MO-FG-207B-02: Breast.

State-of-the-Art in Radiomics in Radiology and Radiation Oncology Radiomics is the science of converting medical images into mineable data, data that are descriptive of "phenotypes," which may provide diagnostic, prognostic, or therapeutic information. Genomics is the science of sequencing and analyzing the function and structure of genomes; the complete set of DNA in a single cell of an organism. In turn, imaging genomics (or radiogenomics) is concerned with the correlation between image-based features, as determined by radiomics, and gene expression, as determined by genomics. Imaging genomics arose from decades of work in at least three key areas: 1) sequencing of the human genome, 2) quantitative imaging, computer-aided diagnosis, therapy prognostics, and assessment of therapy response in preclinical and clinical research and practice; and 3) data science, including the burgeoning areas of genomics, preclinical and population-based disease modeling, individualized medicine, and big data. Imaging genomics may answer important questions in medicine by correlating validated, quantitative image phenotypes (through validated imaging biomarkers) with clinical data, histopathologic data, molecular classifications, genomic assays, and treatment outcomes. This approach could address some of the greatest health burdens, including cancer, cardiac disease, and arthritis. Participants will discuss the state-of-the-art of radiomics across multiple disease sites and modalities. Aspects of the presentations will include how to improve the quality of image-based phenotypes of normal and diseased tissue, how to better determine the relationships between these phenotypes and the underlying biology associated with the images, and how to create predictive models using the image-based phenotypes. Topics will also include: 1) creating, archiving, curating and sharing ultra-large datasets ("big data"); 2) standardizing image acquisition and processing methods; 3) standardizing and validating phenotype extraction methods and classifier designs; and 4) using high-throughput, robust, and validated phenotyping systems. H. Aerts, NIH; NCIH. Li, This research was funded in part by the University of Chicago Dean Bridge Fund,and by NCI U24-CA143848-05, P50- CA58223 Breast SPORE program. Hui Li received royalties from Hologic.W. Lu, This work was supported in part by the National Cancer Institute Grants R01CA172638.