Evidence map›Paper›PMID 40131688›Full record

ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2025

Artificial intelligence in dysphagia assessment: evaluating lingual muscle composition in head and neck cancer.

Laura Ferrera Alayón, Barbara Salas-Salas, Fiorella Ximena Palmas-Candia, Raquel Diaz-Saavedra, Anais Ramos-Ortiz, Pedro C Lara, Marta Lloret Sáez-Bravo

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In one paragraph

Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Laura Ferrera AlayónDepartment of Radiation Oncology, University Hospital Dr Negrín, Las Palmas de Gran Canaria, Spain.
Barbara Salas-SalasDepartment of Radiation Oncology, University Hospital Dr Negrín, Las Palmas de Gran Canaria, Spain.
Fiorella Ximena Palmas-CandiaEndocrinology and Nutrition Department, Vall d'Hebron University Hospital, Barcelona, Spain.
Raquel Diaz-SaavedraDepartment of Radiation Oncology, University Hospital Dr Negrín, Las Palmas de Gran Canaria, Spain.
Anais Ramos-OrtizDepartment of Radiation Oncology, University Hospital Dr Negrín, Las Palmas de Gran Canaria, Spain.
Pedro C LaraFernando Pessoa Canarias University, Las Palmas de Gran Canaria, Spain. plara@dcc.ulpgc.es.ORCID http://orcid.org/0000-0003-1709-6232
Marta Lloret Sáez-BravoDepartment of Radiation Oncology, University Hospital Dr Negrín, Las Palmas de Gran Canaria, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeOropharyngeal dysphagia is a common and debilitating condition in head and neck cancer (HNC) patients. This study aimed to evaluate the relationship between tongue muscle composition (quantity and quality) and the risk of dysphagia in non-surgically treated HNC patients, using artificial intelligence (AI) analysis of pretreatment computed tomography (CT) scans.

methodsA prospective analysis was conducted on 41 non-surgically treated HNC patients under-going curative radiotherapy. Tongue muscle quantity was measured as cross-sectional area (cm

resultsA significant association was found between EAT-10 categorical scores and dysphagia risk (Chi

conclusionsThe study demonstrates that non-surgically treated HNC patients with increased tongue area percentages and higher muscle density are at greater risk of dysphagia. Additionally, pre-treatment dysphagia was found to be a strong predictor of post-treatment dysphagia. The use of AI-based CT analysis provides a precise method for identifying patients at risk, allowing for timely interventions to improve swallowing function and quality of life.

Indexed as

Artificial IntelligenceDeglutition DisordersHead and Neck NeoplasmsMuscle, SkeletalTongueAdultAgedBody CompositionFemaleHumansMaleMiddle AgedProspective StudiesTomography, X-Ray ComputedArtificial intelligenceDeglutition disordersHead and neck neoplasmsMuscleRadiotherapySkeletalTomographyTongue

Identifiers

PMID40131688

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.