Literature DB >> 28273746

Background echotexture classification in breast ultrasound: inter-observer agreement study.

Won Hwa Kim1,2, Su Hyun Lee1, Jung Min Chang1, Nariya Cho1, Woo Kyung Moon1.   

Abstract

Background According to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS), background echotexture in breast ultrasound (US) can be categorized as homogeneous or heterogeneous. Purpose To prospectively evaluate the inter-observer agreement of a four-category classification in background echotexture assessments of breast US and to determine whether background echotexture is related to breast cancer risk factors, including mammography density. Material and Methods Thirty-eight healthy women (age range, 25-72) were recruited. Eleven radiologists performed breast US on all participants and classified each background echotexture into four categories (homogeneous, mild, moderate, and marked heterogeneous). The inter-observer agreement in the assessments was measured using kappa statistics (к). The association between background echotexture and breast cancer risk factors, including mammographic density, menopausal status, and parity, were evaluated using Spearman's correlation coefficient (ρ) and multiple linear regression analysis. Results There was moderate inter-observer agreement between the radiologists for the four categories of background echotexture (average к = 0.45). Heterogeneity of the background echotexture was positively correlated with mammographic density in both pre- and postmenopausal women (premenopausal, ρ = 0.42, P < 0.0001; postmenopausal, ρ = 0.56, P < 0.0001). Multiple linear regression analysis revealed that mammographic density and parity were significantly associated with background echotexture. Conclusion Background echotexture assessment of breast US using a four-category classification showed moderate inter-observer agreement, and more heterogeneous background echotexture was associated with denser breasts and lower parity.

Entities:  

Keywords:  BI-RADS; Breast ultrasonography; background echotexture; density; mammography

Mesh:

Year:  2017        PMID: 28273746     DOI: 10.1177/0284185117695665

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  7 in total

1.  Breast-density assessment with hand-held ultrasound: A novel biomarker to assess breast cancer risk and to tailor screening?

Authors:  Sergio J Sanabria; Orcun Goksel; Katharina Martini; Serafino Forte; Thomas Frauenfelder; Rahel A Kubik-Huch; Marga B Rominger
Journal:  Eur Radiol       Date:  2018-03-19       Impact factor: 5.315

2.  The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study.

Authors:  Xiao Luo PhD; Min Xu; Guoxue Tang; Yi Wang PhD; Na Wang; Dong Ni PhD; Xi Lin PhD; An-Hua Li
Journal:  Br J Radiol       Date:  2021-12-15       Impact factor: 3.039

3.  Assessment of MRI-Detected Breast Lesions: A Benign Correlate on Second-Look Ultrasound Can Safely Exclude Malignancy.

Authors:  Karin Hellerhoff; Hanna Dietrich; Regina Schinner; Dorothea Rjosk-Dendorfer; Anikó Sztrókay-Gaul; Maximilian Reiser; Susanne Grandl
Journal:  Breast Care (Basel)       Date:  2021-01-18       Impact factor: 2.268

Review 4.  Glandular Tissue Component on Breast Ultrasound in Dense Breasts: A New Imaging Biomarker for Breast Cancer Risk.

Authors:  Su Hyun Lee; Woo Kyung Moon
Journal:  Korean J Radiol       Date:  2022-06       Impact factor: 7.109

5.  Evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women: a cross-sectional study.

Authors:  Zhongtao Bao; Yanchun Zhao; Shuqiang Chen; Xiaoyu Chen; Xiang Xu; Linglin Wei; Ling Chen
Journal:  BMC Med Imaging       Date:  2021-10-19       Impact factor: 1.930

6.  Comparison of the background echotexture between automated breast ultrasound and handheld breast ultrasound.

Authors:  Jieun Kim; Eun Young Ko; Boo-Kyung Han; Eun Sook Ko; Ji Soo Choi; Ko Woon Park; Haejung Kim
Journal:  Medicine (Baltimore)       Date:  2022-07-08       Impact factor: 1.817

7.  Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks.

Authors:  Jeongmin Lee; Bong Joo Kang; Sung Hun Kim; Ga Eun Park
Journal:  Diagnostics (Basel)       Date:  2022-02-24
  7 in total

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