Literature DB >> 29331358

Assessing Risk Category of Breast Cancer by Ultrasound Imaging Characteristics.

Qiang Guo1, Lei Zhang2, Zhixin Di2, Chunping Ning3, Zhiwu Dong4, Ziyao Li2, Dongmo Wang2, Chong Liu2, Ming Zhao2, Jiawei Tian5.   

Abstract

The purpose of our study was to assess the potential clinical value of ultrasound imaging in predicting risk category in patients with breast cancer. Three hundred thirty-six patients were enrolled and divided into a high-risk group (99, 29.5%) and mid- to low-risk group (237, 70.5%) according to the St. Gallen risk criteria. All data were retrospectively collected to analyze correlations between ultrasound features and risk category. The results revealed that the ultrasound features of irregular shape (p= 0.002), vertical growth orientation (p= 0.002), angular contour (p= 0.022) and high color Doppler flow imaging grade (p= 0.001) tended to be present in images of the high-risk group. Therefore, tumor ultrasound features should be recognized as an ideal option for determination of risk category in patients with breast cancer.
Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast neoplasms; Lymphatic metastasis; Prognosis; Recurrence; St. Gallen; Ultrasonography

Mesh:

Year:  2018        PMID: 29331358     DOI: 10.1016/j.ultrasmedbio.2017.12.001

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


  9 in total

1.  A computer-assisted system for handheld whole-breast ultrasonography.

Authors:  Filip Šroubek; Michal Bartoš; Jan Schier; Zuzana Bílková; Barbara Zitová; Jan Vydra; Iva Macová; Jan Daneš; Lukáš Lambert
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-01-23       Impact factor: 2.924

2.  A Novel lncRNA Panel for Risk Stratification and Immune Landscape in Breast Cancer Patients.

Authors:  Chen Li; Xiaolong Wang; Tong Chen; Wenhao Li; Qifeng Yang
Journal:  Int J Gen Med       Date:  2022-05-27

3.  Machine Learning Models to Improve the Differentiation Between Benign and Malignant Breast Lesions on Ultrasound: A Multicenter External Validation Study.

Authors:  Ling Huo; Yao Tan; Shu Wang; Cuizhi Geng; Yi Li; XiangJun Ma; Bin Wang; YingJian He; Chen Yao; Tao Ouyang
Journal:  Cancer Manag Res       Date:  2021-04-16       Impact factor: 3.989

4.  Predictive Value of Ultrasound Characteristics for Disease-Free Survival in Breast Cancer.

Authors:  Qiang Guo; Zhiwu Dong; Lixin Jiang; Lei Zhang; Ziyao Li; Dongmo Wang
Journal:  Diagnostics (Basel)       Date:  2022-06-29

5.  Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model.

Authors:  Alvaro Ruiz-Martinez; Chang Gong; Hanwen Wang; Richard J Sové; Haoyang Mi; Holly Kimko; Aleksander S Popel
Journal:  PLoS Comput Biol       Date:  2022-07-22       Impact factor: 4.779

6.  Breast ultrasound in Chinese hospitals: A cross-sectional study of the current status and influencing factors of BI-RADS utilization and diagnostic accuracy.

Authors:  Luying Gao; Jianchu Li; Yang Gu; Li Ma; Wen Xu; Xixi Tao; Ruojiao Wang; Rui Zhang; Yixuan Zhang; Hongyan Wang; Yuxin Jiang
Journal:  Lancet Reg Health West Pac       Date:  2022-08-27

7.  Sonography with vertical orientation feature predicts worse disease outcome in triple negative breast cancer.

Authors:  Haoyu Wang; Weiwei Zhan; Weiguo Chen; Yafen Li; Xiaosong Chen; Kunwei Shen
Journal:  Breast       Date:  2019-10-23       Impact factor: 4.380

8.  Association of sonographic features and molecular subtypes in predicting breast cancer disease outcomes.

Authors:  Haoyu Wang; Jiejie Yao; Ying Zhu; Weiwei Zhan; Xiaosong Chen; Kunwei Shen
Journal:  Cancer Med       Date:  2020-07-13       Impact factor: 4.452

9.  Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer.

Authors:  Hongxu Zhang; Haiwang Liu; Lihui Ma; Jianping Liu; Dawei Hu
Journal:  J Healthc Eng       Date:  2021-09-17       Impact factor: 2.682

  9 in total

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