The cabig Cancer Genome Atlas Radiology Project. Eliot Siegel, M.D. University of Maryland School of Medicine Department of Diagnostic Radiology

The cabig Cancer Genome Atlas Radiology Project Eliot Siegel, M.D. University of Maryland School of Medicine Department of Diagnostic Radiology

Introduction One of the major original goals of cabig was to determine out how to create a system that would enable extraction of data for research or clinical decision support that would: Allow access to a variety of types and sources of data including genomic, proteomic, clinical, lab, demographic, and diagnostic imaging Take advantage of analytic potential of grid computing to combine and cross-reference these for analysis for research and clinical care

The cabig Imaging workspace has worked to build basic tools toward this goal and the TCGA imaging workspace project represents an example of the potential for cabig to have a major impact on the way in which data are shared, research conducted, and patient care is provided

Introduction to the cabig in Vivo Imaging Workspace cabig in vivo Imaging workspace established April 2005 a little more than a year after the establishment of the other cabig workspaces NCI funded effort by far the biggest and most productive effort in imaging informatics today Subject matter experts from around country with representation from major Universities, informatics experts, industry, NCI

Review of Relevant Workspace Projects XIP, AIM, Middleware, NBIA

Rapid application development environment for diagnostic imaging tasks that researchers and others use to create targeted workflows customized for specific projects

XIP Class Library Auto Conversion Tool ITK VTK XIP LIB... XIP Application Builder Medical Imaging Workstation XIP Application XIP Modules Host Independent XIP Host Adapter Web-based Application XIP Host (Can be replaced with any DICOM WG23- compatible Host) DICOM, HL7, and othercagrid Services via services per IHE ProfilesImaging Middleware WG 23 WG 23 WG 23 WG 23 Host-Specific Plug-in Libraries Distribute Standalone Application

Annotations and Image Markup (AIM) Being Adopted by Increasing Number of Research and Commercial Systems Represents a standard means of adding information/knowledge to an image in a research or clinical environment to allow easy and automated search for image content

Imaging Middleware (including GridCAD and Virtual PACS) Grid computing has received surprisingly little attention. One application has been to allow multiple computers to work in parallel on a single task such as CAD detection of lung nodules or to give multiple opinions using multiple algorithms Middleware software is used to create interoperability between DICOM devices and the cagrid which uses a service oriented architecture

NBIA: National Cancer Imaging Archive Initially designed as repository for LIDC and RIDER CT lung nodule studies 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

NBIA Demo: Home Page

NBIA Demo: Using the Search Criteria

NBIA Demo: Search Results/ Selecting Images for Download

NBIA Demo: Image Visualization

NBIA PC DICOM Viewer: Cedara i- Response

NBIA Mac DICOM Viewer: OSIRIX Download or Virtual PACS

The Cancer Genome Atlas (TCGA) In Vivo Imaging Project Initial Phase

TCGA The Cancer Genome Atlas Collaboration between National Human Genome Research Institute and NCI The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the genetics of cancer using innovative genome analysis technologies.

The Cancer Genome Atlas TCGA researchers have identified four distinct molecular subtypes of glioblastoma multiforme (GBM), and demonstrated that response to aggressive chemotherapy and radiation differed by subtype These findings, reported in the January 19 issue of Cancer Cell, may result in more personalized approaches to treating groups of GBM patients based on their genetic alterations

TCGA Second Study in Cancer Cell Another study published in April by The Cancer Genome Atlas Research Network also in Cancer Cell used epigenomic profiling Maps specific chemical changes or 'marks' to different areas of the genome, to reveal a new subtype of Glioblastoma Multiforme (GBM) Most patients with GBM survive only 12-15 months after their initial diagnosis However, patients with this specific subtype, called Glioma CpG Island Methylator Phenotype (G-CIMP), have a median survival of three years

Goals of TCGA Imaging Workspace Project Investigate the added value of highly structured interpretation and quantification of MRI images of the TCGA dataset using AIM Determine the correlation between MRI imaging and genotypic information and response to therapy and prognosis Revise Cell article to include impact of MRI data Determine the potential for these tools in routine clinical practice

Feature Set Controlled Vocabulary 20 features clustered by categories. Lesion Location Morphology of Lesion Substance Morphology of Lesion Margin Alterations in Vicinity of Lesion Extent of Resection Goal is to capture imaging features of entire tumor and imaging features of resection specimen.

