A nomogram for predicting overall survival in patients with follicular thyroid cancer after thyroidectomy: a SEER database analysis

BACKGROUND: A good predictive model requires patient participation in prognostic counseling and subsequent clinical follow-up. We aimed to construct and validate a nomogram for predicting overall survival (OS) in patients with follicular thyroid cancer (FTC) after thyroidectomy. METHODS: This was a retrospective observational study. We screened 802 patients with initially diagnosed FTC from the Surveillance Epidemiology and End Results (SEER) database between 2010 and 2015. Then the patients were all divided into the training set and validation set randomly at a ratio of 7:3. Univariate and multivariate Cox proportional hazard models were used to analyze the influence of different variables on OS. The concordance index (C-index) and calibration curves were used to evaluate the precision of the nomogram. RESULTS: Univariate and multivariate analyses demonstrated that four factors, namely age, grade, race, and M stage (all P<0.05), were independent predictors of OS in FTC patients. Based on these factors, a predictive model was established by using the training cohort and validated by the validation cohort. A good consistency between the actual OS and predicted OS was showed by the calibration curves. Moreover, compared with the traditional tumor-node-metastasis (TNM) staging system, the nomogram had better predictive ability for the survival of patients with FTC. The C-index of the nomogram in the training set and validation set had high consistency in evaluating the survival rate of patients with FTC [training set: C-index =0.904, 95% confidence interval (CI): 0.883–0.925; validation set: C-index =0.835, 95% CI: 0.772–0.898; TNM: C-index =0.775, 95% CI: 0.732–0.818]. CONCLUSIONS: Based on several clinical variables, we established the first predictive model of FTC after thyroidectomy by using Cox multivariate analysis which provide a basis for each patient with prognosis and postoperative follow-up.