Abstract
The prognosis of oxyphilic cell papillary thyroid carcinoma (OCPTC) remains unclear. The aim of this study was to investigate the prognosis of OCPTC and provide a new perspective on treatment guidelines for these patients. We investigated a large cohort of DTC patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2013. Patient mortality was examined by Kaplan-Meier analyses with log-rank tests and Cox proportional hazards regression analyses. In the study cohort, the rate of cancer-specific mortality per 1000 person-years for OCPTC was lower than that for classic papillary thyroid cancer (CPTC) and follicular thyroid cancer (FTC). According to the multivariate Cox regression model, the cancer-specific and all-cause mortality rates of OCPTC were similar to that of CPTC and FTC. The cancer-specific survival rate in patients with OCPTC was higher than that in patients with FTC, but similar to patients with CPTC, after matching for influential factors using propensity score matching analysis. The unanticipated prognosis provided new implications for the treatment of patients with OCPTC.
Highlights
Thyroid cancer has been rising rapidly in recent decades [1,2,3,4,5]
The cancer-specific mortality rate, per 1000 person-years, for oxyphilic cell papillary thyroid carcinoma (OCPTC), classic papillary thyroid cancer (CPTC), and follicular thyroid cancer (FTC) were 1.872 [95% confidence interval (CI), 0.264–13.293], 2.512, and 6.68, respectively (Table 2)
The all-cause mortality, per 1000 person-years, in patients with OCPTC, CPTC, and FTC were 16.852, 10.538 and 18.583, respectively (Table 2)
Summary
Thyroid cancer has been rising rapidly in recent decades [1,2,3,4,5]. Papillary thyroid cancer (PTC) accounts for 80–90% of all thyroid malignancies, making it the most common type of thyroid malignancy [6]. Rare histological variants of PTC include follicular, tall cell, columnar cell, diffuse sclerosing, solid, hobnail, and insular variants [7,8,9,10,11]. Propensity score matching (PSM) method is a statistical matching technique for analyzing observational data by estimating the effects of a treatment, policy, or other intervention and accounting for covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding variables
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