When a mammogram detects a suspicious lesion, a needle biopsy is performed to determine if it is cancer. Roughly 70 percent of the lesions are benign, 20 percent are malignant, and 10 percent are high-risk lesions.
Doctors manage high-risk lesions in different ways. Some do surgery in all cases, while others perform surgery only for lesions that have higher cancer rates, such as “atypical ductal hyperplasia” (ADH) or a “lobular carcinoma in situ” (LCIS).
The first approach requires that the patient undergo a painful, time-consuming, and expensive surgery that is usually unnecessary; the second approach is imprecise and could result in missing cancers in high-risk lesions other than ADH and LCIS.
“The vast majority of patients with high-risk lesions do not have cancer, and we’re trying to find the few that do,” says Bahl, a fellow doctor at MGH’s Department of Radiology. “In a scenario like this there’s always a risk that when you try to increase the number of cancers you can identify, you’ll also increase the number of false positives you find.”
Using a method known as a “random-forest classifier,” the team’s model resulted in fewer unnecessary surgeries compared to the strategy of always doing surgery, while also being able to diagnose more cancerous lesions than the strategy of only doing surgery on traditional “high-risk lesions.” (Specifically, the new model diagnosed 97 percent of cancers compared to 79 percent.)
“This work highlights an example of using cutting-edge machine learning technology to avoid unnecessary surgery,” says Marc Kohli, director of clinical informatics in the Department of Radiology and Biomedical Imaging at the University of California at San Francisco. “This is the first step toward the medical community embracing machine learning as a way to identify patterns and trends that are otherwise invisible to humans.”
Lehman says that MGH radiologists will begin incorporating the model into their clinical practice over the next year.
“In the past we might have recommended that all high-risk lesions be surgically excised,” Lehman says. “But now, if the model determines that the lesion has a very low chance of being cancerous in a specific patient, we can have a more informed discussion with our patient about her options. It may be reasonable for some patients to have their lesions followed with imaging rather than surgically excised.”
The team says that they are still working to further hone the model.
“In future work we hope to incorporate the actual images from the mammograms and images of the pathology slides, as well as more extensive patient information from medical records,” says Bahl.
Moving forward, the model could also easily be tweaked to be applied to other kinds of cancer and even other diseases entirely.
“A model like this will work anytime you have lots of different factors that correlate with a specific outcome,” says Barzilay. “It hopefully will enable us to start to go beyond a one-size-fits-all approach to medical diagnosis.”
By Adam Conner-Simons