An approach that combines digital imaging with genomic sequencing to match lung cancer patients with treatments could result in improved survival rates, according to researchers at Ohio State University, Columbus (OSU), and University of Kentucky, Lexington (UK).

“Lung cancer treatment choice hangs critically on how a pathologist classifies certain cancer cell traits – the phenotype. But this interpretation can be highly subjective,” Kun Huang, PhD, associate professor of biomedical informatics at OSU’s College of Medicine, said in a release. “We have genomic data that tells us what treatments might work best for a specific person, but that doesn’t tell us how aggressive the cancer type may be. So clinicians today are making decisions on the best available data, but it’s an incomplete set of information.”

The model aims to use pathology information with knowledge of a person’s genome to help clinicians select the best treatment. Initially, Huang and his research partner, Lin Yang, PhD, assistant professor at UK, will create the model using a repository of tissue from more than 4,000 lung cancer patients from Appalachia and genomic data from the Cancer Genome Atlas, a National Institutes of Health database.

In the fall of 2012, the team received a $50,000 Community Engagement Joint Pilot Award Collaboration Grant from the OSU Center for Clinical and Translation Science (CCTS) and the UK CCTS. They hope to start sharing results in one year.