Researchers at University of Washington and University of California, Los Angeles, have developed an Artificial Intelligence system that could help pathologists read biopsies more accurately, and lead to better detection and diagnosis of breast cancer.
Doctors examine images of breast tissue biopsies to diagnose breast cancer. But the differences between cancerous and benign images can be difficult for the human eye to classify. This new algorithm helps interpret them — and it does so nearly as accurately or better than an experienced pathologist, depending on the task. The research team published its results Aug. 9 in the journal JAMA Network Open.
“This work concentrated on how to capture the characteristics of the different diagnostic classes by analyzing the pattern of the tissue classes surrounding the ducts in whole-slide images of breast biopsies,” said co-author Linda Shapiro, a professor in both the UW’s Paul G. Allen School of Computer Science & Engineering and the UW’s electrical and computer engineering department. “My doctoral student, Ezgi Mercan, invented a novel descriptor called the structure feature that was able to represent these patterns in a compact way for use in machine learning.”
In 2015, a study from the UW School of Medicine found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year. The study revealed that diagnostic errors occurred for about one out of every six women who had a noninvasive type of breast cancer called “ductal carcinoma in situ.” In addition, incorrect diagnoses were given in about half of the biopsy cases with abnormal cells that are associated with a higher risk for breast cancer — a condition called breast atypia.
“Medical images of breast biopsies contain a great deal of complex data, and interpreting them can be very subjective,” said co-author Dr. Joann Elmore, a professor of medicine at the David Geffen School of Medicine at UCLA, who was previously a professor of internal medicine at the UW School of Medicine. “Distinguishing breast atypia from ductal carcinoma in situ is important clinically, but very challenging for pathologists.
