Evidence map›Paper›PMID 42333867›Full record

SynthesisHead & neck2026

Artificial Intelligence-Driven MRI for Cervical Nodal Metastasis Detection in Oral Squamous Cell Carcinoma: A Hierarchical Meta-Analysis of Diagnostic Accuracy.

José Evando da Silva-Filho, Renata Roque Ribeiro, André Wescley Oliveira de Aguiar, Caio Marques Silva, Daniela Pita de Melo, Karuza Maria Alves Pereira, Danielle Frota de Albuquerque, Fábio Wildson Gurgel Costa, Eduardo Diogo Gurgel-Filho

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Head & neck, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

José Evando da Silva-FilhoFaculty of Dentistry, University of Fortaleza, Fortaleza, Ceará, Brazil.ORCID https://orcid.org/0000-0002-4146-9134
Renata Roque RibeiroDivision of Dentistry, Faculty of Pharmacy, Dentistry and Nursing, Federal University of Ceará, Fortaleza, Ceará, Brazil.ORCID https://orcid.org/0000-0002-1685-8955
André Wescley Oliveira de AguiarCenter for Technological Sciences, University of Fortaleza, Fortaleza, Ceará, Brazil.ORCID https://orcid.org/0000-0001-5437-0945
Caio Marques SilvaCenter for Technological Sciences, University of Fortaleza, Fortaleza, Ceará, Brazil.ORCID https://orcid.org/0009-0005-1740-2254
Daniela Pita de MeloCollege of Dentistry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.ORCID https://orcid.org/0000-0002-6477-6997
Karuza Maria Alves PereiraDivision of Dentistry, Faculty of Pharmacy, Dentistry and Nursing, Federal University of Ceará, Fortaleza, Ceará, Brazil.ORCID https://orcid.org/0000-0002-2880-6466
Danielle Frota de AlbuquerqueFaculty of Dentistry, University of Fortaleza, Fortaleza, Ceará, Brazil.ORCID https://orcid.org/0000-0002-5691-3824
Fábio Wildson Gurgel CostaDivision of Dentistry, Faculty of Pharmacy, Dentistry and Nursing, Federal University of Ceará, Fortaleza, Ceará, Brazil.ORCID https://orcid.org/0000-0002-3262-3347
Eduardo Diogo Gurgel-FilhoFaculty of Dentistry, University of Fortaleza, Fortaleza, Ceará, Brazil.ORCID https://orcid.org/0000-0002-5772-446X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) applied to magnetic resonance imaging (MRI) may improve detection of cervical lymph node metastases in oral squamous cell carcinoma (OSCC) but is heterogeneous.

methodsA systematic review identified observational studies (from 2000) evaluating AI-based MRI in adults with histopathologically confirmed OSCC. Risk of bias was assessed with QUADAS-AI. Diagnostic performance was synthesized using a hierarchical bivariate model. Publication bias and certainty of evidence were assessed using Deeks' test and GRADE.

resultsTwelve studies were included; seven datasets (548 participants) were meta-analyzed. Pooled sensitivity was 0.72 (95% CI: 0.62-0.80) and specificity 0.79 (95% CI: 0.73-0.83), with AUC 0.82 and diagnostic odds ratio 9.42. Heterogeneity is mainly related to threshold effects. No significant publication bias was detected (p = 0.536). Evidence certainty was low.

conclusionsAI-assisted MRI shows moderate diagnostic performance. Multicenter validation is required before clinical implementation. CLINICAL RELEVANCE: AI-supported MRI may serve as an adjunctive tool to improve preoperative risk stratification of cervical lymph node metastasis in OSCC.

Indexed as

Artificial IntelligenceCarcinoma, Squamous CellLymphatic MetastasisMagnetic Resonance ImagingMouth NeoplasmsHumansLymph NodesNeckSensitivity and Specificityartificial intelligencediagnostic accuracylymph node metastasismagnetic resonance imagingoral squamous cell carcinoma

Identifiers

PMID42333867
PMCPMC13432375

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.