Improving Cancer Detection in Women with Dense Breasts
Detecting breast cancer in its early stages in women with dense breasts has become increasingly important to women’s health clinicians. However, identifying lesions in dense breasts is challenging. The breast is made of fibroglandular and fatty tissues. The more fibroglandular tissue, the denser the breast.
Women with dense breasts are at a greater risk of breast cancer. Because fibroglandular tissue shows white on mammograms, as do cancerous lesions, dense breasts increase the difficulty of identifying lesions and diagnosing breast cancer in its early stages. To tackle the challenge of increasing sensitivity of mammograms for patients with dense breasts, manufacturers have developed multiple software packages and upgrades to increase image clarity and assist radiologists and physicians in interpreting images.
Breast density assessment is important in managing patients’ breast care, and many states have passed laws requiring breast density reporting to patients and to referring providers. Breast density assessment has traditionally been evaluated by a clinician’s visual assessment of the mammogram. Due to its subjective nature, this method results in inconsistent classification of breast density.
Volumetric breast density software measures density by calculating the ratio of fibroglandular tissue to total breast volume. This software allows clinicians to objectively assign cases to the appropriate class of density for reporting purposes. Women with breasts classified as dense may undergo additional screening, such as supplementing the mammogram with a breast ultrasound or MRI.
Computer-aided detection (CAD) software, widely used in mammography, essentially acts as a second set of eyes, highlighting areas that may be abnormal through preprogrammed pattern recognition. This signals to the clinician that an area needs closer examination. Although this software is very useful in aiding lesion identification, it also increases the number of false positives, resulting in unnecessary additional screening and diagnostics for some patients.
Tomosynthesis is also widely used, with most facilities opting for 3D systems with a 2D image synthesizing software package over a full field digital (FFDM) system. While tomosynthesis is useful in analyzing all breast types, it is especially beneficial for evaluating dense breasts. Tomosynthesis creates a 3D image of the breast by taking multiple x-rays at different angles.
This technology not only aids in identifying cancerous lesions earlier, but it also helps reduce the number of false positives and callbacks. This is due to the ability of clinicians to more easily distinguish cancerous lesions from fibroglandular tissue by viewing the multiple slices of a 3D image compared to viewing a single 2D image.
The promise of AI
One recent innovation attracting interest in mammography and in other imaging modalities is artificial intelligence (AI) software. AI learns patterns continuously, and over time accuracy in its lesion detection increases. AI can increase the sensitivity of mammograms for dense breasts, increasing early detection of breast cancer in patients with higher risk. This software also has the potential to improve workflow with a more consistent and automated approach to analyzing an image.
The goal of each advancement in mammography is to increase early detection of breast cancer and to decrease the occurrence of false positives. These upgrades to mammography systems can be utilized together in analyzing mammograms, increasing the overall quality of an exam and increasing the likelihood of a positive outcome for each case.