Additional applications
“We were not surprised that AI was able to predict clinical outcomes, but we were surprised that it was able to do so with such high accuracy and precision,” said Dr. Walsh. “We were also surprised to learn that our predictions were equally accurate using only six-plex data, compared with 35-plex. This hinted to us that we could potentially scale down the number of markers to a practical number that would be amenable to technologies available in routine pathology labs.”
Dr. Walsh and colleagues are now validating the predictive tool using a lower-plex technology. In addition, they are investigating the immune landscapes of primary and metastatic brain tumors.
“This study is important, as it helps us to understand and appreciate the biological and mechanistic factors that may influence treatment outcomes. Our standard clinical predictors for predicting risk of recurrence and probability of response to therapy are not optimal,” Yee Ung, MD, an associate professor of radiation oncology at Sunnybrook Health Sciences Centre, Toronto, said in an interview.
Dr. Ung, who wasn’t involved with this study, has researched noninvasive hypoxia imaging and targeting in lung cancer. Ideally, he said, future studies should incorporate the use of noninvasive imaging predictive factors, in addition to the tumor immune microenvironment and clinical factors, to predict outcomes and provide personalized treatment.
“As we begin to investigate and understand more about cancer biology down to the cellular and molecular level, we need to strategically use AI methodologies in the processing and analysis of data,” he said.
The study was supported by the McGill Interdisciplinary Initiative in Infection and Immunity, the Brain Tumour Funders’ Collaborative, the Canadian Institutes of Health Research, and the Canadian Foundation for Innovation. Dr. Walsh and Dr. Ung have disclosed no relevant financial relationships.
A version of this article first appeared on Medscape.com.
