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Background <p>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.</p> Methods <p>A retrospective cohort of 131 HNC patients (101 males, 30 females; aged 23–87) treated with VMAT-based chemoradiotherapy (60–70&#xa0;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 (<i>n</i> = 91; 69.5%) and testing (<i>n</i> = 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.</p> Results <p>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 X9X) (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 D<sub>mean</sub>RT_PG (mean: 0.447; p: 0.045), as significant predictors of RIXM.</p> Conclusions <p>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.</p>

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SHAP-based interpretable machine learning with longitudinal delta-radiomics across seven weeks of treatment for xerostomia prediction in head-and-neck cancer

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  • 发表日期：

    2026年4月23日

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

摘要

Background

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.

Methods

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.

Results

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 X9X) (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 DmeanRT_PG (mean: 0.447; p: 0.045), as significant predictors of RIXM.

Conclusions

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.