In mammography, “density” refers to the amount of dense tissue present in the breast, as measured on a standard mammogram. Dense tissue, such as glandular and fibrous tissue, is generally harmless; however, mammographic density is an important risk factor for breast cancer. Fortunately, advancements in artificial intelligence (AI) are revolutionizing the future of breast cancer screening, according to a review article recently published in the journal Trends in Cancer. Read on for more information about how AI mammography can improve breast cancer diagnostics and outcomes.
Predicting Breast Cancer with AI Mammography
Women with dense breasts have a statistically higher risk of developing breast cancer. This is due to two factors: First, dense tissue contains more cells that can potentially become cancerous. Second, dense breast tissue makes it more difficult for radiologists to see cancer on standard mammograms. While fatty tissue looks dark on a mammogram readout, dense tissue generally looks white. Breast masses and cancers may also look white, which makes them difficult to see among large amounts of dense tissue. This is known as the “masking effect.”
Given this important factor in breast cancer risk, doctors are increasingly notifying women of their mammographic density during breast screenings. For example, in the United States, the Food and Drug Administration (FDA) now requires that all mammogram reports sent to patients include breast density. However, despite this increased focus on density, clinicians continue to struggle with issues arising from the masking effect. AI could help alleviate these issues and revolutionize mammography.
Deep Learning and AI Mammography
According to the review article, “advanced computational approaches” such as deep learning are one key way that AI could be used to analyze mammographic images, including those with barriers related to breast density. Some AI-generated mammographic features may indicate “early malignancy that is undetectable by radiologist-read mammography,” the authors write. Additionally, these “risk-predicting AI-generated mammographic features” could provide new opportunities to identify women at risk of developing breast cancer without the harmful roadblocks provided by the masking effect. For example, the BRAIx risk score is an AI-driven, mammogram-based biomarker for breast cancer created by applying AI to standard mammography. This score is being piloted to arm women with more information about their health and breast cancer risk before any breast masses appear.
Clinical Applications of AI Advancements
Crucially, AI mammography could play a role in disease prevention. Imagine a scenario in which AI allows clinicians to identify a woman with mammographic features associated with a high risk of breast cancer. That woman may benefit from more frequent cancer screenings; she might also be a candidate for risk-reducing medication, which could lower her risk of developing breast cancer.
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Issues like breast density can stand in the way of effective breast cancer diagnostics and prevention. Fortunately, developments in AI may be transforming the mammography landscape before our very eyes.
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