Associations between preoperative thyroid parameters, aggressive clinicopathological features and risk of recurrence...

The study aimed (1) to investigate the association between aggressive clinicopathological characteristics and the American Thyroid Association (ATA) recurrence risk classification in differentiated thyroid cancer (DTC) patients, and (2) to investigate the prognostic value of preoperative thyroid parameters.

A total of 3833 patients histologically confirmed DTC were recruited. Preoperative clinical and postoperative pathologic data were retrospectively collected. Participants were stratified into low recurrence risk and intermediate-to-high recurrence risk groups based on the ATA risk stratification system.

The study cohort had a mean age of 48.87 ± 8.08 years, and 1,465 (76.82%) were female. Male (OR = 1.37, p = .024), aged 52 years and older (OR = 2.01, p < .001), larger tumor size (OR = 3.71, p = 0.011), nerve invasion (OR = 6.69, p = .004), margin involvement (OR = 5.46, p < .001), multifocality (OR = 3.71, p < .001), and bilaterality (OR = 3.95, p < .001) were identified as risk factors for a higher ATA recurrence risk classification, in addition to established factors such as lymph node metastasis and angioinvasion, after adjusting for potential confounding variables. Higher preoperative levels of free triiodothyronine (FT3), FT3 to free thyroxine (FT3/FT4), and lower thyroid feedback quantile-based index (TFQI) levels were associated with a higher ATA recurrence risk classification. The comprehensive predictive model incorporating these variables demonstrated excellent discrimination (AUC = 0.836). Furthermore, higher FT3/FT4 levels and lower TFQI levels were associated with higher risk of lymph node metastases and angioinvasion.

Factors such as male sex, older age, multifocality, bilaterality, margin involvement, nerve invasion, larger tumor size, and preoperative thyroid parameters serve as complementary predictors for higher ATA recurrence risk in DTC, in addition to conventional risk factors. These insights contribute to a more nuanced understanding and optimization of current risk stratification methodologies.