Abstract

Objectives

To establish superb microvascular imaging (SMI) based thyroid imaging reporting and data system (SMI TI-RADS) for risk stratification of malignancy in thyroid nodules.

Methods

In total, 471 patients, comprising 643 thyroid nodules, who received conventional ultrasound (US), SMI, and a final diagnosis were extensively analyzed. A qualitative assessment of US features of the nodules was performed followed by univariable and multivariable logistic regression analyses, leading to the construction of the SMI TI-RADS, which was further verified using internal and external validation cohorts.

Results

Among the stand-alone US, predictive factors were the shape and margins of the nodules, echogenicity and echogenic foci, vascularity, extrathyroidal extension, ring-SMI patterns, penetrating vascularity, flow-signal enlarged, and vascularity area ratio. SMI TI-RADS depicted an enhanced area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI: 0.92, 0.96; p < 0.001 relative to other stratification systems), a 79% biopsy yield of malignancy (BYM, 189/240 nodules), and a 21% unnecessary biopsy rate (UBR, 51/240 nodules). In the verification cohorts, we demonstrated AUCs, malignancy biopsy yields, and unnecessary biopsy rates of 0.88 (95% CI: 0.83, 0.94), 79% (59/75 nodules), and 21% (16/75 nodules) for the internal cohort, respectively, and 0.91 (95% CI: 0.85, 0.96), 72% (31/43 nodules), and 28% (12/43 nodules) for the external cohort, respectively.

Conclusion

SMI TI-RADS was found to be superior in diagnostic sensitivity, specificity, and efficiency than existing TI-RADSs, showing better stratification of the malignancy risk, and thus decreasing the rate of unnecessary needle biopsy.

Critical relevance statement

To develop an imaging and data system based on conventional US and SMI features for stratifying the malignancy risk in thyroid nodules.

Key Points

SMI features could improve thyroid nodule risk stratification.

SMI TI-RADS showed superior diagnostic efficiency and accuracy for biopsy guidance.

SMI TI-RADS can provide better guidance for clinical diagnosis and treatment of thyroid nodules.

Details

Title
Application of microvascular ultrasound-assisted thyroid imaging report and data system in thyroid nodule risk stratification
Author
Ma, Guangrong 1 ; Chen, Libin 2 ; Wang, Yong 3 ; Luo, Zhiyan 4 ; Zeng, Yiqing 1 ; Wang, Xue 1 ; Shi, Zhan 1 ; Zhang, Tao 1   VIAFID ORCID Logo  ; Hong, Yurong 1 ; Huang, Pintong 5 

 The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Ultrasound in Medicine, Hangzhou, P.R. China (GRID:grid.412465.0); Zhejiang University, Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (GRID:grid.13402.34) (ISNI:0000 0004 1759 700X) 
 The First Affiliated Hospital of Ningbo University, Department of Ultrasound in Medicine, Ningbo, P.R. China (GRID:grid.460077.2) (ISNI:0000 0004 1808 3393) 
 The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Department of Thyroid Surgery, Hangzhou, P.R. China (GRID:grid.13402.34) (ISNI:0000 0004 1759 700X) 
 The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Ultrasound in Medicine, Hangzhou, P.R. China (GRID:grid.412465.0) 
 The Second Affiliated Hospital of Zhejiang University School of Medicine, Department of Ultrasound in Medicine, Hangzhou, P.R. China (GRID:grid.412465.0); Zhejiang University, Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (GRID:grid.13402.34) (ISNI:0000 0004 1759 700X); Binjiang Institute of Zhejiang University, Research Center for Life Science and Human Health, Hangzhou, P.R. China (GRID:grid.13402.34) (ISNI:0000 0004 1759 700X) 
Pages
230
Publication year
2024
Publication date
Dec 2024
Publisher
Springer Nature B.V.
e-ISSN
18694101
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3108458108
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.