The requirements for quantitative imaging, particularly as applied to predicting and/or measuring response to therapy, are extensively covered in a special issue of this journal and will not be addressed in this report due to space limitations [6], [64] and [65]. Databases linking imaging with molecular data are http://www.selleckchem.com/products/ABT-888.html just beginning to emerge at a slow pace due to the high cost of large-scale imaging studies and lack of standards for interpretation. To conduct meaningful imaging genomic correlation studies, big scale (Big-N) imaging studies will be needed, which will require data acquisition, aggregation, management, and analysis methodologies,

as well as technologies quite different from those used in research see more today. Achieving such large-scale aggregation will require new incentive structures, computing infrastructure, security policies, and analysis methods. In addition to the NIH supported TCGA-TCIA data archive, there are three other examples of note for platforms being built for the purpose of integrating disparate data. They include (a) the Information Sciences in Imaging at Stanford (ISIS) group, (b) the I-SPY TRIAL, and (c) the Georgetown Database of Cancer (G-DOC). ISIS is developing several tools to collect and integrate annotated imaging, clinical, and molecular data through novel

computational models that help identify relationships within the data [66]. The I-SPY TRIAL breast cancer data collection was a collaboration of ACRIN, Cancer and Leukemia Group B (CALGB), and NCI’s Specialized Programs of Research Excellence (SPORE). The study aimed to identify molecular markers of response to conventional neoadjuvant chemotherapy and imaging markers associated with response to therapy [67], posing new challenges for data archiving. G-DOC, developed

at Georgetown University, deals with five types of -omics data integrated with clinical metadata and patient outcome data. It offers a model for how to store, integrate, and visualize multiple disparate data types. A major challenge in analyzing the potentially enormous datasets, however, is to design them to be useful for the end user—the translational researcher who is either developing clinical decision support systems or implementing Etofibrate these methods into clinical trials. The generation and computer visualization of reports from such data-integrating platforms are critically needed to reduce the multi-dimensional data into graphical representations that can be more readily interpreted. Thus, it is clear that more consensus approaches are potentially needed to develop interoperable web-based data archives using common standards that are initially being promoted by the NCI-funded TCIA-TCGA database. Cloud-based computing and resources present new opportunities for supporting imaging and genomics correlation research.