Improving the Risk Prediction of the 2015 ATA Recurrence Risk Stratification in Papillary Thyroid Cancer.

Abstract

Background: Various prognostic factors are expected to refine the American Thyroid Association (ATA) recurrence risk stratification for patients with papillary thyroid cancer (PTC). However, it remains unclear to what extent integrating these factors improves patient treatment decision-making.

Methods: We developed two predictive models for structural incomplete response (SIR) at the one-year follow-up visit, based on comprehensive clinical data from a retrospective cohort of 2539 patients. Model 1 included the recurrence risk stratification and lymph node features (i.e., number and ratio of metastatic lymph nodes, N stage). Model 2 further incorporated preablation stimulated thyroglobulin (s-Tg). An independent cohort of 746 patients was used for validation analysis. We assessed the models' predictive performance compared to the recurrence risk stratification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement (NRI). The clinical utility of the models was evaluated using decision curve analysis.

Results: Both Model 1 and Model 2 outperformed the recurrence risk stratification in predicting SIR, with improved correct classification rates (Model 1: IDI=0.02, event NRI=42.31%; Model 2: IDI=0.07, event NRI=53.54%). The decision curves indicated that both models provided greater benefits over the risk stratification system in clinical decision-making. In the validation set, Model 2 maintained similar performance while Model 1 did not significantly improve correct reclassification.

Conclusion: The inclusion of lymph node features and s-Tg showed potential to enhance the predictive accuracy and clinical utility of the existing risk stratification system for PTC patients.