• Bin Zhang, Shufang Pei, Qiuying Chen, Yuhao Dong, Lu Zhang, Xiaokai Mo, Shuzhen Cong, and Shuixing Zhang.
• Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
• Clin. Endocrinol. (Oxf). 2020 Dec 1; 93 (6): 729-738.

ObjectivesPrevious publications on risk-stratification systems for malignant thyroid nodules were based on conventional ultrasound only. We aimed to develop a practical and simplified prediction model for categorizing the malignancy risk of thyroid nodules based on clinical data, biochemical data, conventional ultrasound and real-time elastography.DesignRetrospective cohort study.PatientsA total of 2818 patients (1890 female, mean age, 45.5 ± 13.2 years) with 2850 thyroid nodules were retrospectively evaluated between April 2011 and October 2016. 26.8% nodules were malignant.MeasurementsWe used a randomly divided sample of 80% of the nodules to perform a multivariate logistic regression analysis. Cut-points were determined to create a risk-stratification scoring system. Patients were classified as having low, moderate and high probability of malignancy according to their scores. We validated the models to the remaining 20% of the nodules. The area under the curve (AUC) was used to evaluate the discrimination ability of the systems.ResultsTen variables were selected as predictors of malignancy. The point-based scoring systems with and without elasticity score achieved similar AUCs of 0.916 (95% confidence interval [CI]: 0.885-0.948) and 0.906 (95% CI: 0.872-0.941) when validated. Malignancy risk was segmented from 0% to 100.0% and was positively associated with an increase in risk scores. We then developed a Web-based risk-stratification system of thyroid nodules (http: thynodscore.com).ConclusionA simple and reliable Web-based risk-stratification system could be practically used in stratifying the risk of malignancy in thyroid nodules.© 2020 John Wiley & Sons Ltd.

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