Development and Validation of an Ultrasonic Diagnostic Model for Differentiating Follicular Thyroid Carcinoma from Follicular Adenoma

Qingshan Huang; Lijun Xie; Liyan Huang; Weili Wei; Haiying Li; Yunfang Zhuang; Xinxiu Liu; Shuqiang Chen; Sufang Zhang

doi:10.2147/IJGM.S331338

Development and Validation of an Ultrasonic Diagnostic Model for Differentiating Follicular Thyroid Carcinoma from Follicular Adenoma

Int J Gen Med. 2021 Aug 30:14:5069-5078. doi: 10.2147/IJGM.S331338. eCollection 2021.

Authors

Qingshan Huang¹, Lijun Xie², Liyan Huang², Weili Wei², Haiying Li², Yunfang Zhuang², Xinxiu Liu², Shuqiang Chen², Sufang Zhang²

Affiliations

¹ Musculoskeletal Tumor Center, Peking University People's Hospital, Beijing, 100044, People's Republic of China.
² Department of Ultrasound, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, People's Republic of China.

Abstract

Background: High-resolution ultrasound is the first choice for the diagnosis of thyroid nodules, but it is still difficult to distinguish between follicular thyroid carcinoma (FTC) and follicular adenoma (FA). Our research aimed to develop and validate an ultrasonic diagnostic model for differentiating FTC from FA.

Methods: This study retrospectively analyzed 196 patients who were diagnosed as FTC (n=83) and FA (n=113). LASSO regression analysis was used to screen clinical and ultrasonic features. Multivariate logistic regression analysis was used to establish the ultrasonic diagnostic model of FTC. Nomogram was used for the visualization of diagnostic models. C-index, ROC, and calibration curves analysis were used to evaluate the accuracy of the diagnostic model. Decision curve analysis (DCA) was used to evaluate the net benefits of the ultrasonic diagnostic model for FTC diagnosis under different threshold probabilities. The bootstrap method was used to verify the ultrasonic diagnostic model.

Results: After Lasso regression analysis, 10 clinical and ultrasonic features were used to construct the ultrasonic diagnostic model of FTC. The C-index and AUC of the model were 0.868 and 0.860, respectively. DCA showed that the ultrasonic model had good clinical application value. The C-index in the validation group was 0.818, which was close to the C-index in the model.

Conclusion: Ultrasonic diagnostic model constructed with 10 clinical and ultrasonic features can better distinguish FTC from FA.

Keywords: diagnostic; follicular adenoma; follicular thyroid carcinoma; ultrasonic.

Grants and funding

This work was supported by the Fujian Medical University Sailing Fund Project (No. 2018QH1085), Fujian Medical University Sailing Fund Project (No. 2018QH1086), Natural Science Foundation of Fujian (No. 2017J01289), and Medical Innovation Project of Fujian (No. 2017-CX-32).