Evidence map›Paper›PMID 39766113›Full record

ArticleCancers2024

Description of a New miRNA Signature for the Surgical Management of Thyroid Nodules.

Marie Quiriny, Joel Rodrigues Vitόria, Manuel Saiselet, Geneviève Dom, Nicolas De Saint Aubain, Esther Willemse, Antoine Digonnet, Didier Dequanter, Alexandra Rodriguez, Guy Andry and 2 more

Abstract read
In one paragraph

Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Marie QuirinyInstitut Jules Bordet, HUB, Université libre de Bruxelles, 1070 Brussels, Belgium.
Joel Rodrigues VitόriaIRIBHM Jacques E. Dumont, Université libre de Bruxelles, 1070 Brussels, Belgium.ORCID 0000-0002-0539-0015
Manuel SaiseletIRIBHM Jacques E. Dumont, Université libre de Bruxelles, 1070 Brussels, Belgium.
Geneviève DomIRIBHM Jacques E. Dumont, Université libre de Bruxelles, 1070 Brussels, Belgium.
Nicolas De Saint AubainInstitut Jules Bordet, HUB, Université libre de Bruxelles, 1070 Brussels, Belgium.
Esther WillemseInstitut Jules Bordet, HUB, Université libre de Bruxelles, 1070 Brussels, Belgium.ORCID 0009-0005-8584-365X
Antoine DigonnetInstitut Jules Bordet, HUB, Université libre de Bruxelles, 1070 Brussels, Belgium.
Didier DequanterDepartment of Otolaryngology-Head and Neck Surgery, CHU Saint-Pierre, Université Libre de Bruxelles, 1070 Brussels, Belgium.ORCID 0000-0001-7721-5315
Alexandra RodriguezDepartment of Otolaryngology-Head and Neck Surgery, CHU Saint-Pierre, Université Libre de Bruxelles, 1070 Brussels, Belgium.
Guy AndryInstitut Jules Bordet, HUB, Université libre de Bruxelles, 1070 Brussels, Belgium.
Vincent DetoursIRIBHM Jacques E. Dumont, Université libre de Bruxelles, 1070 Brussels, Belgium.
Carine MaenhautIRIBHM Jacques E. Dumont, Université libre de Bruxelles, 1070 Brussels, Belgium.

Funding

Fonds David et Alice Van Buuren, Fondation Jaumotte-Demoulin and Fondation Héger-Masson. 11587122King Baudouin Foundation 2018-J1812080-E003
6 · The paper itself

Abstract

backgroundThe diagnosis of malignant thyroid nodules is mainly based on the fine-needle aspiration biopsy (FNAB). To improve the detection of malignant nodules, different molecular tests have been developed. We present a new molecular signature based on altered miRNA expressions and specific mutations.

methodsThis is a prospective non-interventional study, including all Bethesda categories, carried out on an FNAB sampled in suspicious nodule(s) during thyroidectomy. miRNA quantification and mutations detection were performed. The reference diagnosis was the pathological assessment of the surgical specimen. Different classification algorithms were trained with molecular data to correctly classify the samples.

resultsA total of 294 samples were recorded and randomly divided in two equal groups. The random forest algorithm showed the highest accuracy and used mostly miRNAs to classify the nodules. The sensitivity and the specificity of our signature were, respectively, 76% and 96%, and the positive and negative predictive values were both 90% (disease prevalence of 30%).

conclusionsWe have identified a molecular classifier that combines miRNA expressions with mutations detection. This signature could potentially help clinicians, as complementary to the Bethesda classification, to discriminate indeterminate FNABs.

Indexed as

fine-needle aspiration biopsymicroRNAmolecular signaturethyroid carcinomathyroidectomy

Identifiers

PMID39766113
PMCPMC11674976

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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.