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

Area of Science:

• Endocrinology
• Oncology
• Diagnostic Cytopathology

Background:

• Indeterminate thyroid cytology (TIR3B) presents a diagnostic challenge with up to 30% risk of thyroid cancer.
• Current guidelines often recommend surgery for TIR3B, despite a high rate of benign histology post-operatively.
• Improved methods are needed to refine oncological risk stratification for these cases.

Purpose of the Study:

• To develop and validate a predictive algorithm for improved surgical indication in TIR3B thyroid cytology.
• To identify key clinical, ultrasound, and cytological features that predict malignancy in TIR3B nodules.

Main Methods:

• Retrospective analysis of TIR3B nodules from a single endocrine unit (May 2014 - Dec 2021) for algorithm development (Phase 1).
• Validation of the developed algorithm on a separate cohort of TIR3B nodules undergoing surgery (Jan 2022 - June 2022) (Phase 2).
• Logistic regression analysis was used to build the predictive model, with histology as the outcome.

Main Results:

• In Phase 1, a score >14.5 demonstrated an odds ratio of 4.98 for malignancy, with a positive predictive value (PPV) of 57% and negative predictive value (NPV) of 79%.
• Phase 2 validation confirmed the algorithm's utility, with a threshold of 14.5 points yielding a PPV of 53% and NPV of 80%.
• The algorithm effectively integrates clinical, ultrasound, and cytological data for risk stratification.

Conclusions:

• A predictive algorithm incorporating clinical, ultrasound, and cytological features significantly enhances the oncological stratification of TIR3B cytology.
• This tool can aid clinicians in making more informed decisions regarding surgical intervention for indeterminate thyroid nodules.
• Further validation may refine surgical decision-making processes for TIR3B cases.