Is Mammography Ready for AI?
RadNet is among a growing number of commercial companies offering AI solutions for mammography. MammoScreen and Hologic, for example, are two other companies that provide AI programs to assist radiologists in reading screening mammograms.
“We are at the start of the AI integration into breast imaging at this point,” said Laura Heacock, MD, a breast imaging radiologist and associate professor of radiology at NYU Langone Health. “There are multiple commercial AI models now available to radiologists to use in their practice [ and] there will likely be more. We’re in the transition stage where people are still deciding: Which is the best model to go with? How do I put it in my system? How do I ensure it works they way it was intended? Every practice and medical system will have a different answer to that question.”
At NYU Langone Health, researchers have been developing and studying optimal AI models for breast imaging for several years, Dr. Heacock said. Researchers thus far, have developed AI models for 2D digital mammography, 3D mammograms, breast ultrasound, and breast MRI. Similar to commercial AI systems, the AI is embedded into the picture archiving and communication (PACS) system used by radiologists to review images. Radiologists press a button to launch the AI, which draws a box around suspicious areas of the image and scores the suspicion.
“I take a look of where it is on the mammogram and decide whether that fits my level of suspicion,” Dr. Heacock said. The AI may not understand things about the mammogram like we do. For example, surgical scars look very suspicious to an AI model. But if I’m looking at a mammogram where [the patient] has had a stable scar that hasn’t changed in 10 years, I’m not concerned that the AI found it suspicious. My clinical judgment is the ultimate decider. This is just an additional piece of information that’s helpful to me.”
Research by New York University (NYU) has shown that when used by an expert radiologist the AI models have improved breast cancer detection in all four modalities, she said.
However, the AI has not yet launched at NYU Langone. More research is needed before deploying the technology, according to Dr. Heacock.
“At NYU, we are still testing the benefits to patients,” she said. “We know it improves cancer detection, but we want to make sure there are no drawbacks. We are still exploring the best ways to put it into effect at our institution.”
Dr. Heacock pointed to recent studies on AI in screening mammography that show promise.
An analysis of more than 80,000 women, for example, published in The Lancet Oncology in August, found that AI-supported screen reading led to a similar cancer detection rate as compared with a two-person reader system. This screening resulted in 244 screen-detected cancers, 861 recalls, and a total of 46,345 screen readings, according to the study. Standard screening resulted in 203 screen-detected cancers, 817 recalls, and a total of 83,231 screen readings.
The AI system also reduced the screen-reading workload for radiologists by 44%, the study found.
Meanwhile, a September 2023 study, published in The Lancet Digital Health, found that replacing one radiologist with AI resulted in more cancer detection without a large increase in false-positive cases. The AI led to a 4% higher, noninferior cancer detection rate, compared with radiologist double reading, the study found.
Dr. Heacock emphasized that both studies were conducted in Europe where the standard is for two radiologists to evaluate mammograms.
“That makes the results exciting, but not directly applicable to US practice just yet,” she said.