New I3LUNG project to fund AI tools for improving treatment and outcomes for lung cancer patients

The University of Chicago Medicine has joined the I3LUNG project, a research initiative funded by a five-year, €10M grant from the European Union to develop a decision-making tool for creating individually tailored lung cancer treatment plans. The project will use artificial intelligence (AI) software and machine learning to analyze a wide range of information from clinical data, radiology images, and biological characteristics of tumors.

“This highly innovative project has the goal of using machine learning and artificial intelligence to predict the outcomes of immunotherapy, which is something we are not currently capable of doing,” said Marina Garassino, MD, Professor of Medicine and the leader of the University of Chicago Medicine’s arm of the project. “Right now, we know that less than 30% of patients will have a great response to immunotherapy, but we are not able to predict who those patients are. Improving our ability to make these predictions can help us better tailor treatments for individual patients.”

The data will come from 2,000 patients across multiple research centers in the United States, Italy, Germany, Greece, Spain and Israel. Two hundred new patients will be enrolled in a prospective study to gather new biological data from tumor genetics, the immune system, digital pathology, gut microbiome, imaging and other genetic and molecular analyses. In parallel, researchers will also conduct a psychological study to integrate patient experiences and preferences into decision-making tools.

The team at UChicago Medicine will apply the expertise of researchers from the labs of Garassino and Alexander Pearson, MD, PhD, a longtime machine learning scientist and clinician, to help process the original set of patient data and prepare it for use by other researchers. The data can in turn be used to develop and test new algorithms for predicting immunotherapy outcomes.

“Here at UChicago Medicine, we have a lot of expertise in processing these types of data,” said Pearson, Assistant Professor of Medicine. “Finding the most relevant signal in messy genomic data, or finding the patterns in how a tumor grows, or the features in a CT scan that correspond to good or bad outcomes when a cancer is being treated, all of those domains have been led by us. So, when Dr. Garassino brought her global lung cancer expertise to the organization, it was clear that we could work together to leverage these emerging methods.”

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Originally reported in The Forefront, 8/30/2022