Future Horizons in Breast Cancer Screening

Breast cancer remains a significant health concern, responsible for more than 600,000 deaths annually. Early detection, particularly through screening, is pivotal in mitigating its impact. The current gold standard for breast cancer detection is annual screening mammography starting at age 40, as per the guidelines of the American College of Radiology and the American Cancer Society.¹ Annual mammographic screening has been shown to reduce breast cancer deaths and catch cancers at earlier and more treatable stages. While mammography has been instrumental in the early treatment of breast cancer, it has known limitations, particularly in the setting of dense fibroglandular breast tissue.² In recent years, there has been a surge of innovative technologies aimed at overcoming the limitations inherent in traditional screening approaches. These advances promise to both increase the accuracy and timeliness of cancer detection in the future.

Mammographic screening is designed to detect structural changes in the breast parenchyma caused by cancer over time. This requires compression of the breast, which can be challenging for many patients, and a small dose of radiation. Advanced imaging techniques such as cone beam breast computed tomography (CBCT), contrast-enhanced mammography (CEM), abbreviated breast magnetic resonance imaging (AB-MRI) and ultrasound tomography (UT) all seek to address the limitations of breast cancer by removing the need for compression, by avoiding ionizing radiation or by allowing functional imaging of the breast tissue to better identify early and interval breast cancers.

The rapid rise and development of artificial intelligence (AI) in radiology has been particularly notable in breast imaging, where it is being rapidly implemented into the clinical workflow. By using sophisticated algorithms capable of learning and adapting, AI has the capability to assist and augment radiologists as they interpret mammograms and other breast imaging. This has been shown to both increase accuracy and decrease false positive imaging. AI is also poised to usher in a new level of personalized breast cancer risk assessment, allowing more accurate identification of patients who could benefit from additional screening methods.

Abbreviated Breast Magnetic Resonance Imaging (AB-MRI)

Breast MRI is currently recommended for breast cancer screening in patients at intermediate (>15%) and high lifetime risk (>20%) of breast cancer. This includes patients who are genetic mutation carriers known to be at increased risk of breast cancer (BRCA1, BRCA2, CHEK2 and others), women with a strong family history of premenopausal breast cancer, and those with a high lifetime risk as determined through breast cancer risk assessment. Breast MRI has also been shown to have high cancer detection rates in women at average risk of breast cancer, although with an increased chance of false-positive biopsies. However, breast MRI is currently underutilized by patients due to a combination of cost, limited availability in some regions and an inability to tolerate the examination length.

Cone-beam Breast Computed Tomography (CBBCT)

Cone-beam breast computed tomography (CBBCT) is an attempt to improve on the limitations of both mammography and breast MRI. Both mammography and breast MRI require breast compression for accurate images to be taken, which is difficult for some patients to tolerate. Breast MRI allows the patient to lie prone but further requires the arms to be positioned over the patient’s head, which is difficult for a patient with a history of shoulder surgery or arthritis. CBBCT allows the patient to lie prone without compression for imaging and provides three-dimensional images at a higher resolution than breast MRI, including visualization of calcifications. The three-dimensional resolution allows improved resolution in dense breasts compared to digital breast tomosynthesis, with less tissue overlap.

Ultrasound Tomography (UT)

Supplemental screening ultrasound is the most common supplemental screening modality for women at increased risk of breast cancer due to its accessibility, relatively low cost and ease of use. However, screening ultrasound has a relatively low additional cancer detection rate and, when performed by a radiologist or technologist, is dependent on the skill of the operator. Recent FDA-approved ultrasound tomography (UT) improves on standard breast ultrasound by capturing tomographic, three-dimensional views of the breast tissue to improve lesion detection and characterization. Similar to CBBCT and MRI, the patient lies prone, and there is minimal compression compared to mammography.

UT does not require ionizing radiation or injected contrast. However, unlike CEM, CBBCT and FAST MRI, this means it evaluates tissue structure alone without physiological information from contrast inflow, limiting its cancer detection rate compared to these modalities. UT, however, shows potential in supplemental breast cancer screening, assessing breast density, tumor volume, and tumor response to neoadjuvant therapy.

The AI Frontier in Breast Imaging

Breast imaging remains at the forefront of clinical AI implementation in radiology, with over 22 FDA-approved clinical algorithms. Radiologists have used computer-aided detection since the 1990s to assist in the interpretation of mammograms. However, these detection programs ultimately were not proven to increase cancer detection rates. New advances in machine learning and artificial intelligence computer-aided detection have leveraged large datasets of more than a million mammograms. These AI systems can detect cancer at rates comparable to that of experienced breast imagers, with the best performance coming from the combination of a human and AI reader and are now in routine clinical use at many practice sites.

Recent large prospective studies have confirmed that the use of AI in mammography and digital breast tomosynthesis is comparable or better to human reader performance with no significant increase in recall rates.⁹ These studies were performed, however, in countries where two radiologists routinely read each mammogram. While this is likely generalizable to the United States, similar large-scale studies are needed to confirm these performance results.