The value of S-Detect for the differential diagnosis of breast masses on ultrasound: a systematic review and pooled meta-analysis.

Jun Li, Tian Sang, Wen-Hui Yu, Meng Jiang, Shu-Yan Hunag, Chun-Li Cao, Ming Chen, Yu-Wen Cao, Xin-Wu Cui, Christoph F Dietrich

Journal: Medical ultrasonography 2021;22(2):211-219

PMID: 32399528

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Abstract

AIM

To evaluate the value of S-Detect (a computer aided diagnosis system using deep learning) in breast ultrasound (US) for discriminating benign and malignant breast masses.

MATERIAL AND METHODS

A literature search was performed and relevant studies using S-Detect for the differential diagnosis of breast masses were selected. The quality of included studies was assessed using a Quality Assessment of Diagnostic Accuracy Studies (QUADAS) questionnaire. Two review authors independently searched the articles and assessed the eligibility of the reports.

RESULTS

A total of ten studies were included in the meta-analysis. The pooled estimates of sensitivity and specificity were 0.82 (95%CI: 0.77-0.87) and 0.86 (95%CI: 0.76-0.92), respectively. In addition, the diagnostic odds ratios, positive likelihood ratio and negative likelihood ratio were 28 (95%CI: 16- 49), 5.7 (95%CI: 3.4-9.5), and 0.21 (95%CI: 0.16-0.27), respectively. Area under the curve was 0.89 (95%CI: 0.86-0.92). No significant publication bias was observed.

CONCLUSIONS

S-Detect exhibited a favourable diagnostic value in assisting physicians discriminating benign and malignant breast masses and it can be considered as a useful complement for conventional US.

Address: Department of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China. [email protected].; Department of Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Xinjiang, China. [email protected].; Department of Medical Ultrasound, Wuchang Hospital affiliated Wuhan University of Science and Technology, Wuhan, China. [email protected].; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan China. [email protected].; Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China. [email protected].; Department of Medical Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China. [email protected].; Department of Medical Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China. [email protected].; Department of Medical Ultrasound, the First Affiliated Hospital of Medical College, Shihezi University, Shihezi, China. [email protected].; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. [email protected].; Department of Internal Medicine, Hirslanden Clinic, Bern, Switzerland. [email protected].

DOI: 10.11152/mu-2402

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https://doi.org/10.11152/mu-2402

Medical:

https://medlineplus.gov/breastcancer.html

MeSH Terms: Breast, Breast Neoplasms, Diagnosis, Differential, Female, Humans, Sensitivity and Specificity, Ultrasonography, Mammary