Abstract

The ability to more accurately predict and prevent disease has the potential to transform clinical practice by improving response to specific treatment regimens and decreasing morbidity and mortality. Part of what limits the accuracy to which we can predict and prevent disease results from our limited understanding of the relationship between clinical presentation and disease progression [1]. Although vast amounts of data are collected at clinical presentation, ranging from macro-scale Magnetic Resonance Imaging (MRI) scans, to micro-scale pathology slides, to nano-scale proteins and genes, there are challenges associated with analyzing, combining, and correlating these data to make diagnostic, prognostic, and theranostic predictions [2–4]. Computerized image analysis and data integration methods have the potential to improve our understanding of the relationship between these heterogeneous multi-format, multi-scale data to better predict disease outcomes and treatment responses. Computer-based Image Analysis Advances in imaging hardware and computational processing have catalyzed the growth of digital imaging and computer-based image analysis in pathology. Digitization of entire glass