Literature DB >> 23312962

Three-dimensional elastography for cervical lymph node volume measurements: a study to investigate feasibility, accuracy and reliability.

Michael Ying1, Yong-Ping Zheng, Brian Chin-Wing Kot, James Chung-Wai Cheung, Sammy Chi-Him Cheng, Dora Lai-Wan Kwong.   

Abstract

This study investigated the feasibility of using three-dimensional (3-D) elastography in measuring cervical lymph node volume and compared the accuracy and reliability of 3-D elastography and 3-D grayscale ultrasound in measurement of ill-defined cervical nodes. Eighteen porcine lymph nodes from the neck were embedded in tissue-mimicking phantoms and scanned with the two ultrasound techniques. Ultrasound measurements were compared with the volume determined by water-displacement method to evaluate measurement accuracy. Inter-observer reproducibility and intra-observer repeatability of measurements were evaluated. Four patients with enlarged neck nodes were included to evaluate intra-observer repeatability of ultrasound measurements. Results demonstrated that lymph nodes that appeared ill-defined on grayscale ultrasound showed well-defined boundaries on elastography. 3-D elastography has higher measurement accuracy (84.2%), reproducibility (intraclass correlation coefficient, ICC = 0.909) and repeatability (ICC = 0.964-0.988) than does 3-D grayscale ultrasound (62.2%, 0.777 and 0.863-0.906 respectively). As a conclusion, 3-D elastography is accurate and reliable in volume measurement of ill-defined lymph nodes and has potential for accurate assessment of lymph node volume.
Copyright © 2013 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

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Year:  2013        PMID: 23312962     DOI: 10.1016/j.ultrasmedbio.2012.10.005

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  2 in total

1.  Five-dimensional ultrasound system for soft tissue visualization.

Authors:  Nishikant P Deshmukh; Jesus J Caban; Russell H Taylor; Gregory D Hager; Emad M Boctor
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-08-15       Impact factor: 2.924

Review 2.  Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.

Authors:  Ye-Jiao Mao; Hyo-Jung Lim; Ming Ni; Wai-Hin Yan; Duo Wai-Chi Wong; James Chung-Wai Cheung
Journal:  Cancers (Basel)       Date:  2022-01-12       Impact factor: 6.639

  2 in total

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