Finding the Optimal Age Cutoff for the UICC/AJCC TNM Staging System in Patients with Papillary or Follicular Thyroid Cancer.

Abstract

Background: Differentiated thyroid cancer (DTC) is the only cancer entity for which the UICC/AJCC (Union for International Cancer Control and American Joint Committee on Cancer) TNM (tumor-node-metastasis) staging system involves an age cutoff as a prognostic criterion. However, the optimal age cutoff has not yet been determined in detail. The aim of our study was therefore to investigate the optimal age cutoff for the TNM staging system to predict disease-specific survival (DSS) with a focus on differences between patients with papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC). Methods: We retrospectively studied two large well-described cohorts of adult DTC patients from a Dutch and a German university hospital. DSS was analyzed for DTC overall, and for PTC and FTC separately, using several age cutoffs (per 5-year increment between 20 and 85 years and subsequently 1-year increments between 35 and 55 years), employing the histopathological criteria from the TNM staging system, eighth edition. Results: We included 3074 DTC patients (77% PTC and 23% FTC; mean age at diagnosis was 49 years). Median follow-up was seven years. For DTC and for PTC and FTC separately, the majority of the age cutoffs had a better statistical model performance than a model with no age cutoff. For DTC overall and for PTC, an age cutoff of 50 years had the best statistical model performance, while it was 40 years for FTC. Conclusions: In this large European population of DTC patients, when employing the histopathological criteria of the TNM system (eighth edition), the optimal age cutoff to predict DSS is 50 years rather than the 55 years currently in use. With the optimal age cutoff being 50 years for PTC and 40 years for FTC, there was a substantial difference in age cutoff for the respective histological entities. Therefore, implementation of different age cutoffs for PTC and FTC could improve the predictive value of the TNM staging system.