NIH grant has funding from American Recovery and Reinvestment Act; collaborators include Penn Medicine, Siemens
Rutgers University and two collaborators have received a $3.4 million research grant to develop tools aimed at improving the identification of prostate cancer using MRI, or magnetic resonance imaging.
The five-year grant, with funding in the first two years from the American Recovery and Reinvestment Act (ARRA) of 2009, was awarded by the National Institutes of Health under an initiative to promote industry and academic partnerships. Rutgers is working with Penn Medicine and Siemens on this pioneering research.
Recent studies by Rutgers and Penn researchers show that powerful, high-resolution MRI technology can reveal cancerous tissue in prostate glands and pinpoint where the tissue is concentrated. Radiologists, however, do not always know whether unusual-looking visual features indicate cancerous growth or benign variations.
"At this time, it's often just a prediction or an educated guess," said Anant Madabhushi, assistant professor of biomedical engineering and Rutgers' principal investigator on the grant. "Before MRI technology is ready for widespread clinical use, the medical profession will have to be confident that it can make readings accurately and consistently."
Each year, there are more than 27,000 deaths from prostate cancer in the United States and 190,000 new cases diagnosed. Diagnosis is based on PSA levels in blood, physical examination and needle biopsies, because current imaging techniques don't distinguish cancerous tissue. MRI has the potential to offer a diagnosis non-invasively and, along with other information, help physicians customize the most effective and least debilitating treatment plan.
Under the research grant, Penn researchers will make magnetic resonance images of prostate glands in cancer patients and prepare tissue samples from those same glands after they are surgically removed in the course of treatment. Rutgers and Siemens researchers will then develop computerized tools that align MRI views with digitized images of tissue slices. These tools will allow investigators to better identify MRI features that reveal cancerous tissue and develop pattern recognition software that will help radiologists make accurate and timely diagnoses.