Evidence map›Paper›PMID 39200141›Full record

ReviewBiomedicines2024

Thyroid Nodule Characterization: Overview and State of the Art of Diagnosis with Recent Developments, from Imaging to Molecular Diagnosis and Artificial Intelligence.

Emanuele David, Hektor Grazhdani, Giuliana Tattaresu, Alessandra Pittari, Pietro Valerio Foti, Stefano Palmucci, Corrado Spatola, Maria Chiara Lo Greco, Corrado Inì, Francesco Tiralongo and 4 more

Abstract readReview
In one paragraph

Review in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 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

22 citing papers in PubMed, 3 syntheses or guidelines 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

14 authors.

Emanuele DavidDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.
Hektor GrazhdaniKlinika Dani, 1010 Tirane, Albania.
Giuliana TattaresuDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.
Alessandra PittariDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.
Pietro Valerio FotiDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.ORCID 0000-0002-0918-0103
Stefano PalmucciDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.ORCID 0000-0002-7196-2974
Corrado SpatolaDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.ORCID 0000-0001-5562-189X
Maria Chiara Lo GrecoDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.ORCID 0000-0003-4294-7350
Corrado InìDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.ORCID 0000-0002-7529-2680
Francesco TiralongoDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.ORCID 0000-0001-9637-1970
Davide CastiglioneDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.ORCID 0000-0002-1542-1073
Giampiero MastroeniUnit of Radiology, Papardo Hospital, 98158 Messina, Italy.
Silvia GigliDepartment of Diagnostic Imaging, Sandro Pertini Hospital, 00157 Rome, Italy.
Antonio BasileDepartment of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultrasound (US) is the primary tool for evaluating patients with thyroid nodules, and the risk of malignancy assessed is based on US features. These features help determine which patients require fine-needle aspiration (FNA) biopsy. Classification systems for US features have been developed to facilitate efficient interpretation, reporting, and communication of thyroid US findings. These systems have been validated by numerous studies and are reviewed in this article. Additionally, this overview provides a comprehensive description of the clinical and laboratory evaluation of patients with thyroid nodules, various imaging modalities, grayscale US features, color Doppler US, contrast-enhanced US (CEUS), US elastography, FNA biopsy assessment, and the recent introduction of molecular testing. The potential of artificial intelligence in thyroid US is also discussed.

Indexed as

artificial intelligencefine-needle aspiration biopsythyroid cancerthyroid imaging reporting and data systemthyroid nodulesultrasound

Identifiers

PMID39200141
PMCPMC11351886

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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.