caBIG Imaging Presentation: Novartis and RSNA Conference

1. caBIG Imaging Presentation:Novartis and RSNA Conference January 7, 2011

2. Table of Contents • Novartis – RSNA Pharma/NCI Meeting: Pharma Image Exchange and caGrid Experiences and Next Steps • Imaging Workspace Applications • Examples of Imaging Supporting Scientific Discovery • caBIG® Imaging in the Real World • Thank You

3. Pharma NeedsMulticenter Clinical Imaging Trials • Federated archives for large image sets • Seamless image exchange with CROs, academic partners • Seamless integration across partners on process level (QC, on-demand image viewing, ..)

4. Strategy • One solution for all partners • Service Oriented Architecture • Open Source Reference Implementation • Maximize re-use of existing tools

5. Tactics • Novartis started with an example (ImagEDC on caGrid) • Proof of concept: successful! • Expand together with small group of Pharma peers (e.g., project with shared objectives) • ..using caGrid infrastructure?

6. The example: ImagEDC

7. Lessons Learned • Great software, packaging, support, documentation, community. „It just works“ • Raised Interest between Pharma peers and outside (CROs, Bio-IT world) • Interest to scale up: use productively, and expand scope • To go productive using caGrid, need to solve a few open questions

8. Desired Meeting Outcome • Identify solution for SW maintenance (bugs, small changes): Who, Service Levels? GxP • Identify solution for Production Grid hosting: GxP, Service, Support • Identify path to evolve features:feedback cycle core SW

9. National Biomedical Imaging Archive (NBIA)and Supporting Technologies • Expanded to include multiple additional types of image collections with role based security to share with public or a selected group or to support ongoing clinical trials or other reader studies • Open source and free • Meant to be “federated” to create virtual database across multiple instances of NCIA software Supporting Technologies: • AIM Model and Data Service –Structured reporting • XIP and AVT – Provide software tools for • stakeholders such as PI’s to build their own custom platforms for individual studies or clinical practice • Middleware and Virtual PACS – Support connectivity of NBIA to other commercial and non-commercial imaging workstations • caBIG versions of Clear Canvas and Osirix • workstations: Provide free visualization • combatable with NBIA, AIM, and caGrid

10. caBIG Imaging Presentation:RSNA Conference

11. The National Biomedical Imaging Archive (NBIA) • The NBIA is a searchable, national repository supported by NCI CBIIT that integrates in vivo cancer images with clinical and genomic data. NBIA provides the cancer research community, industry, and academia with public access to DICOM images, image markup, annotations, and rich metadata. • NBIA is available as a downloadable package for any institution to set up their own local or national archive. • This can be set up as web accessible, API accessible and/or grid accessible.

12. Annotations and Imaging Markup Developer and Data Service (AIM)/ Imaging Core Middleware/Virtual PACS • In addition, we will be demonstrating a data service that has been developed to enable hosting of a service that functions as a repository for AIM. • Imaging Core Middleware/ Virtual PACS • Imaging Middleware is a set of tools, libraries, and applications designed to support multiple operations including DICOM, picture archiving and communication systems (PACS) interoperability with the caGrid, remote client operations on a PACS server, and secure bulk data transfer of DICOM objects in a grid based environment • Annotations and Imaging Markup Developer and Data Service (AIM) • AIM provides a standard for medical image annotation and markup for images used in the research space, and in particular, the image based cancer clinical trial. • It is notable that there is no existing standard for radiology annotation and markup and the caBIG® program is working with almost every standards body such as DICOM to elicit consensus regarding using AIM as the accepted standard for radiology annotation and markup, and is positioned to extend AIM to digital pathology.

13. eXtensible Imaging Platform (XIP)/Algorithm Validation Tools (AVT) • Algorithm Validation Tools (AVT) • AVT provides a set of tools that users can apply to a collection of images in order to generate measurements that reflect “truth” and allows determination of the consistency of any measurement method for detecting change. • AVT can be extended to provide the types of imaging data adjudication sought by anyone attempting to achieve high validity of data in a multi-institutional study/trial. • eXtensible Imaging Platform (XIP) • XIP is an easily extensible open source platform that facilitates easier access to specific post-processing applications at multiple sites. The XIP enables cancer researchers to easily create complex data analysis programs (e.g. lesion change detection) targeted at specific investigations. • Employing the DICOM WG-23 Application Hosting interfaces, XIP Applications can be distributed to a variety of installations, thus providing some consistency in data collection for clinical trials. The XIP tools can be employed to create many other types of applications.

14. caBIG® Clinical Trials Tools Suite(CCTS) • In the past year, the caBIG® Imaging and Clinical Trials programs have begun working closely together in the interest of enabling the user to leverage both these tool sets simultaneously for true “caBIG® powered” free, open source, and interoperable clinical trials management support with imaging functionality. • The CCTS is a modular enterprise clinical trials management system designed to facilitate clinical workflows and data sharing in single and multi-site settings. • Being designed primarily for use in trial sites, the suite is comprised of a collection of interoperable modules covering a broad range of key areas in clinical trials management. • These include: study participant registration (C3PR), patient scheduling (PSC), adverse event management and reporting (caAERS), exchange of clinical laboratory data and other clinical data (caBIG® IntegrationHub and LabViewer), person, and organization management, (NCI Enterprise Services) and integration with clinical data management systems (Connectors).

15. caBIG® Imaging In The Real World • Today, there are multiple instances of caBIG® Imaging technology being used in a “production way” to support scientific discovery in diverse settings including Government, academia, and industry with cancer and other diseases. Attendees will have an opportunity to learn about how caBIG® is supporting engagements such as NIAMS’ Osteoarthritis Initiative (OAI), NCI/NHGRI’S The Cancer Genome Atlas (TCGA), and NHLBI’s CardioVascular Research Grid among others. • The TCGA Radiology project features reference implementations of AIM (including the AIM data service), NBIA, and a free and open radiology workstation that connects to NBIA and AIM. • Since standards are a critical part to the success of any endeavor, there will be an opportunity learn about caBIG®’s standards, activities, and the program’s engagement with many of the world’s most important standards organizations.

16. Division of Cancer Prevention’s National Lung Screening Trial (NLST) • NBIA is providing centralized storage and review of imaging data for 50,000 patients at 30 participating sites. The principal investor at Washington University, Dr. David Gierada, is able to use NBIA at their site to support all of the participating PIs. • This enables principal investigators, such as Dr. Eliot Fishman from Johns Hopkins, to submit and retrieve the consortium’s imaging data in the same fashion as all of the other investigators. • The NSLT closes out in 2011. Published findings are expected to be forthcoming. Purpose: NLST is comparing two ways of detecting lung cancer: spiral computed tomography (CT) and standard chest X-ray. Both chest X-rays and spiral CT scans have been used to find lung cancer early. So far, neither chest X-rays nor spiral CT scans has been shown to reduce a person's chance of dying from lung cancer. This study will aim to show if either test is better at reducing deaths from this disease.

17. Thank you Adam Flanders CBITT Government Sponsors:  • Ed Helton • Robert Shirley • MerviHeiskanen • Juli Klemm In collaboration with: • NCI Cancer Imaging Program • Carl Jaffe • John Freyman • Justin Kirby Supported by:  • 5AM • Booz Allen Hamilton • Buckler Biomedical, LLC. • Capability Plus Solutions • ClearCanvas, Inc. • Emory University • Northwestern University • SAIC • Stanford University • Thomas Jefferson University • University of Maryland • University of Virginia