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As a result, there is an emerging need to develop management strategies for older patients with advanced-stage HNSCC. This study aims to apply machine learning (ML) techniques to compare the effectiveness of definitive concurrent chemoradiotherapy (CRT) and radiotherapy (RT) alone in older patients (\u2265\u200965 years) with stage III-IV non-surgically treated HNSCC. We used Kaplan-Meier (KM) survival curves, Cox proportional hazards models, and ML-based permutation feature importance (PFI) to compare the effectiveness of CRT versus single modality radiotherapy (RT) for disease-specific survival (DSS) in this population of patients with HNSCC. Finally, we used propensity-score matched (PSM) analysis to clarify if there is a significant difference between patients that received either CRT or RT alone. A total of 1525 patients with HNSCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program to build the ML model. Furthermore, the PFI analysis evaluated the effectiveness of CRT or RT alone regarding DSS of older (\u2265\u200965 year) patients with HNSCC. Then, we performed a 1:1 propensity-score matched (PSM) analysis to reduce treatment selection bias and to analyze the prognostic role of CRT. Our ML model showed performance accuracy of 55.5% in predicting DSS in this group with HNSCC. The aggregate feature importance showed that treatment option (CRT or RT alone) is among the four most important features for enhancing DSS among these patients. Specifically, our KM curves, Cox proportional hazards model, and PFI analyses all showed that CRT seems more feasible and effective than RT alone for older patients with stage III-IV HNSCC who did not receive surgery. In addition, patients with stage III disease had better survival rates than those with stage IV. The PSM confirmed that there are substantial differences in nearly all covariates between patients who received CRT or RT alone. The results from the SEER database provide insight into a potential survival benefit for CRT in older patients with stage III-IV HNSCC who are not candidates for surgery. This further emphasizes that treatment decisions at an advanced stage may be made according to other patient-related factors rather than chronological (calendar) age alone.<\/jats:p>","DOI":"10.1007\/s12032-026-03420-5","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T09:56:01Z","timestamp":1791453361000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Effectiveness of radiotherapy or chemoradiotherapy in older patients with advanced-stage, non-surgically treated head and neck cancer: A machine learning analysis"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7655-5924","authenticated-orcid":false,"given":"Rasheed Omobolaji","family":"Alabi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alhadi","family":"Almangush","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Elmusrati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ylva","family":"Tiblom Ehrsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00f6ran","family":"Laurell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antti A.","family":"M\u00e4kitie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"3420_CR1","doi-asserted-by":"publisher","first-page":"5931","DOI":"10.1007\/s00405-024-08805-8","volume":"281","author":"R Zhu","year":"2024","unstructured":"Zhu R, Wang P, Dai J, Cheng Y, Zhu H. 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