About 1 in 5 new breast cancers are caught at their earliest stages, before they’ve spread from milk ducts into the surrounding breast tissue.
But what doctors can’t currently predict with high confidence is which of these cancers — known as ductal carcinoma in situ (DCIS) or stage 0 breast cancer — are likely to recur and spread after surgery, and which ones surgery is likely to cure.
Researchers at the U-M Rogel Cancer Center have developed a new diagnostic approach that would use artificial intelligence that aims to do exactly that — and with greater than 90% accuracy, according to findings published in the American Journal of Physiology-Cell Physiology.
“Scientists don’t really understand what leads to cancer recurrence at the molecular level and that has made it impossible to accurately predict which patients will experience a recurrence and which won’t,” says Howard Petty, Ph.D., a professor of ophthalmology and visual sciences, and of microbiology and immunology at Michigan Medicine, the University of Michigan’s academic medical center. “What we found is that certain key enzymes collect near the cell membrane in these early breast cancers that end up being aggressive, but they don’t in the cancers that are non-aggressive.”
Knowing how aggressive a stage 0 cancer is likely to be could help patients and their doctors decide on the best course of treatment — which is typically either breast conserving surgery, which consists of removal of the tumor and a small amount of tissue, followed by radiation, or removal of the entire breast.
Importantly, Petty and co-author Alexandra Kraft, now a graduate student in the department of human genetics, found that the abundance of these key proteins didn’t predict a cancer’s aggressiveness, while their location near the cell membrane did.
A critical suggestion that influenced the direction of the research came from an unlikely source — Petty’s son, a cyber-security expert at a credit monitoring company, who suggested the researchers could use machine learning techniques to teach computers to make increasingly refined connections between subtle features in high-resolution images taken of patient tissue samples and the outcomes those patients experienced.
“The computer is looking for patterns in the images that humans can’t readily discern, from the level of individual pixels up to an entire image of a million pixels,” says Petty, a member of the U-M Rogel Cancer Center.
The researchers started with samples from 70 patients with stage 0 breast cancer who had undergone a mastectomy, and for whom there were at least 10 additional years of medical records available. Twenty of the 70 patients experienced a recurrence of their cancer, while 50 did not.
These tissue samples were stained so that the proteins of interest would fluoresce under the microscope. Then, using a state-of-the-art computer vision application, the scientists created a library of microscope images that were associated either with aggressive or non-aggressive DCIS, based on what had happened to that patient.