WeChat Mini Program
Old Version Features

Multimodality Ultrasound Utilizing Microvascular Flow Imaging and Shear Wave Elastography to Guide Fine-Needle Aspiration of Thyroid Lesions: A Prospective Study Validating Pattern-Based Microvascular Classification.

Jiamin Chen, Jing Zhong, Yu Zhuang, Bixue Deng, Jiayi Hong,Yuhong Lin,Zhongzhen Su, Xin Wen

Thyroid official journal of the American Thyroid Association(2025)

Department of Ultrasound

Cited 0|Views1
Abstract
Background: Most current guidelines recommend fine-needle aspiration (FNA) biopsy of thyroid nodules based on grayscale ultrasound (GUS) features, but the biopsy rate for benign nodules remains high. Our aim was to construct a new pattern-based microvascular classification (PBMC) for thyroid nodules to develop and validate predictive multimodality US models based on GUS, microvascular flow imaging, and shear wave elastography, and compare FNA decision accuracy with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). Methods: This prospective study included consecutive patients with thyroid nodules who underwent multimodality US examinations from September 2022 to December 2023. Using PBMC, lesions were divided into three categories: malignant signs (convergence sign, piercing sign, and spoke wheel sign), benign signs (ring sign), and other vascular patterns. Univariate and multivariable logistic regression analyses were conducted to determine the odds ratios (ORs) of US features, including vascular signs, and construct predictive models based on multimodality US. Multimodality US models were validated with internal cross-validation and evaluated based on discrimination, calibration, and decision curve analyses. Results: Overall, 793 thyroid nodules confirmed using pathological analysis (248 benign and 545 malignant) in 599 participants (mean age, 43 years ±11 [SD]) were included. In univariate logistic regression analyses, malignant vascular signs showed a positive association with malignant nodules (OR: 10.43, 95% confidence interval [CI]: 5.76, 18.88; p < 0.01), whereas benign vascular signs were inversely associated with malignancy (OR: 0.10, 95% CI: 0.06, 0.16; p < 0.01). Four multivariable models incorporated GUS features, Young's modulus, and PBMC. The highest area under the receiver operating characteristic curve (AUC) was 0.95 (95% CI: 0.82, 0.97) for the multimodality US model, and the lowest AUC was 0.62 (95% CI: 0.57, 0.66) for ACR TI-RADS based on GUS (p < 0.001). At a 71% risk threshold, multimodality US avoided 27% (95% CI: 21, 34) of FNA procedures, compared with 13% (95% CI: 0, 38) with TI-RADS (p < 0.001). Conclusion: Visual assessment of microvascular morphology patterns may improve differentiation of benign and malignant thyroid nodules and potentially reduce the risk of unnecessary biopsy of benign thyroid nodules.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined