Evidence map›Paper›PMID 37736856›Full record

ArticleJournal of endocrinological investigation2024

Improving the TIR3B oncological stratification: try to bridge the gap through a comprehensive presurgical algorithm.

C Sparano, M Puccioni, V Adornato, E Zago, B Fibbi, B Badii, L Bencini, G Mannelli, V Vezzosi, M Maggi and 1 more

Abstract read
In one paragraph

Article in Journal of endocrinological investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

C SparanoEndocrinology Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy.
M PuccioniEndocrinology Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy.
V AdornatoEndocrinology Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy.
E ZagoEndocrinology Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy.
B FibbiEndocrinology Unit, Medical-Geriatric Department, Careggi University Hospital, Viale Pieraccini 18, 50139, Florence, Italy.
B BadiiUnit of Endocrine Surgery, Careggi University Hospital, Florence, Italy.
L BenciniDivision of General Surgery, Department of Oncology and Robotic Surgery, Careggi University Hospital, Florence, Italy.
G MannelliHead and Neck Oncology and Robotic Surgery, Department of Experimental and Clinical Medicine, University of Florence, 50134, Florence, Italy.
V VezzosiDepartment of Histopathology and Molecular Diagnostics, Careggi University Hospital, Florence, Italy.
M MaggiEndocrinology Unit, Department of Experimental and Clinical Biomedical Sciences 'Mario Serio', University of Florence, Florence, Italy.
L PetroneEndocrinology Unit, Medical-Geriatric Department, Careggi University Hospital, Viale Pieraccini 18, 50139, Florence, Italy. luisa.petrone@unifi.it.ORCID http://orcid.org/0000-0002-2167-8844

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIndeterminate cytology still puzzles clinicians, due to its wide range of oncological risks. According to the Italian SIAPEC-IAP classification, TIR3B cytology holds up to 30% of thyroid cancer, which justifies the surgical indication, even if more than half of cases do not result in a positive histology. The study aim is to identify potential clinical, ultrasound or cytological features able to improve the surgical indication.

methodsRetrospective analysis. A consecutive series of TIR3B nodules referred to the Endocrine Unit of Careggi Hospital from 1st May 2014 to 31st December 2021 was considered for the exploratory analysis (Phase 1). Thereafter, a smaller confirmatory sample of consecutive TIR3B diagnosed and referred to surgery from 1st January 2022 to 31st June 2022 was considered to verify the algorithm (Phase 2). The main clinical, ultrasound and cytological features have been collected. A comprehensive stepwise logistic regression was applied to build a prediction algorithm. The histological results represented the final outcome.

resultsOf 599 TIR3B nodules referred to surgery, 451 cases were included in the exploratory analysis. A final score > 14.5 corresponded to an OR = 4.98 (95% CI 3.24-7.65, p < 0.0001) and showed a PPV and NPV of 57% and 79%, respectively. The Phase 2 analysis on a confirmatory sample of 58 TIR3B cytology confirmed that a threshold of 14.5 points has a comparable PPV and NPV of 53% and 80%, respectively.

conclusionsA predictive algorithm which considers the main clinical, US and cytological features can significantly improve the oncological stratification of TIR3B cytology.

Indexed as

AlgorithmsThyroid NeoplasmsHospitalsHumansMedical OncologyRetrospective StudiesAlgorithmsCytologyIndeterminate cytologyThyroid cancer

Identifiers

PMID37736856
PMCPMC10904402

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