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The diagnostic performance of automated versus handheld breast ultrasound and mammography in symptomatic outpatient women: a multicenter, cross-sectional study in China

European Radiology volume 31, pages 947–957 (2021)Cite this article

Abstract

Objectives

The purpose of this study was to evaluate the diagnostic performance of automated breast ultrasound (ABUS) for breast cancer by comparing it to handheld ultrasound (HHUS) and mammography (MG).

Methods

A multicenter cross-sectional study was conducted between February 2016 and March 2017 in five tertiary hospitals in China, and 1922 women aged 30–69 years old were recruited. Women aged 30–39 years (group A) underwent ABUS and HHUS, and women aged 40–69 (group B) underwent additional MG. Images were interpreted using the Breast Imaging Reporting and Data System (BI-RADS). All BI-RADS 4 and 5 cases were confirmed pathologically. Sensitivities and specificities of all modalities were compared.

Results

There were 83 cancers in 677 women in group A and 321 cancers in 1245 women in group B. In the whole study population, the sensitivities of ABUS and HHUS were 92.8% (375/404) and 96.3% (389/404), and the specificities were 93.0% (1411/1518) and 89.6% (1360/1518), respectively. ABUS had a significantly higher specificity to HHUS (p < 0.01), while HHUS had higher sensitivity (p = 0.01). In group B, the sensitivities of ABUS, HHUS, and MG were 93.5% (300/321), 96.6% (310/321), and 87.9% (282/321). The specificities were 93.0% (859/924), 89.9% (831/924), and 91.6% (846/924). ABUS had significantly higher sensitivity (p = 0.02) and comparable specificity compared with MG (p = 0.14).

Conclusion

ABUS increased sensitivity and had similar specificity compared with mammography in the diagnosis of breast cancer. Additionally, ABUS has comparable performance to HHUS in women aged 30–69 years old. ABUS or HHUS is a suitable modality for breast cancer diagnosis.

Key Points

• In breast cancer diagnosis settings, automated breast ultrasound has a higher cancer detection rate, sensitivity, and specificity than mammography, especially in women with dense breasts.

• Compared with handheld ultrasound, automated breast ultrasound has higher specificity, lower sensitivity, and comparable diagnostic performance.

• Automated breast ultrasound is a suitable modality for breast cancer diagnosis, and may have a potential indication for its further use in the breast cancer early detection.

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Abbreviations

ABUS:

Automated breast ultrasound

ACR BI-RADS:

American College of Radiology Breast Imaging Reporting and Data System

AUC:

Area under the curve

CDR:

Cancer detection rate

CIs:

Confidence intervals

DCIS:

Ductal carcinoma in situ

FPR:

False positive rate

HHUS:

Handheld ultrasound

MG:

Mammography

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Acknowledgments

The authors would like to thank everyone who participated in this study, including the many individuals not specifically mentioned in the paper who provided significant support.

Funding

This work was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS 2017-I2M-B&R-03), the GE Healthcare (11183574598), and the Peking Union Medical College Doctoral Innovation Fund (2017-1001-18).

Author information

Author notes

  1. Xi Lin and Mengmeng Jia contributed equally as co-first authors.

  2. Youlin Qiao and Anhua Li contributed equally as co-correspondence authors.

Affiliations

  1. Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China

    Xi Lin & Anhua Li

  2. Department of Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Mengmeng Jia & Youlin Qiao

  3. Department of Interventional Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Xiang Zhou

  4. Department of Ultrasound, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China

    Lingyun Bao & Luoxi Zhu

  5. Department of Ultrasound, Xin Hua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China

    Yaqing Chen & Hui Wang

  6. Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China

    Peifang Liu & Ying Zhu

  7. Department of Cancer Prevention Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China

    Ruimei Feng

  8. Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing Office for Cancer Prevention and Control, Peking University Cancer Hospital and Institute, Beijing, 100142, China

    Xi Zhang

  9. Department of Ultrasound, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China

    Guoxue Tang

  10. Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Wenqi Feng

  11. School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Youlin Qiao

Authors
  1. Xi Lin
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  2. Mengmeng Jia
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  3. Xiang Zhou
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  8. Xi Zhang
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  10. Hui Wang
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  13. Wenqi Feng
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  14. Anhua Li
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  15. Youlin Qiao
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Corresponding authors

Correspondence to Anhua Li or Youlin Qiao.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Youlin Qiao from the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College.

Conflict of interest

Author Mengmeng Jia is the 2018 GE Healthcare Call for proposal Awardee. Other authors declare that they have no conflict of interest.

Statistics and biometry

Four of the authors (Mengmeng Jia, Xi Zhang, Ruimei Feng, and Youlin Qiao) have significant statistical expertise.

Informed consent

Written informed consent was obtained from all participants in this study.

Ethical approval

Institutional Review Board approval was obtained from all five study centers, including Cancer Hospital, Chinese Academy of Medical Sciences, Sun Yat-sen University Cancer Center, the First People’s Hospital of Hangzhou, Xin Hua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, and Tianjin Medical University Cancer Institute and Hospital.

Study subjects or cohorts overlap

The methodology of this study has been previously reported in the following paper:

Zhang X, Lin X, Tan Y, et al A multicenter hospital-based diagnosis study of automated breast ultrasound system in detecting breast cancer among Chinese women. Chin J Cancer Res. 2018;30(2):231–239. doi:10.21147/j.issn.1000-9604.2018.02.06

Another newly published paper, which evaluated and compared the diagnostic performance of ABUS and HHUS as adjuncts to mammogram in women with dense breasts, has some overlap participant with the current study. Only the women with dense breasts were analyzed in the previous study, and these two papers have different objectives. (Jia, M., Lin, X., Zhou, X. et al Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts. Breast Cancer Res Treat (2020). https://doi.org/10.1007/s10549-020-05625-2).

Methodology

• Prospective study

• Cross-sectional study

• Multicenter study

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Lin, X., Jia, M., Zhou, X. et al. The diagnostic performance of automated versus handheld breast ultrasound and mammography in symptomatic outpatient women: a multicenter, cross-sectional study in China. Eur Radiol 31, 947–957 (2021). https://doi.org/10.1007/s00330-020-07197-7

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  • DOI: https://doi.org/10.1007/s00330-020-07197-7

Keywords

  • Breast cancer
  • Breast ultrasound
  • Mammography
  • Diagnostic performance