Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies.


Journal

International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791

Informations de publication

Date de publication:
09 Apr 2022
Historique:
received: 11 03 2022
revised: 31 03 2022
accepted: 05 04 2022
entrez: 23 4 2022
pubmed: 24 4 2022
medline: 27 4 2022
Statut: epublish

Résumé

Fine-needle aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After cytomorphology, 20-30% of cases are deemed "indeterminate for malignancy" and undergo surgery. However, after thyroidectomy, 70-80% of these nodules are benign. The identification of tools for improving FNA's diagnostic performances is explored by matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI). A clinical study was conducted in order to build a classification model for the characterization of thyroid nodules on a large cohort of 240 samples, showing that MALDI-MSI can be effective in separating areas with benign/malignant cells. The model had optimal performances in the internal validation set (

Identifiants

pubmed: 35456973
pii: ijms23084156
doi: 10.3390/ijms23084156
pmc: PMC9028391
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Italian Association for Cancer Research
ID : MFAG grant 2016- Id. 18445
Organisme : Regione Lombardia
ID : POR FESR 2014-2020
Organisme : Regione Lombardia
ID : Programma degli interventi per la ripresa economica: sviluppo di nuovi accordi di collaborazione con le università per la ricerca, l'innovazione e il trasferimento tecnologico: NephropaThy
Organisme : Ricerca Finalizzata
ID : GR-2019-12368592
Organisme : Regione Lombardia
ID : Call HUB Ricerca ed Innovazione: ImmunHUB
Organisme : Fondazione Gigi & Pupa Ferrari
ID : Fondazione Gigi & Pupa Ferrari 2021

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Auteurs

Giulia Capitoli (G)

Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.

Isabella Piga (I)

Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.

Vincenzo L'Imperio (V)

Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, 20900 Monza, Italy.

Francesca Clerici (F)

Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.

Davide Leni (D)

Department of Radiology, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy.

Mattia Garancini (M)

Department of Surgery, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy.

Gabriele Casati (G)

Department of Clinical Pathology, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy.

Stefania Galimberti (S)

Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.

Fulvio Magni (F)

Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.

Fabio Pagni (F)

Department of Medicine and Surgery, Pathology, University of Milano-Bicocca, 20900 Monza, Italy.

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