Evidence map›Paper›PMID 40390252›Full record

ArticleHead & neck2025

The Role of Machine Learning to Detect Occult Neck Lymph Node Metastases in Early-Stage (T1-T2/N0) Oral Cavity Carcinomas.

Stefania Troise, Lorenzo Ugga, Maria Esposito, Maria Positano, Andrea Elefante, Serena Capasso, Renato Cuocolo, Raffaele Merola, Umberto Committeri, Vincenzo Abbate and 3 more

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In one paragraph

Article in Head & neck, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

13 authors.

Stefania TroiseMaxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.ORCID 0000-0001-8421-0328
Lorenzo UggaDepartment of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
Maria EspositoMaxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.
Maria PositanoMaxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.
Andrea ElefanteDepartment of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
Serena CapassoDepartment of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
Renato CuocoloDepartment of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
Raffaele MerolaAnesthesia and Intensive Care Medicine, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.ORCID 0000-0002-0159-1426
Umberto CommitteriMaxillofacial Surgery Unit, University Hospital of Terni, Terni, Italy.
Vincenzo AbbateMaxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.ORCID 0000-0002-7905-0531
Paola BonavolontàMaxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.
Riccardo NociniOtolaryngology-Head and Neck Surgery Department, University and Hospital Trust of Verona, Verona, Italy.
Giovanni Dell'Aversana OrabonaMaxillofacial Surgery Unit, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveOral cavity carcinomas (OCCs) represent roughly 50% of all head and neck cancers. The risk of occult neck metastases for early-stage OCCs ranges from 15% to 35%, hence the need to develop tools that can support the diagnosis of detecting these neck metastases. Machine learning and radiomic features are emerging as effective tools in this field. Thus, the aim of this study is to demonstrate the effectiveness of radiomic features to predict the risk of occult neck metastases in early-stage (T1-T2/N0) OCCs. STUDY

designRetrospective study.

settingA single-institution analysis (Maxillo-facial Surgery Unit, University of Naples Federico II).

methodsA retrospective analysis was conducted on 75 patients surgically treated for early-stage OCC. For all patients, data regarding TNM, in particular pN status after the histopathological examination, have been obtained and the analysis of radiomic features from MRI has been extrapolated.

results56 patients confirmed N0 status after surgery, while 19 resulted in pN+. The radiomic features, extracted by a machine-learning algorithm, exhibited the ability to preoperatively discriminate occult neck metastases with a sensitivity of 78%, specificity of 83%, an AUC of 86%, accuracy of 80%, and a positive predictive value (PPV) of 63%.

conclusionsOur results seem to confirm that radiomic features, extracted by machine learning methods, are effective tools in detecting occult neck metastases in early-stage OCCs. The clinical relevance of this study is that radiomics could be used routinely as a preoperative tool to support diagnosis and to help surgeons in the surgical decision-making process, particularly regarding surgical indications for neck lymph node treatment.

Indexed as

Lymphatic MetastasisLymph NodesMachine LearningMouth NeoplasmsAdultAgedAged, 80 and overFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeckNeoplasm StagingRetrospective StudiesSensitivity and Specificityearly‐stage tumormachine learningoccult neck metastasisOral cavity carcinomaradiomics features

Identifiers

PMID40390252
PMCPMC12434568

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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.