Daniel L. Rubin MD, MS, Presenter: Nothing to Disclose

Adam Eugene Flanders MD, Presenter: Nothing to Disclose

Elizabeth S. Burnside MD, MPH, Presenter: Stockholder, Cellectar Biosciences, Inc Stockholder, NeuWave Medical Inc

Raghunandan Vikram MBBS, FRCR, Presenter: Nothing to Disclose

Ross Warren Filice MD, Presenter: Nothing to Disclose

1) Understand the new Big Data paradigm of radiology and emerging scientific areas in breast, brain and renal imaging that are leveraging this to drive new radiological insights. 2) Understand the Annotation and Image Markup (AIM) standard for image metadata and tools that use it to enable radiologists to make their observations explicit and machine-accessible. 3) Learn how AIM can support radiology clinical workflow, help referring physicians, be used outside the clinical environment, support research objectives, support regulatory objectives, improve regulatory science, facilitate new biomarker development and validation, help with meta-analyses across disparate trial data.

AIM brings tremendous benefits to clinical radiology by standardizing annotation documentation, allowing more specific and customizable searching of annotations, enabling objective and computable analysis of measurements, and allowing clinicians to more easily consume measurement data. But, AIM also has many important use cases outside of the clinical setting. Clinical trials, many of which include imaging and measurement data, are the foundation of medical product approval and regulation. Regulators must be able to easily review increasingly large amounts of data to make an educated decision regarding a new medical product. By submitting imaging measurement data in a standard format, such as AIM, regulators could more easily review this data. Additionally, as meta-analysis of data across multiple trials becomes more and more useful - both in the regulatory as well as the research settings - documenting measurements in standard formats better facilitates these analyses. Finally, standardizing measurement data and aggregating multiple datasets could better allow for new imaging biomarker discovery and validation that might lead to better response criteria for use both clinically and in the approval of new medical products.

http://media.rsna.org/media/abstract/2014/14001871/RCA32 sec.pdf

Rubin, D, Flanders, A, Burnside, E, Vikram, R, Filice, R, Tapping into Big Data in Images: Annotation and Image Markup, Tools, and Applications (Hands-on).  Radiological Society of North America 2014 Scientific Assembly and Annual Meeting, - ,Chicago IL. http://archive.rsna.org/2014/14001871.html