Defining a Valid Age Cutoff in Staging of Well-Differentiated Thyroid Cancer



Age 45 years is used as a cutoff in the staging of well-differentiated thyroid cancer (WDTC) as it represents the median age of most datasets. The aim of this study was to determine a statistically optimized age threshold using a large dataset of patients treated at a comprehensive cancer center.


Overall, 1807 patients with a median follow-up of 109 months were included in the study. Recursive partitioning was used to determine which American Joint Committee on Cancer (AJCC) variables were most predictive of disease-specific death, and whether a different cutoff for age would be found. From the resulting tree, a new age cutoff was picked and patients were restaged using this new cutoff.


The 10-year disease-specific survival (DSS) by Union for International Cancer Control (AJCC/UICC) stage was 99.6, 100, 96, and 81 % for stages I–IV, respectively. Using recursive partitioning, the presence of distant metastasis was the most powerful predictor of DSS. For M0 patients, age was the next most powerful predictor, with a cutoff of 56 years. For M1 patients, a cutoff at 54 years was most predictive. Having reviewed the analysis, age 55 years was selected as a more robust age cutoff than 45 years. The 10-year DSS by new stage (using age 55 years as the cutoff) was 99.2, 98, 100, and 74 % for stages I–IV, respectively.


A change in age cutoff in the AJCC/UICC staging for WDTC to 55 years would improve the accuracy of the system and appropriately prevent low-risk patients being overstaged and overtreated.

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Iain J. Nixon, Deborah Kuk, Volkert Wreesmann, Luc Morris, Frank L. Palmer, Ian Ganly, Snehal G. Patel, Bhuvanesh Singh, R. Michael Tuttle, Ashok R. Shaha, Mithat Gönen, and Jatin P. Shah have no disclosures to declare.

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Correspondence to Iain J. Nixon PhD.

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Nixon, I.J., Kuk, D., Wreesmann, V. et al. Defining a Valid Age Cutoff in Staging of Well-Differentiated Thyroid Cancer. Ann Surg Oncol 23, 410–415 (2016).

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  • Thyroid Cancer
  • Staging System
  • Recursive Partitioning
  • Recursive Partitioning Analysis
  • Memorial Sloan Kettering Cancer Center