Development and validation of a Web-based malignancy risk-stratification system of thyroid nodules.
OBJECTIVES
Previous 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.


DESIGN
Retrospective cohort study.


PATIENTS
A 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.


MEASUREMENTS
We 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.


RESULTS
Ten 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).


CONCLUSION
A simple and reliable Web-based risk-stratification system could be practically used in stratifying the risk of malignancy in thyroid nodules.
['Zhang|Bin|B|0000-0002-6286-6227', 'Pei|Shufang|S|', 'Chen|Qiuying|Q|', 'Dong|Yuhao|Y|', 'Zhang|Lu|L|', 'Mo|Xiaokai|X|', 'Cong|Shuzhen|S|', 'Zhang|Shuixing|S|0000-0001-7377-382X']
D054459:Elasticity Imaging Techniques D005260:Female D006801:Humans D007231:Infant, Newborn D020407:Internet D012189:Retrospective Studies D013964:Thyroid Neoplasms D016606:Thyroid Nodule D014463:Ultrasonography
thyroid nodules real-time elastography risk stratification ultrasonography