Literature DB >> 31563481

Diagnostic Performance Using Automated Breast Ultrasound System for Breast Cancer in Chinese Women Aged 40 Years or Older: A Comparative Study.

Li Zhang1, Ling-Yun Bao2, Yan-Juan Tan1, Luo-Qian Zhu1, Xiao-Jing Xu1, Qing-Qing Zhu1, Yan-Na Shan3, Jing Zhao3, Le-Si Xie4, Jan Liu5.   

Abstract

The purpose of this study was to investigate the diagnostic performance of the automated breast ultrasound system (ABUS) compared with hand-held ultrasonography (HHUS) and mammography (MG) for breast cancer in women aged 40 y or older. A total of 594 breasts in 385 patients were enrolled in the study. HHUS, ABUS and MG exams were performed for these patients. Follow-up and pathologic findings were used as the reference standard. Based on the reference standard, 519 units were benign or normal and 75 were malignant. The sensitivity, specificity, accuracy and Youden index were 97.33%, 89.79%, 90.74% and 0.87 for HHUS; 90.67%, 92.49%, 92.26% and 0.83 for ABUS; 84.00%, 92.87%, 91.75% and 0.77 for MG, respectively. The specificity of ABUS was significantly superior to that of HHUS (p = 0.024). The area under the receiver operating characteristic curve was 0.936 for HHUS, which was the highest, followed by 0.916 for ABUS and 0.884 for MG. However, the difference was not statistically significant (p > 0.05). In conclusion, the diagnostic performance of ABUS for breast cancer was equivalent to HHUS and MG and potentially can be used as an alternative method for breast cancer diagnosis.
Copyright © 2019. Published by Elsevier Inc.

Entities:  

Keywords:  Automated breast ultrasound system; Breast cancer; Hand-held ultrasonography; Mammography

Year:  2019        PMID: 31563481     DOI: 10.1016/j.ultrasmedbio.2019.08.016

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


  3 in total

1.  Performance of novel deep learning network with the incorporation of the automatic segmentation network for diagnosis of breast cancer in automated breast ultrasound.

Authors:  Qiucheng Wang; He Chen; Gongning Luo; Bo Li; Haitao Shang; Hua Shao; Shanshan Sun; Zhongshuai Wang; Kuanquan Wang; Wen Cheng
Journal:  Eur Radiol       Date:  2022-04-30       Impact factor: 7.034

Review 2.  Evaluation of Diagnostic Performance of Automatic Breast Volume Scanner Compared to Handheld Ultrasound on Different Breast Lesions: A Systematic Review.

Authors:  Shahad A Ibraheem; Rozi Mahmud; Suraini Mohamad Saini; Hasyma Abu Hassan; Aysar Sabah Keiteb; Ahmed M Dirie
Journal:  Diagnostics (Basel)       Date:  2022-02-19

3.  Prediction model of axillary lymph node status using automated breast ultrasound (ABUS) and ki-67 status in early-stage breast cancer.

Authors:  Qiucheng Wang; Bo Li; Zhao Liu; Haitao Shang; Hui Jing; Hua Shao; Kexin Chen; Xiaoshuan Liang; Wen Cheng
Journal:  BMC Cancer       Date:  2022-08-28       Impact factor: 4.638

  3 in total

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