ArticleJournal of medical physics
Machine Learning Improves Prediction of Normal-Tissue Complications Compared with Radiobiological Models in Head-and-Neck Radiotherapy: An Exploratory Small-cohort Study.
Article in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Accurate prediction of radiation-induced toxicity remains a key challenge in head-and-neck radiotherapy. This retrospective study compared traditional normal-tissue complication probability (NTCP) models, Lyman-Kutcher-Burman and relative seriality, with machine learning (ML) approaches (artificial neural network [ANN] and extreme gradient boosting [XGBoost]) for organ-at-risk (OAR) toxicity prediction for a small cohort ( Materials and Methods: Fifty-seven patients treated with intensity-modulated radiotherapy, volumetric modulated arc therapy, or hybrid techniques were analyzed across 115 OARs (parotid glands [54], larynx [31], and spinal cord [30]). Post-treatment toxicities were graded using Common Terminology Criteria for Adverse Events v5.0 with a median follow-up of 10 months. Models and ML were implemented using stratified 5-fold cross-validation and assessed using discrimination (area under the receiver operating characteristic curve [AUC]), Brier score, calibration analysis, and SHapley Additive exPlanations values. Results: Grade ≥2 toxicity occurred in 63.0% (34/54) of parotid glands and 45.2% (14/31) of larynges, with no spinal cord events. ML models achieved superior discrimination for parotid glands (ANN: AUC = 0.866, 95% confidence interval [CI]: 0.81-0.93; XGBoost: AUC = 0.847, 95% CI: 0.78-0.91) and larynx (XGBoost: AUC = 0.853, 95% CI: 0.78-0.92) compared to traditional models (all AUC < 0.60, Conclusion: ML captured nonlinear interactions between dosimetric and clinical variables more effectively than traditional NTCP models, yielding superior predictive accuracy. However, findings are exploratory given the same-dataset validation with a modest cohort size (
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