A Novel Knowledge Framework for Discovery, Development, and Validation of Quantitative Imaging Biomarkers (QIB)

July 11, 2016

Quantitative Imaging Biomarkers are of interest to enable personalized medicine as well as for their potential to accelerate drug development, but their use is hampered by inadequate and inconsistent methodology. A powerful informatics framework developed by Elucid Bioimaging enables prospective as well as retrospective use cases for development, optimization, validation, and discovery of quantitative imaging phenotype data. (http://elucidbio.com/blog/)

A Novel Knowledge Framework for Discovery, Development, and Validation of Quantitative Imaging Biomarkers (QIB)

Quantitative Imaging Biomarkers are of interest to enable personalized medicine as well as for their potential to accelerate drug development, but their use is hampered by inadequate and inconsistent methodology which hampers careful characterization. Before QIB can be recommended in practice guidelines or used in clinical trials, the biomarker must be represented consistently across imaging devices and clinical centers. This representation must support advanced statistical techniques, controlled vocabularies, and service architecture for processing potentially large numbers of images as well as other associated clinical and genomic data. Elucid has developed a sophisticated semantic infrastructure as part of its Computer-Aided-Phenotyping platform which is available to clients through customized system development services. A subset of capability is also available in an open-source version through Elucid’s QI-Bench program used to support collaborative projects. Image data can be stored, queried, and retrieved via SPARQL-endpoints to allow both prospective development and validation as well as retrospective discovery of QIBs of interest. Such QIBs can then be used in the conduct of clinical trials, in translational medicine research, and in the clinical care of patients. (http://www.ncbi.nlm.nih.gov/pubmed/23546775)