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09 November 2019 : Clinical Research  

Nomograms Predict Survival in Patients with Anaplastic Thyroid Carcinoma

Bei Qiu1AB, Hong-qiang Li1CD, Qun-gang Chang1EF, De-tao Yin1AFG*

DOI: 10.12659/MSM.918245

Med Sci Monit 2019; 25:8447-8456

Abstract

BACKGROUND: Anaplastic thyroid carcinoma (ATC) is a very rare, highly lethal malignant cancer. Our aim in this study was to develop nomograms that predict survival in ATC patients.

MATERIAL AND METHODS: ATC incidence and mortality were assessed via joinpoint regression analysis of 567 ATC patients selected from the Surveillance, Epidemiology, and End Results 18 Registries Research database. Predictive models were established via univariate and multivariate Cox regression analysis of potential risk factors and used to produce nomograms. Performance of the nomograms in terms of discrimination ability and calibration was evaluated by determining the concordance index (C-index) and by generating calibration plots, respectively.

RESULTS: The incidence and mortality rates for ATC increased from 2000 to 2015 according to the collected data (p<0.05). Two nomograms were constructed based on 2 predictive models: nomogram 1 considered age, tumor size, and metastasis (all before surgery), and nomogram 2 considered age, tumor size, metastasis, surgery, and extrathyroidal extension (all after surgery). Both nomogram 1 (C-index, 0.6803; 95% confidence interval, 0.6517–0.7089) and nomogram 2 (C-index, 0.7064; 95% confidence interval, 0.6783–0.7345) had good discrimination ability. The validated C-index values were 0.6783 and 0.7029 for nomogram 1 and 2, respectively. The observed values were in agreement with the calibration curves.

CONCLUSIONS: Nomogram 1 can assist in preoperative prediction of survival time in ATC patients, whereas nomogram 2 can provide additional outcome-related information.

Keywords: general surgery, nomograms, Forecasting, Incidence, Predictive Value of Tests, Regression Analysis, Risk Factors, Thyroid Carcinoma, Anaplastic

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750