WeChat Mini Program
Old Version Features

    • A Practical CEUS Thyroid Reporting System for Thyroid Nodules

    Jingliang Ruan,Xiaolin Xu,Yan Cai,Hongyan Zeng,Man Luo,Wenyue Zhang,Rongbin Liu,Peiliang Lin,Yangming Xu, Qiong Ye,Bing Ou,Baoming Luo

    RADIOLOGY（2022）

    Department of Ultrasound

    Cited 32|Views29
    Abstract
    Background The role of contrast-enhanced US (CEUS) in reducing unnecessary biopsies of thyroid nodules has received little attention. Purpose To construct and externally validate a thyroid imaging reporting and data system (TI-RADS) based on nonenhanced US and CEUS to stratify the malignancy risk of thyroid nodules. Materials and Methods This retrospective study evaluated 756 patients with 801 thyroid nodules who underwent nonenhanced US, CEUS, and fine-needle aspiration and received a final diagnosis from January 2018 to December 2019. Qualitative US features of the thyroid nodules were analyzed with univariable and multivariable logistic regression to construct a CEUS TI-RADS. The CEUS TI-RADS was validated with use of internal cross-validation and external validation. Results A total of 801 thyroid nodules in 590 female (mean age, 44 years ± 13) and 166 male (mean age, 47 years ± 13 [SD]) patients were included. Independent predictive US features included nodule composition at CEUS, echogenicity, nodule shape, nodule margin, echogenic foci, extrathyroidal extension, enhancement direction, peak intensity, and ring enhancement. The CEUS TI-RADS showed a higher area under the receiver operating characteristic curve of 0.93 (95% CI: 0.92, 0.95; P < .001 in comparison with all other systems), a biopsy yield of malignancy of 66% (157 of 239 nodules), and an unnecessary biopsy rate of 34% (82 of 239 nodules). In the external validation, the area under the receiver operating characteristic curve, biopsy yield of malignancy, and unnecessary biopsy rate of CEUS TI-RADS were 0.89 (95% CI: 0.84, 0.92), 61% (65 of 106 nodules), and 39% (41 of 106 nodules) for the first external validation set and 0.90 (95% CI: 0.85, 0.94), 57% (56 of 99 nodules), and 43% (43 of 99 nodules) for the second external validation set. Conclusion A contrast-enhanced US (CEUS) thyroid imaging reporting and data system was created with thyroid nodule malignancy risk stratification according to the simplified regression coefficients of nonenhanced US and qualitative features of CEUS. Clinical trials registration no. ChiCTR2000028712 Published under a CC BY 4.0 license. Online supplemental material is available for this article.
    More
    Translated text
    Key words
    Thyroid Nodules,Ectopic Thyroid Tissue
    上传PDF
    View via Publisher
    Bibtex
    收藏
    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