Prognostic factors and survival score for patients with anaplastic thyroid carcinoma: a retrospective study from a regional registry

Abstract

BACKGROUND AND PURPOSE Anaplastic thyroid carcinoma (ATC) is the least common but most lethal of thyroid cancers despite various therapeutic options with limited efficacy. Some prognostic factors were identified in patients with ATC and a few patients survive for a relatively long time after modern intensive treatment. In order to help therapeutic decision-making, the purpose of this study was to develop a new prognostic score providing survival estimates in patients with ATC. METHODS Based on a multivariate analysis of 149 retrospectively analyzed patients diagnosed with ATC from 1968 to 2017 at a referral center, a propensity score was developed. A model was generated providing survival probability at 6 months and median overall survival estimates. RESULTS The median survival was 96 days. The overall survival rate was 35% at 6 months, 20% at 1 year and 13% at 2 years. Most of the patients (86%) died within 17 month, 17% died within the first month, 35% lived for 1-6 months and 47 % of the patients lived longer than 6 months after the initial consultation. The stepwise Cox regression revealed that the most appropriate death prediction model included metastatic spread, tumor size and age class as explanatory variables.This model made it possible to define three categories of patients with survival profiles which seems different: patients with no pejorative prognostic factor which had a survival probability at 6 months = 0,84 (95% CI: 0,69-1), patients with one or two pejoratives prognostics factors which have a survival probability at 6 months = 0,32 (95% CI: 0,22-0,46), and those with three pejoratives prognostics factors which had a survival probability at 6 months = 0,11 (95% CI: 0,018 -0,71). CONCLUSION Distant metastasis, age and primary tumor size are strong independent factors that affect prognosis in patients with ATC.Using these significant pretreatment factors, we developed a score to predict survival in these poor prognosis patients in order to provide easy-to-use tools for clinical practice. External validation in an additional dataset is needed for further outlooks.