Elsevier

The Breast

Volume 36, December 2017, Pages 31-33
The Breast

Short communication
Artificial intelligence for breast cancer screening: Opportunity or hype?

https://doi.org/10.1016/j.breast.2017.09.003Get rights and content

Abstract

Interpretation of mammography for breast cancer (BC) screening can confer a mortality benefit through early BC detection, can miss a cancer that is present or fast growing, or can result in false-positives. Efforts to improve screening outcomes have mostly focused on intensifying imaging practices (double instead of single-reading, more frequent screens, or supplemental imaging) that may add substantial resource expenditures and harms associated with population screening. Less attention has been given to making mammography screening practice ‘smarter’ or more efficient. Artificial intelligence (AI) is capable of advanced learning using large complex datasets and has the potential to perform tasks such as image interpretation. With both highly-specific capabilities, and also possible un-intended (and poorly understood) consequences, this viewpoint considers the promise and current reality of AI in BC detection.

Section snippets

Breast cancer screening

Most developed health care systems have implemented population screening for breast cancer (BC) based on evidence from randomised trials that mammography confers BC mortality reduction, complemented by observational evidence of benefit from real-world screening [1], [2]. BC screening involves interpretation of digital mammograms to identify suspicious abnormalities that warrant further investigation (recall to assessment) to rule in or rule out BC. Mammography interpretation is subjective. In

Artificial intelligence (AI)

Machine learning (ML), a rapidly growing field of AI, integrating computer science and statistics, allows computers to learn without explicit programming through automatic extraction and analysis of complex data [12], [13], [14], [15], [16], [17]. ML is being touted as a potential AI tool to help discover new materials, master various games, and improve predictive ability in clinical medicine. The convergence of new ML techniques, such as deep learning (a class of learning algorithms that

Exploration of AI for BC screening

Medical imaging is ripe for incorporating AI solutions because of existing capabilities in computer vision and automated image feature analysis, the large storage capacity for hundreds of thousands of digital imaging exams through picture archiving and communication systems (PACS), linkage of PACS to electronic medical records, and the binary outcome of imaging-based screening tests [10]. Recent work developing AI algorithms in digital mammography interpretation has undertaken the conversion of

Future research using AI for BC screening

Although research into the capability of AI in mammography screening at present focuses on interpretive accuracy with the anticipation of primarily reducing false-positives, there remains much opportunity to extend exploration of the role of AI in BC screening, for example to develop future models that also better identify BC. This would not be limited to potential detection of cancers ‘missed’ at human screen-reading (with the aim of reducing the frequency of interval cancers) but may

Unexplored aspects of AI for BC screening

A number of unexplored issues, highly relevant for the application of AI in BC screening, warrant consideration and research effort. These include the social and ethical concerns and implications inherent in entrusting cancer detection to an AI model, and the possibility of un-intended consequences [11]. These issues need to be explored early in the phase of developing AI models for BC screening to provide an understanding of societal perspectives, and to define an ethical-legal framework for

Conclusion

Whether the anticipated promise of AI in BC detection, or indeed more broadly in cancer research, translates into practice and meets expectations remains to be seen over the coming years. At present, however, AI represents an opportunity that is both feasible and timely for exploration in population BC screening. To make the most of this opportunity, extremely large data-sets of imaging examinations linked to clinical factors and cancer outcomes are needed to train and validate robust AI models

Financial disclosure

None to disclose.

Conflict of interest statement (COI)

None to declare.

Acknowledgements

N. Houssami receives research support through a National Breast Cancer Foundation (NBCF Australia), Breast Cancer Research Leadership Fellowship.

References (17)

  • S. Ciatto et al.

    Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study

    Lancet Oncol

    (2013)
  • C. Ding et al.

    Trunk-branch ensemble convolutional neural networks for video-based face recognition

    IEEE Trans Pattern Anal Mach Intell

    (2017)
  • B. Lauby-Secretan et al.

    Breast-cancer screening — viewpoint of the IARC working group

    N Engl J Med

    (2015)
  • Independent UK Panel on Breast Cancer Screening

    The benefits and harms of breast cancer screening: an independent review

    The Lancet

    (2012)
  • H.D. Nelson et al.

    Harms of breast cancer screening: systematic review to update the 2009 U.S. Preventive services task force recommendation

    Ann Intern Med

    (2016)
  • Breast Cancer Surveillance Consortium (BCSC). Performance measures for 1,838,372 screening mammography examinations...
  • N. Houssami

    Overdiagnosis of breast cancer in population screening: does it make breast screening worthless?

    Cancer Biol Med

    (2017)
  • N. Houssami et al.

    The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening

    Nat (NPJ) Breast Cancer

    (2017)
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