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

J Endocrinol Invest. 2024 Mar;47(3):633-643. doi: 10.1007/s40618-023-02182-5. Epub 2023 Sep 22.

Abstract

Purpose: Indeterminate 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.

Methods: Retrospective 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.

Results: Of 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.

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

Keywords: Algorithms; Cytology; Indeterminate cytology; Thyroid cancer.

MeSH terms

  • Algorithms*
  • Hospitals
  • Humans
  • Medical Oncology
  • Retrospective Studies
  • Thyroid Neoplasms*