The Role of Contrast Enhanced Mammography (CEM)
CEM is an advanced breast imaging technique that uses a contrast agent to significantly improve the detection and characterization of breast cancer. When originally introduced in 2011, CEM consistently demonstrated better imaging results compared with standard mammography, especially when dealing with dense breast tissue. This is especially important at NYGH as racialized women—who are more likely to have dense breast tissue, a known risk factor for breast cancer—make up a significant portion of our community.
Often those with dense breast tissue would require an MRI in addition to their mammogram, however, wait times can be lengthy to receive an MRI. CEM offers sensitivity and specificity for breast cancer detection comparable to breast MRI; it can be used as an adjunct/alternate for breast cancer staging, and it is more cost-effective than MRI for intermediate-risk screening and the evaluation of dense breasts.
The Role of Artificial Intelligence
AI poses a unique opportunity for care teams, as the technology can continuously use data points from thousands of patients (anonymously) to help predict the pathways of future patients. AI detection technology assists radiologists to interpret mammograms by using a deep-learning neural network to analyze tomosynthesis images and identify lesions which appear to be potentially cancerous.
The algorithm searches for the common visual appearances of cancers: calcifications, masses, densities, and distortions. This will help our care teams plan ahead and potentially intervene sooner as images are assigned a case score indicating severity or urgency which can help prioritize review. North York General is a beta site for AI testing in Canada as Health Canada determines its approval and as such will help lead the future of breast health care for individuals across the country.
In terms of what success looks like in its implementation, it is the intention that AI will reduce the number of false positives reported from mammograms, which would decrease the number of recalls (or patients being required to come back for additional screening).