Childhood cancer is a relatively rare disease diagnosed in over 16,000 U.S. children and adolescents (ages 0 – 19) each year. While 84% of children with cancer survive 5 years or more, cancer remains the second leading cause of death in children after accidents. Molecular variations make all childhood cancers extraordinarily rare and difficult to study. The Childhood Cancer Data Initiative (CCDI) of the National Cancer Institute recognizes the critical need to collect and analyze and share data to address this understudied cancer burden. Innovations in informatics methods and data science are clearly needed to assimilate new data sources, characterize the burden in the population and empower clinicians and scientists to overcome the complexities of the emerging multi-omics data paradigm. This seminar will present approaches to enhance the childhood cancer data ecosystem through innovations in population-based cancer surveillance, electronic pathology reporting, natural language processing, virtual tissue repositories and targeted molecular data acquisition. Strategies for identifying potential childhood cancer disparities and national childhood cancer data initiatives will also be presented.
Eric B. Durbin, DrPH, MS, is an Assistant Professor in the Division of Biomedical Informatics at the University of Kentucky (UK) College of Medicine. He serves as the Director of the SEER Kentucky Cancer Registry (KCR) and the Director of the Cancer Research Informatics Shared Resource Facility at the NCI-designated UK Markey Cancer Center. He has over 30 years of experience in population-based cancer surveillance and informatics support for basic, clinical and translational cancer research. His research interests include precision cancer surveillance, pathology informatics, machine learning methods and cancer epidemiology. Dr. Durbin’s current research is focused on the integration of multi–omics data to support decision-making in precision medicine and cancer prevention and control, informatics methods to support population-based pediatric and young adult cancer research and the development of machine learning methods to derive clinical biomarkers from narrative documents and digital slide images.
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