CHICAGO – The way diagnostic imaging is practiced today has changed little in the last 100 years, according to Eliot L. Siegel, MD, professor and vice-chair of the University of Maryland School of Medicine’s Department of Diagnostic Radiology and Nuclear Medicine.

The radiology field, however, is on the cusp of taking advantage of and introducing innovative technology into diagnostic imaging, he said.

Siegel, who is also chief of imaging for the Veterans Affairs Maryland Healthcare System, is part of a panel that will be presenting at RSNA 2010 on Imaging for Clinical Trials and Research Networks (Informatics: Advances), RC130, on November 28, Sunday, 2:00 PM - 3:30 PM CT.
 
While C. Carl Jaffe, MD, will focus on Imaging in Clinical Trials and Krishna Juluru, MD, will present on Integrating Clinical and Research Data, Siegel will discuss the caBIG Imaging Workspace.

Initiated by the National Cancer Institute, the Cancer Biomedical Informatics Grid (caBIG) is a network of infrastructure, tools and ideas that allow providers and researchers to collect, analyze and share data and information that will lead to faster research discoveries and improved patient outcomes. The grid can be leveraged in other areas and for other means, such as connecting to share information in electronic health records in a standardized fashion, Siegel said.

Challenges within IT departments and barriers such as firewalls, however, have prevented many sites from being able to connect to the grid. To address this issue, caBIG has moved away from a grid infrastructure toward a service-oriented architecture.

Siegel encountered other challenges when he began work on the Cancer Imaging Program about five years ago. There were no standards for structuring, storing, searching and sharing images. Health IT workstations were proprietary to the vendors, which prevented the sharing of images among multiple vendor workstations.

The caBIG tool or new standard, Annotation Imaging Markup (AIM), has enabled the addition of information and knowledge into a system workstation, which allowed clinicians to not only share images but store machine-searchable information. “For the first time, we have to capability of being able to search through diagnostic images,” Siegel said.

When a patient is presented with a brain tumor through an MRI, clinicians can use a standardized template to look at the case and interpret it versus certain criteria. Clinicians can search through the large database to find patients who have similar characteristics, such as their genomics and proteomics, and learn how these patients responded to a certain type of therapy or what the life expectancy is for different situations.

AIM is currently in pilots with vendors, Siegel said. The industry must now determine how to make the “next leap” of making stored information easily shared and easily searchable.

The work being done under caBIG is leading to personalized medicine, Siegel said. By being able to combine all of the previous patient’s data, clinicians can tailor their patients’ treatment. “This is a really a promising potential in how we practice medical imaging,” he said.