Novel nomogram to predict risk of bone metastasis in newly diagnosed thyroid carcinoma: a population-based study

BMC Cancer

Zhiyi Fan¹  Yuexin Tong¹  Zhangheng Huang¹  Youxin Song¹  Chuan Hu²  Lujian Zhu³ 

[1] Department of Orthopedic, Affiliated Hospital of Chengde Medical University, No. 36 Nanyingzi St, 067000, Chengde, Hebei, China;Department of Orthopedic, Affiliated Hospital of Chengde Medical University, No. 36 Nanyingzi St, 067000, Chengde, Hebei, China;Qingdao University Medical College, 266000, Qingdao, Shandong, China;The First Affiliated Hospital of Wenzhou Medical University, 325000, Wenzhou, Zhejiang, China;

关键词: hyroid carcinoma;    bone;    metastasis;    risk factors;    Nomogram;    SEER database;   

DOI  :  10.1186/s12885-020-07554-1

来源: Springer

【 摘 要 】

BackgroundThe aim of this study was to develop and validate a visual nomogram for predicting the risk of bone metastasis (BM) in newly diagnosed thyroid carcinoma (TC) patients.MethodsThe demographics and clinicopathologic variables of TC patients from 2010 to 2015 in the Surveillance, Epidemiology and End Results (SEER) database were retrospectively reviewed. Chi-squared (χ2) test and logistic regression analysis were performed to identify independent risk factors. Based on that, a predictive nomogram was developed and validated for predicting the risk of BM in TC patients. The C-index was used to compute the predictive performance of the nomogram. Calibration curves and decision curve analysis (DCA) were furthermore used to evaluate the clinical value of the nomogram.ResultsAccording to the inclusion and exclusion criteria, the data of 14,772 patients were used to analyze in our study. After statistical analysis, TC patients with older age, higher T stage, higher N stage, poorly differentiated, follicular thyroid carcinoma (FTC) and black people had a higher risk of BM. We further developed a nomogram with a C-index of 0.925 (95%CI,0.895–0.948) in the training set and 0.842 (95%CI,0.777–0.907) in the validation set. The calibration curves and decision curve analysis (DCA) also demonstrated the reliability and accuracy of the clinical prediction model.ConclusionsThe present study developed a visual nomogram to accurately identify TC patients with high risk of BM, which might help to further provide more individualized clinical decision guidelines.

【 授权许可】

CC BY