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Using AI for personalized breast cancer risk assessment: An important tool for early detection

September 30, 2024
Women's Health
Lester Litchfield
By Lester Litchfield

Breast cancer is the most common cancer in women, accounting for about 30% of all new female cancers annually. With one in eight women in the U.S. developing breast cancer, the healthcare industry must adopt a nuanced approach to screening that considers individual medical histories and risk factors.

Early detection of breast cancer significantly improves patient outcomes as it often leads to less invasive treatments. However, predicting who is at the highest risk is complex.

Risk factors like breast tissue density, genetic predispositions and lifestyle factors all contribute to the complexity of determining who is at the highest risk, underscoring the need for personalized screening strategies that go beyond age-based guidelines.
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Mammography: Strengths and limitations
Mammography remains the gold standard for early detection, with over 41 million procedures performed annually. However, mammograms miss about 20% of existing breast cancers due to image quality variability and the difficulty of detecting cancer in dense breast tissue.

Breast density refers to the relative amounts of different kinds of fibrous, glandular, and fatty tissue, and is both a robust and independent risk factor for breast cancer and a factor impacting the sensitivity of mammography. On a mammogram, both dense tissue and tumors appear white, which can obscure potential cancers. As breast density increases, the sensitivity of mammograms decreases, making it harder to detect cancerous growths early. Dense tissue also increases your risk for developing breast cancer.

According to dense breast-info.org, some cancers will not show on a mammogram in women with dense breasts. Additional tests, such as ultrasound, molecular breast imaging, contrast enhancement, and MRI after a mammogram, may find these cancers. It is important to note, however, that women should not skip their mammogram, as some cancers are only seen on a mammogram, even in dense breasts.

Ordinarily, radiologists visually and subjectively assess a patient’s breast density to place them in one of four categories according to definitions set out in the American College of Radiology’s Breast Imaging Reporting and Data System (BI-RADS®). Alternatively, AI can automatically assess the volume of dense tissue present in a patient’s breast on a continuous scale to help ensure a more precise identification and consistent application of screening protocols. Research has shown that radiologists using only subjective, visual assessment assign density categories to patients inconsistently, and the variability between how different radiologists categorize density is quite high. Breast density can be objectively and quantitatively assessed as a percentage: the amount of dense tissue divided by the total breast tissue. Women with smaller breast volumes often have a higher density percentage, which further complicates cancer detection.

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