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Breast cancer detection using machine learning in digital mammography and breast tomosynthesis: A systematic review

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Abstract

Artificial intelligence (AI) and particularly the introduction of deep learning convolutional neural networks brings new potential in computer-aided detection systems. In the context of breast cancer, several AI-based systems have been developed and assessed over the last decade, attempting to improve benefits in breast cancer screening accuracy and efficiency. This study focuses on mature systems developed for breast cancer detection in digital mammography (DM) and digital breast tomosynthesis (DBT) images, using machine learning (ML) algorithms. The aim is to gather evidence on their overall performance and readiness to be deployed in population screening practice. Moreover, their perspective wide use is discussed along with their limitations and further issues that still need to be addressed.

A systematic review was conducted with appropriate search keywords using the PubMed scientific database, limited to journal papers published during the last five years. Inclusion and exclusion criteria were set prior to the final selection of eligible studies.

The ML algorithms presented in the studies that were included in this review were found to be based on deep learning techniques. Vast majority of the systems had been earlier independently evaluated, while some of them are commercially available and have been FDA approved and/or CE marked. Although reviewed systems, show promising outcomes, either as standalone systems, or, as a concurrently used assistance to the radiologists during DM or DBT interpretation, there are several aspects that still need to be explored. More large-scale studies with wider range of datasets and populations are essential to clarify the exact potential of these systems and to reveal the full spectrum of AI benefits in breast cancer detection.

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Acknowledgements

The Research Project “ X-ray Image Processing and Diagnosis Techniques using Machine Learning and Sparse Representations: Application in Breast Imaging,MaLeSpaR4BI,” was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “1st Call for H.F.R.I. Research Projects to support Faculty Members & Researchers and the Procurement of High-and the procurement of high-cost research equipment grant” (Project Number: 4271).

Funding

This research work was funded by the Hellenic Foundation for Research and Innovation (H.F.R.I.) (Project Number: 4271).

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  1. Biomedical Technology Unit, Faculty of Medicine, University of Patras, Patras, Greece

    A. Malliori & N. Pallikarakis

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Malliori, A., Pallikarakis, N. Breast cancer detection using machine learning in digital mammography and breast tomosynthesis: A systematic review. Health Technol. 12, 893–910 (2022). https://doi.org/10.1007/s12553-022-00693-4

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