VIDEO: Connie Lehman discusses trends in breast imaging
Constance "Connie" Lehman, MD, PhD, chief of breast imaging, co-director of the Avon Comprehensive Breast Evaluation Center at the Massachusetts General Hospital, and professor of radiology at Harvard Medical School, discusses trends in breast imaging.
"Mammography is clearly the the best tool we have to detect breast cancer early when it can be cured, but it does have its limitations in very dense breast tissue," Lehman said. "Clearly the advances in digital mammography have improved our ability to penetrate dense breast tissue."
She said this was clear in clinical trials comparing full field digital mammography (FFDM) to film mammography. This has been further improved by the advance of digital breast tomosynthesis (DBT), which shoots a series of images that are reconstructed into slices, so radiologists can look between the layers that are hard to see through when they are stacked
"We are excited about 3D tomosynthesis, where we can do thin slices through breast tissue to uncover these cancers that hide in those dense, dense areas of the breast," she explained.
The U.S. Food and Drug Administration (FDA) reports that nearly 50% of mammography systems are now tomosynthesis, and each year that number grows. Lehman said 3D mammography is rapidly becoming the standard-of-care. However, she said there are still concerns about the higher cost of these new systems.
"If you had a community that was really having challenges in resources, would you rather screen twice as many women with 2D mammography, or screen half as many women with 3D?" She said.
Lehman is also excited about advances in vascular imaging, which includes breast MRI and contrast-enhanced mammography (CEM). "This allows us to see areas that are recruiting blood vessels as the cancer cells and the tumors grow," she said.
Other technologies to watch include the use of artificial intelligence (AI), which is being used to help better assess breast density and with sorting through large numbers of breast images in DBT datasets.