| Abstract: |
Background: In most staging systems, 45 years of age is used to differentiate low risk thyroid cancer from high risk thyroid cancer. However, recent studies have questioned both the precise 45 year age point and the concept of using a binary cut off as accurate predictors of disease specific mortality. Methods: A cohort of 3664 thyroid cancer patients that received surgery and adjuvant treatment at Memorial Sloan Kettering Cancer Center (MSKCC) from the years 1985 to 2010 were analyzed to determine the significance of age at diagnosis as a categorical variable at a variety of age cutoffs (5 year intervals between 30 and 70 years of age). The unadjusted and adjusted hazard ratio for the association between disease-specific survival and age was determined using a Cox proportional hazards model adjusted for other predictive variables sex, histology, and pathological T, N, and M status. Furthermore, predictive nomograms of disease-specific mortality were created and validated on an external dataset of 4551 patients to evaluate the impact of age at diagnosis as both a categorical and continuous variable. Results: In the MSKCC cohort, with a median follow-up time of 54 months (range 1-332), there were 59 deaths from thyroid cancer with a 10 year disease-specific survival of 96%. Adjusted hazard ratios for all age cutoffs from age 30 to age 70 years were significant. There was no specific cutoff age which risk stratifies patients with differentiated thyroid cancer (DTC). Categorizing age into five strata (<40, 40-49, 50-59, 60-69 and >70 years) showed a 37-fold increase in hazard ratio from age <40 years to age >70 years. A predictive nomogram using age as a continuous variable with other predictive variables had a high concordance index of 96%. Validation on the external cohort had a concordance index of 73%. Conclusions: Mortality from DTC increases progressively with advancing age. There is no specific cutoff age which risk stratifies patients with DTC. A predictive nomogram using age as a continuous variable may be a more appropriate tool for stratifying patients with DTC and for predicting outcome. © Mary Ann Liebert, Inc. 2015. |
