Literature DB >> 31444031

Quantitative Ultrasound Image Analysis of Axillary Lymph Nodes to Diagnose Metastatic Involvement in Breast Cancer.

David Coronado-Gutiérrez1, Gorane Santamaría2, Sergi Ganau2, Xavier Bargalló2, Stefania Orlando3, M Eulalia Oliva-Brañas3, Alvaro Perez-Moreno4, Xavier P Burgos-Artizzu5.   

Abstract

This study aimed to assess the potential of state-of-the-art ultrasound analysis techniques to non-invasively diagnose axillary lymph nodes involvement in breast cancer. After exclusion criteria, 105 patients were selected from two different hospitals. The 118 lymph node ultrasound images taken from these patients were divided into 53 cases and 65 controls, which made up the study series. The clinical outcome of each node was verified by ultrasound-guided fine needle aspiration, core needle biopsy or surgical biopsy. The achieved accuracy of the proposed method was 86.4%, with 84.9% sensitivity and 87.7% specificity. When tested on breast cancer patients only, the proposed method improved the accuracy of the sonographic assessment of axillary lymph nodes performed by expert radiologists by 9% (87.0% vs 77.9%). In conclusion, the results demonstrate the potential of ultrasound image analysis to detect the microstructural and compositional changes that occur in lymph nodes because of metastatic involvement.
Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Axillary lymph node; Breast cancer; Cancer metastasis; Deep learning; Image analysis; Image biomarker; Machine learning; Quantitative ultrasound; Ultrasound

Mesh:

Year:  2019        PMID: 31444031     DOI: 10.1016/j.ultrasmedbio.2019.07.413

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


  6 in total

Review 1.  Deep learning in breast radiology: current progress and future directions.

Authors:  William C Ou; Dogan Polat; Basak E Dogan
Journal:  Eur Radiol       Date:  2021-01-15       Impact factor: 5.315

2.  Identifying and overcoming limitations with in situ calibration beads for quantitative ultrasound.

Authors:  Jenna Cario; Andres Coila; Yuning Zhao; Rita J Miller; Michael L Oelze
Journal:  J Acoust Soc Am       Date:  2022-04       Impact factor: 2.482

3.  The role of ultrasound elastography and virtual touch tissue imaging in the personalized prediction of lymph node metastasis of breast cancer.

Authors:  Jue Wang; Zhifei Ben; Shanshan Gao; Shuyi Lyu; Xiuzhi Wei
Journal:  Gland Surg       Date:  2021-04

4.  Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis.

Authors:  Qiuhan Zheng; Le Yang; Bin Zeng; Jiahao Li; Kaixin Guo; Yujie Liang; Guiqing Liao
Journal:  EClinicalMedicine       Date:  2020-12-25

Review 5.  The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.

Authors:  Tomoyuki Fujioka; Mio Mori; Kazunori Kubota; Jun Oyama; Emi Yamaga; Yuka Yashima; Leona Katsuta; Kyoko Nomura; Miyako Nara; Goshi Oda; Tsuyoshi Nakagawa; Yoshio Kitazume; Ukihide Tateishi
Journal:  Diagnostics (Basel)       Date:  2020-12-06

6.  A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.

Authors:  Gaosen Zhang; Yan Shi; Peipei Yin; Feifei Liu; Yi Fang; Xiang Li; Qingyu Zhang; Zhen Zhang
Journal:  Front Oncol       Date:  2022-07-25       Impact factor: 5.738

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.