Examples Non-standardized Features May correspond to Angiogenesis, Oxygenation, Apoptosis, Cellularity Infiltration Margination Edema Non-enhancing tumor. Enhancement Irregular Nodular Indistinct Infiltrative Necrosis Physiologic Diffusion Perfusion

Well marginated Non-enhancing

Infiltrative & Necrotic Type

Nodular Predominantly Non-enhancing

Three Workstations (Osirix [Mac], Clear Canvas [PC] and XIP Purpose Built Were Modified to Retrieve TCGA Images from NBIA Database and Use Standardized Template and Save Interpretation and Quantitative Measurements to AIM Data Service on cagrid Osirix / ipad Assistant Demo Osirix / ipad Workstation XIP / AVT Workstation - Clear Canvas Workstation

Purpose of TCGA Radiology Phase II Project Project Goals Utilize multiple CBIIT/caBIG technologies together to create a practical system to capture diagnostic imaging knowledge in a structured, standardized manner and to allow for the integration with genomic and clinical data Have at least two radiologists interpret the TCGA MRI brain images associated with the Cancer Cell article Utilize cabig tools to create a repository of the qualitative and quantitative information associated with the analysis of the images Utilize cabig tools to perform cross database comparisons for research purposes Demonstrate potential of cabig tools to assist in clinical decision support

Achievements: Radiology Reading TCGA cases in NBIA have been read by at least two funded neuro-radiologists: A radiologist fills out AIM based reporting template. New annotation data is saved on AIME. New markups created on Workstation and saved to the AIME. Existing markups and annotation retrieved from AIM Data Service at Emory (AIME). Images retrieved from NBIA at CBIIT

Achievements: TCGA Cancer Cell Data Service Because the existing TCGA Grid Data Service is not currently available, we created our own grid data service to host genomic and clinical data from the 12/09 Cancer Cell article. Built a data model for Cancer Cell genomic and clinical data Used cacore SDK 4.2 to quickly generate an application from this model Used cagrid Introduce SDK to create a Grid data service from the SDK model Deployed data service at Emory Create scientific queries for cab2b Successfully queried 3 disparate cagrid data services (AIM, NBIA, TCGA Cancer Cell) with cab2b Documented insights gained from the process of setting up our own data and grid service

Achievements: cab2b Query of NBIA, AIM and TCGA CC Data Services Successfully queried 3 disparate cagrid data services (AIM, NBIA, TCGA Cancer Cell) with cab2b

Achievements: Additional Analysis with caintegrator2 caintegrator2 team added a feature to support integration with AIM grid data service to load annotations caintegrator2 Study: Combine TCGA Cancer Cell data (from CSV), AIM data from grid service, and images from NBIA production grid service. Created scientifically relevant queries based on image observations and clinical data Generated Kaplan-Meier plots of survival based on certain observations and genomic subtypes

Achievements: Preliminary Scientific Findings Survival of patients with greater thickness of enhancement (who appear to have had tumors with a thicker rim ) was significantly for shorter than those who had less. Survival of patients who had larger thickness of enhancement tumors with hemorrhage was significantly for shorter than those who did not. Survival of patients who had tumors that crossed midline was significantly for shorter than those who did not.

Opportunities to Further Deploy TCGA Related Imaging and Life Sciences Technologies Cancer Imaging Program: - Continued TCGA Genotype/Phenotype Research with CBIIT, NIH Clinical Center - Quantitative Imaging Network Program - Cancer UK Research Program - All Ireland Initiative Program Radiation Research Program - RTOG 0522 Study NIAMS Osteoarthritis Study - Annotation of radiology data - Integrating of radiology data with other OAI data types

How the TCGA Radiology Project Fits Into the cabig Imaging Program Roadmap The Workstation provides a template for the type of visualization service that we wish to make available as part of the suite of Imaging web-based services. The AIM Data Service is part of the proposed suite of web-based services offered by CBIIT. All of the TCGA technologies are part of the proposed software refactoring for SAIF/ECCF compliance.

