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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1340847

Using multimodal ultrasound including full-time-series contrastenhanced ultrasound cines for identifying the nature of thyroid nodules

Provisionally accepted
Hanlu He Hanlu He 1,2Junyan Zhu Junyan Zhu 1,2Zhengdu Ye Zhengdu Ye 3Haiwei Bao Haiwei Bao 3Jinduo Shou Jinduo Shou 4Ying Liu Ying Liu 2Fen Chen Fen Chen 1,2*
  • 1 Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, Beijing, China
  • 2 Department of Ultrasound, First Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou, China
  • 3 Department of Ultrasound, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
  • 4 Department of Ultrasound, Sir Run Run Shaw Hospital, Hangzhou, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    Based on the conventional ultrasound images of thyroid nodules, contrast-enhanced ultrasound (CEUS) videos were analyzed to investigate whether CEUS improves the classification accuracy of benign and malignant thyroid nodules using machine learning (ML) radiomics and compared with radiologists.The B-mode ultrasound (B-US), real-time elastography (RTE), color doppler flow images (CDFI) and CEUS cines of patients from two centers were retrospectively gathered. Then, the region of interest (ROI) was delineated to extract the radiomics features. Seven ML algorithms combined with four kinds of radiomics data (B-US, B-US + CDFI + RTE, CEUS, and B-US + CDFI + RTE + CEUS) were applied to establish 28 models. The diagnostic performance of ML models was compared with interpretations from expert and nonexpert readers.

    Keywords: thyroid nodules1, Ultrasonography 2, risk assessment3, machine learning4, Radiomics5

    Received: 14 Feb 2024; Accepted: 07 Aug 2024.

    Copyright: © 2024 He, Zhu, Ye, Bao, Shou, Liu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Fen Chen, Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.