Evidence map›Paper›PMID 42848144›Full record

ArticleMedical oncology (Northwood, London, England)2026

Effectiveness of radiotherapy or chemoradiotherapy in older patients with advanced-stage, non-surgically treated head and neck cancer: A machine learning analysis.

Rasheed Omobolaji Alabi, Alhadi Almangush, Mohammed Elmusrati, Ylva Tiblom Ehrsson, Göran Laurell, Antti A Mäkitie

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Article in Medical oncology (Northwood, London, England), 2026. 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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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Rasheed Omobolaji AlabiResearch Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. rasheed.alabi@helsinki.fi.ORCID https://orcid.org/0000-0001-7655-5924
Alhadi AlmangushResearch Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Mohammed ElmusratiCyber-Physical Systems (CPS) Research Team, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
Ylva Tiblom EhrssonDepartment of Surgical Sciences, Section of Otorhinolaryngology and Head and Neck Surgery, Uppsala University, Uppsala, Sweden.
Göran LaurellDepartment of Surgical Sciences, Section of Otorhinolaryngology and Head and Neck Surgery, Uppsala University, Uppsala, Sweden.
Antti A MäkitieResearch Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rising incidence of head and neck squamous cell carcinoma (HNSCC) is linked to both the HPV epidemic and the aging population. 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 (≥ 65 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 (≥ 65 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.

Indexed as

ChemoradiotherapyHead and Neck NeoplasmsMachine LearningSquamous Cell Carcinoma of Head and NeckAgedAged, 80 and overFemaleHumansKaplan-Meier EstimateMaleNeoplasm StagingSEER ProgramTreatment OutcomeChemoradiotherapyHead and Neck Cancer Squamous Cell Carcinoma (HNSCC)Machine learningOverall survivalPropensity Score Matching

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.