Proposed Next Steps for TCGA Radiology 1. Ongoing operation and maintenance of NBIA, Clear Canvas, AIM Data Service and TCGA Cancer Cell Data Service. 2. Communication to community that radiologists can continue to read the cases and add to the AIM TCGA data set 3. CIP recruited additional radiologists to read the cases since the AIM model allows any number of readers to refer to one or more instances of the AIM data service 4. CIP also says that they are working with TCGA sites to get additional TCGA radiology cases to be loaded on CBIIT s NBIA.

1. Plan to create a hosted instance of AIM Data Service, and TCGA Cancer Cell Data Service at CBIIT and in the cloud 2. Communication to community that researchers can now query across the three data services. CIP is also working with Carl Schaefer and Robert Clifford to begin to do research correlations among the clinical, genomic and image annotation data. 3. Solicit feedback from community regarding desired features for the Workstation and AIM Data Service.

Future Plans Provide software to NCI clinical cancer centers for their own clinical trials/research studies involving diagnostic imaging Extend work from in-vivo Imaging to pathology

Future Plans for TCGA Imaging Project Include higher order analysis, such as quantitative diffusion imaging and perfusion imaging metrics, that could be more sensitive predictors of disease severity, candidates for effective therapy, and expected outcomes combining human with semiautomated and automated analysis of images

Future Plans for TCGA Project Ultimately would like to develop a service that has capability to provide immediate feedback for radiologist or oncologist on patient survival, patient treatment, etc. Incorporate genomic and other data display during radiology interpretation at a workstation

General Access TCGA Data We plan to offer the study for public consumption [on the production tier] by the end of September.

Providing Radiology Observation Data for Genotypic/Phenotypic Analysis in Support of TCGA caintegrator2 Demo

caintegrator 2: Login

caintegrator 2: Home

caintegrator 2: Home TCGA data from a Cancer Cell paper TCGA caarray data from cagrid 196 subjects NBIA and AIM data from cagrid

caintegrator 2: Query Criteria

caintegrator 2: Imaging Observations 1. Calvarial Remodeling 2. Cortical involvement 3. Cysts Yes, no, indeterminate Yes, no, indeterminate Yes, no, indeterminate 4. Deep WM Invasion Brainstem, corpus callosum, internal capsule, none, indeterminate 5. Definition of the Enhancing Margin Well-defined, poorly-defined, indeterminate, N/A 6. Definition of the Non-Enhancing Margin Well-defined, poorly-defined, indeterminate, N/A 7. Diffusion 8. Distribution 9. Enhancement Quality 10. Enhancing Tumor Crosses Midline 11. Ependymal Extension 12. Hemorrhage 13. ncet Tumor Crosses Midline 14. Pial Invasion 15. Proportion Enhancing 16. Proportion ncet 17. Proportion Necrosis 18. Proportion of Edema 19. Satellites 20. T1-FLAIR Ratio 21. Thickness of the Enhancing Margin Restricted, facilitated, indeterminate, no image (no ADC) Focal, multifocal, multicentric, multifocal or multicentric, gliomatosis mark/avid, minimal/mild, none, indeterminate Yes, no, indeterminate, N/A Yes, no, indeterminate Yes, no, indeterminate Yes, no, indeterminate, N/A Yes, no, indeterminate 68-95%, 34-67%, 6-33%, <5%, 0%, indeterminate 68-95%, 34-67%, 6-33%, <5%, 0%, indeterminate 68-95%, 34-67%, 6-33%, <5%, 0%, indeterminate 68-95%, 34-67%, 6-33%, <5%, 0%, indeterminate Yes, no, indeterminate Infiltrative, expansive, mixed, indeterminate, N/A Solid, thick/nodular, thin, none, indeterminate

caintegrator 2: Query Criteria

caintegrator 2: Results Type 1. Age At First Diagnosis 2. Gender 3. Karnofsky Score 4. Survival (days) 5. Vital Status 6. Subtype 7. % Necrosis 8. % Tumor Nuclei 9. Etc.

caintegrator 2: Query Results

caintegrator 2: NBIA

caintegrator 2: Query

caintegrator 2: KM Plot

caintegrator 2: Genomic Data

Grandpa Jones

Conclusions Query Analysis Prognosis Clinical Decision Support

Thank you Adam Flanders CBITT Government Sponsors: Ed Helton Robert Shirley Mervi Heiskanen 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