Evidence map›Paper›PMID 35456973›Full record

ArticleInternational journal of molecular sciences2022

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

Giulia Capitoli, Isabella Piga, Vincenzo L'Imperio, Francesca Clerici, Davide Leni, Mattia Garancini, Gabriele Casati, Stefania Galimberti, Fulvio Magni, Fabio Pagni

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
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

10 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Malignancy prediction for calcified thyroid nodules using deep learning based on ultrasound dynamic videos.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
    Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. The application of high-throughput proteomics in cytopathology.Journal of pathology and translational medicine · 2022
    Review
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

10 authors at 2 institutions in 1 country.

Giulia CapitoliBicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0002-8178-2440
Isabella PigaProteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0001-8221-2493
Vincenzo L'ImperioDepartment of Medicine and Surgery, Pathology, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0002-9284-2998
Francesca ClericiProteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0002-3494-251X
Davide LeniDepartment of Radiology, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy.
Mattia GaranciniDepartment of Surgery, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy.
Gabriele CasatiDepartment of Clinical Pathology, San Gerardo Hospital, ASST Monza, 20900 Monza, Italy.
Stefania GalimbertiBicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0003-1504-7242
Fulvio MagniProteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0002-8663-0374
Fabio PagniDepartment of Medicine and Surgery, Pathology, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0001-9625-1637
University of Milano-Bicocca · ITAzienda Ospedaliera San Gerardo · IT

Funding

Fondazione Gigi & Pupa Ferrari Fondazione Gigi & Pupa Ferrari 2021Italian Association for Cancer Research MFAG grant 2016- Id. 18445Regione Lombardia Call HUB Ricerca ed Innovazione: ImmunHUBRegione Lombardia POR FESR 2014-2020Regione Lombardia 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: NephropaThyRicerca Finalizzata GR-2019-12368592
6 · The paper itself

Abstract

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 (

Indexed as

Thyroid NeoplasmsThyroid NoduleBiopsy, Fine-NeedleHumansSensitivity and SpecificitySpectrometry, Mass, Matrix-Assisted Laser Desorption-Ionizationdiagnostic classificationfine-needle aspiration biopsiesMALDI-MSIproteomic analysisthyroid carcinoma

Identifiers

PMID35456973
PMCPMC9028391
OpenAlexW4224285284

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.