CHAPEL HILL, NC – A new collaboration between melanoma researchers in the School of Medicine at the University of North Carolina at Chapel Hill, the Renaissance Computing Institute (RENCI), and researchers from the departments of computer science, epidemiology, biostatistics, and statistics and operations research at UNC Chapel Hill aims to use image analysis techniques to aid doctors in the fight against melanoma, the most serious form of skin cancer.
Over time, the work could help doctors diagnose the seriousness of melanoma cases more quickly and with better accuracy. In addition, this work could lead to new tools for outcome prediction, thus assisting doctors in determining best treatment approaches.
Nancy E. Thomas, MD, PhD, an associate professor of dermatology School of Medicine’s dermatology and a member of UNC Lineberger Comprehensive Cancer Center, will lead the research project, called Image Analysis to Assess Melanoma Heterogeneity. The project will examine imagery from more than 1,300 melanoma patients worldwide, including 214 from North Carolina.
A $190,000 award from the University Cancer Research Fund will support the project. The North Carolina General Assembly created the fund in 2007 to accelerate the battle against cancer at UNC Chapel Hill’s School of Medicine and its Lineberger Comprehensive Cancer Center.
RENCI specialists in bioinformatics, image analysis and data mining will work with Dr. Thomas and melanoma researchers at UNC-Chapel Hill to develop algorithms or computational methods that can identify cancerous and healthy tissue in high-resolution images. Once cells in the images are identified as cancerous or healthy, the researchers will collect information on physical details, such as cell size, shape, and color of melanoma cells. All these details will be used to develop evidence-based models of melanoma cell descriptions.
In addition, advanced computational methods will be used to identify the physical characteristics of the melanoma cells–features uncovered through image analysis–which are associated with tumor classification, based on known information about somatic genetic mutations in melanomas, and survival.
“What’s exciting about our approach is that we may be able to uncover relationships between the physical qualities of melanoma cells, tumor genotype, and patient outcomes” said Charles Schmitt, manager of biological science programs at RENCI and a co-investigator on the project. “It’s the kind of work that can only be done by bringing together expertise in multiple areas.”
Nearly 60,000 new cases of melanoma were diagnosed in the U.S. in 2007, according to estimates from the American Cancer Society, and more than 8,000 patients died of the disease. Melanoma cases continue to increase and prognosis is poor for patients with advanced melanoma tumors.
“The objective of this proposal is to utilize image analysis to uncover associations between the physical characteristics of melanoma, somatic mutations in melanoma, and survival” said Thomas. “But ultimately, our long-term goal is to help patients. We want to use image analysis to improve melanoma classification, which we would expect to improve diagnosis and guide treatment recommendations.”