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27 July 2019 : Clinical Research

Development and Validation of a Nomogram for Predicting Survival in Patients with Thyroid Cancer

Qian Wen12AE, Yong Yu34ADE, Jin Yang1C, Xinwen Wang5C, Jian Wen2B, Yuting Wen6B, Yi Wang6B, Jun Lyu1A*

DOI: 10.12659/MSM.915620

Med Sci Monit 2019; 25:5561-5571

Abstract

BACKGROUND: The AJCC staging system is inadequate for use in patients with thyroid carcinomas. Here, we aimed to establish a nomogram for thyroid cancer, and we compare its prognostic value with the AJCC staging system in adults diagnosed with thyroid carcinoma.

MATERIAL AND METHODS: Patient records were obtained from the Surveillance, Epidemiology, and End Result database. The 8491 included patients were divided into a modeling cohort (n=5943) and a validation cohort (n=2548). The variables included in the modeling cohort were selected using a backward stepwise selection method with Cox regression, and the prognosis nomogram was constructed. In the validation cohort, we compared our survival model with the AJCC prognosis model using the concordance index, the area under the time-dependent receiver operating characteristic curve, the net reclassification improvement, the integrated discrimination improvement, calibration plotting, and decision curve analysis.

RESULTS: Twelve independent prognostic factors were identified and used to establish the nomogram. In particular, marital status was included in a survival prediction model of thyroid cancer for the first time. The concordance index, area under the time-dependent receiver operating characteristic curve, net reclassification improvement, integrated discrimination improvement, calibration plotting, and decision curve analysis for the nomogram showed better performance compared to the AJCC staging system.

CONCLUSIONS: We have developed and validated a highly accurate thyroid cancer prognosis nomogram. The prognostic value of the nomogram is better than that of the AJCC staging system alone.

Keywords: Disease-Free Survival, nomograms, Thyroid Neoplasms

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