Using Artificial Intelligence To Prevent Harm Caused By Immu
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Lung cancer patients were previously divided into two broad categories: those who would benefit from immunotherapy, and those who likely would not. Researchers using artificial intelligence (AI) to analyze simple tissue scans have discovered biomarks that could tell doctors which lung cancer patients might actually get worse from immunotherapy.

A third category of patient called hyper-progressors who would actually be harmed by immunotherapy has begun to emerge. "This is a significant subset of patients who should potentially avoid immunotherapy entirely," the researcher says.

In this and previous research, scientists essentially teach computers to seek and identify patterns in CT scans taken when lung cancer is first diagnosed to reveal information that could have been useful if known before treatment. And while many cancer patients have benefited from immunotherapy, researchers are seeking a better way to identify who would most likely respond to those treatments.

"This is an important finding because it shows that radiomic patterns from routine CT scans are able to discern three kinds of response in lung cancer patients undergoing immunotherapy treatment--responders, non-responders, and the hyper-progressors," said Madabhushi, senior author of the study.

"There are currently no validated biomarkers to distinguish this subset of high-risk patients that not only don't benefit from immunotherapy but may in fact develop rapid acceleration of disease on treatment," said Pradnya Patil, MD, FACP, associate staff at Taussig Cancer Institute, Cleveland Clinic, and study author.

"Analysis of radiomic features on pre-treatment routinely performed scans could provide a non-invasive means to identify these patients," Patil said. "This could prove to be an invaluable tool for treating clinicians while determining optimal systemic therapy for their patients with advanced non-small-cell lung cancer," added Patil.