{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T14:33:19Z","timestamp":1787841199012,"version":"build-2784847793"},"reference-count":54,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>\n                      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\u2013Kutcher\u2013Burman 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 (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 57).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Grade \u22652 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\u20130.93; XGBoost: AUC = 0.847, 95% CI: 0.78\u20130.91) and larynx (XGBoost: AUC = 0.853, 95% CI: 0.78\u20130.92) compared to traditional models (all AUC &lt; 0.60,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; 0.001). Calibration analysis revealed Brier scores of 0.135\u20130.145 for ML models versus 0.276\u20130.295 for traditional approaches, though calibration slopes (1.37\u20131.84) indicated systematic under-prediction requiring attention in clinical implementation. Age (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.002, Cohen\u2019s d = 0.908), total dose (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.035), and treatment duration (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = 0.026) were significantly associated with parotid toxicity. Traditional model parameters required substantial adjustment from literature values (parotid tolerance dose for 50% complication: 10.0 vs. 28.4 Gy).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>\n                      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 (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 57), and external validation in larger, multi-institutional cohorts is essential before clinical implementation.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jmp.jmp_280_25","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:00:32Z","timestamp":1774983632000},"page":"24-39","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Improves Prediction of Normal-Tissue Complications Compared with Radiobiological Models in Head-and-Neck Radiotherapy: An Exploratory Small-cohort Study"],"prefix":"10.4103","volume":"51","author":[{"given":"Kalyan","family":"Mondal","sequence":"first","affiliation":[{"name":"Department of Physics, Institute of Applied 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