intensity kurtosis- (IBSI: C317) (mean: 0. 592; p: 0. 045) ), and DmeanRTPG (mean: 0. 447; p: 0. 045), as significant predictors of RIXM. Delta-radiomics combined with explainable classifiers accurately predicted RIXM at 3, 6, and 12-months post-treatment. Week-4 and 6-month features were most predictive, supporting early, personalized, dose-informed radiotherapy interventions.
Samson et al. (Thu,) studied this question.