March 22, 2022 – The University of Oklahoma has received funding from the National Institutes of Health to establish the Oklahoma Center of Medical Imaging for Translational Cancer Research, a collaboration between the Gallogly College of Engineering on the OU Norman campus and OU Health Stephenson Cancer Center in Oklahoma City.
The award from the Centers of Biomedical Research Excellence (COBRE) program of the NIH is expected to provide more than $11.3 million over a five-year Phase 1 period, with the opportunity to compete for renewal for up to three phases. The first phase supports the center’s establishment to galvanize multidisciplinary biomedical research through equipment and facilities support to junior faculty. This is the second COBRE center on OU’s Norman campus, joining the Oklahoma COBRE in Structural Biology.
Medical imaging is an essential tool to help doctors and scientists assess the size and scope of a tumor that will be effectively removed by surgery, as well as the rate at which tumors shrink in response to medical interventions such as chemotherapy or radiation therapy. OU researchers are investigating multiple avenues to help improve medical imaging use in cancer detection, diagnosis and treatment.
While imaging is the most common testing method used by clinicians, it can be difficult to read these images based on the level of variations among tumors, cancers, and most diseases. Due to this high variability, the diagnostic results could be different for each clinician or hospital, which can reduce treatment efficacy if the proper method isn’t used.
To counter this challenge, the center’s researchers are developing quantitative imaging markers to provide an objective measure or index that can reduce subjectivity and improve consistency for medical image diagnosis using two primary types of research approaches.
The first approach is to develop new investigative cutting-edge imaging modalities to expand the ability of doctors to see or detect more detailed tumor internal structures such as using advanced optical imaging modalities and technology.
The second is to explore and extract more effective image features from the existing clinical imaging modalities – like CT, MRI and X-ray images – and then using artificial intelligence or machine learning models to develop new quantitative imaging markers to help reduce subjectivity and variability of cancer diagnosis and predicting cancer prognosis.