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Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis

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Abstract

Breast cancer was the fourth leading cause of cancer-related death worldwide, and early mammography screening could decrease the breast cancer mortality. Artificial intelligence (AI)-assisted diagnose system based on machine learning (ML) methods can help improve the screening accuracy and efficacy. This study aimed to systematically review and make a meta-analysis on the diagnostic accuracy of mammography diagnosis of breast cancer through various ML methods. Springer Link, Science Direct (Elsevier), IEEE Xplore, PubMed and Web of Science were searched for relevant studies published from January 2000 to September 2021. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42021284227). A Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the included studies, and reporting was evaluated using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). The pooled summary estimates for sensitivity, specificity, the area under the receiver operating characteristic curve (AUC) for three ML methods (convolutional neural network [CNN], artificial neural network [ANN], support vector machine [SVM]) were calculated. A total of 32 studies with 23,804 images were included in the meta-analysis. The overall pooled estimate for sensitivity, specificity and AUC was 0.914 [95% CI 0.868–0.945], 0.916 [95% CI 0.873–0.945] and 0.945 for mammography diagnosis of breast cancer through three ML methods. The pooled sensitivity, specificity and AUC of CNN were 0.961 [95% CI 0.886–0.988], 0.950 [95% CI 0.924–0.967] and 0.974. The pooled sensitivity, specificity and AUC of ANN were 0.837 [95% CI 0.772–0.886], 0.894 [95% CI 0.764–0.957] and 0.881. The pooled sensitivity, specificity and AUC of SVM were 0.889 [95% CI 0.807–0.939], 0.843 [95% CI 0.724–0.916] and 0.913. Machine learning methods (especially CNN) show excellent performance in mammography diagnosis of breast cancer screening based on retrospective studies. More rigorous prospective studies are needed to evaluate the longitudinal performance of AI.

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The data of this study are from published literatures and shown in Appendix B.

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Acknowledgements

We thank Zengbin Li and Rui Li for good suggestions in meta-analysis.

Funding

This work was supported by the National Natural Science Foundation of China (12171387 (MS)); China Postdoctoral Science Foundation (2018M631134 (MS), 2020T130095ZX (MS)); Young Talent Support Program of Shaanxi University Association for Science and Technology (20210307 (MS)). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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  1. School of Medicine, Xi’an Jiaotong University, Xi’an, 710061, Shaanxi, People’s Republic of China

    Junjie Liu, Jiangjie Lei, Yuhang Ou, Yilong Zhao & Xiaofeng Tuo

  2. College of Stomatology, Xi’an Jiaotong University, Xi’an, 710004, Shaanxi, People’s Republic of China

    Baoming Zhang

  3. Key laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi’an Jiaotong University, Xi’an, 710004, Shaanxi, People’s Republic of China

    Baoming Zhang

  4. School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, 710061, Shaanxi, People’s Republic of China

    Baoming Zhang

  5. China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, 710061, Shaanxi, People’s Republic of China

    Mingwang Shen

  6. Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi’an Jiaotong University Health Science Center, Xi’an, 710061, Shaanxi, People’s Republic of China

    Mingwang Shen

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Contributions

MS and JL conceived and designed the study. JL, HO, JL, YZ and XT collected the data. JL analyzed the data, carried out the analysis and performed numerical simulations. JL wrote the first draft of the manuscript. MS critically revised the manuscript. All the authors contributed to writing the paper and agreed with the manuscript results and conclusions.

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Appendix A is a combination of terms used in our search of the five databases. The five electronic databases are Springer Link, Science Direct, IEEE Xplore, PubMed and Web of Science (PDF 44 KB)

Appendix B is the original data table we obtained after data extraction from the 32 included articles (XLSX 20 KB)

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Liu, J., Lei, J., Ou, Y. et al. Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis. Clin Exp Med 23, 2341–2356 (2023). https://doi.org/10.1007/s10238-022-00895-0

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