New artificial intelligence tool may help personalize ovarian cancer treatment from day one
Researchers from the Masonic Cancer Center, University of Minnesota in collaboration with Emory University and the Georgia Institute of Technology have developed a new artificial intelligence (AI) biomarker tool that may help predict how ovarian cancer patients will respond to treatment at the time of diagnosis. The findings were published today in the British Journal of Cancer Reports.
The tool utilizes the measurement of shapes and patterns of cells on medical lab slides, which doctors already use to diagnose cancer. This approach could help doctors determine whether and which chemotherapy is likely to work for each patient, and importantly, does not require any additional procedure or cost.
By identifying which patients are more likely to respond to a specific chemotherapy early on, the tool could help maximize therapeutic benefits, spare patients from unnecessary side effects, and allow them to explore clinical trials sooner.
“From the time of diagnosis, this AI tool can help identify which treatments are most likely to benefit ovarian cancer patients,” said Martina Bazzaro, PhD, an associate professor at the University of Minnesota Medical School and a Masonic Cancer Center researcher. “It can reduce uncertainty early in the treatment planning process, avoid therapies that are unlikely to help, and lessen both the emotional and financial burden on patients and the health care system. We are now moving toward a clinical trial to integrate this tool into clinical care, harnessing new and advanced AI software to guide treatment decisions and improve outcomes, while supporting more efficient and cost-effective healthcare delivery."
"The use of AI in medicine, and in oncology in particular, is evolving rapidly,” said Emil Lou, MD, a professor at the University of Minnesota Medical School and gastrointestinal oncologist with the Masonic Cancer Center and M Health Fairview. “We created an AI tool that measures how much of a tumor is made of cancer cells versus surrounding tissue. This measurement is very helpful when someone is first diagnosed, and our AI was just as accurate as expert doctors. This is a promising example of how we can move things forward into the clinic more quickly than ever before."
The study team aims to move the AI tool into clinical trials within the next six months to further assess its real-world impact, as well as further integrate the technology into health systems so that results can be delivered automatically to care teams.
This research was funded by the Minnesota Ovarian Cancer Alliance, the Randy Shaver Community Fund, and the American Cancer Society.
A version of this article was first published by Ezra Xiong for the U of M Medical School.