the cabig cancer genome atlas radiology project

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 Application

(Can be replaced with any DICOM WG23-compatible Host)

XIP Host Adapter

XIP

LIB

ITK

VTK

. . .

XIP ModulesHost Independent

WG23

XIP HostWG23

WG23

Web-based Application

Medical Imaging Workstation

Standalone Application

Distribute

DICOM, HL7, and other

services per IHE Profiles

caGRID Services via

Imaging Middleware

XIP Application Builder

XIP Class Library Auto Conversion Tool

Host-Specific Plug-in Libraries

WG23

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

Osirix / iPad Assistant Demo

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 Workstation

Clear Canvas Workstation

XIP / AVT 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:

Images retrieved

from NBIA at CBIIT

New markups created

on Workstation and

saved to the AIME.

Existing markups and

annotation retrieved

from AIM Data Service

at Emory (AIME).

A radiologist fills out

AIM based reporting

template.

New annotation data

is saved on AIME.

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 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.

• 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.

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 semi-

automated 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

196 subjects

TCGA data from a

Cancer Cell paper

TCGA caArray data

from caGrid

NBIA and AIM data

from caGrid

caIntegrator 2: Query Criteria

• Yes, no, indeterminate



• Brainstem, corpus callosum, internal capsule, none, indeterminate

• Well-defined, poorly-defined, indeterminate, N/A

• Well-defined, poorly-defined, indeterminate, N/A

• 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, N/A


• 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


• Infiltrative, expansive, mixed, indeterminate, N/A

• Solid, thick/nodular, thin, none, indeterminate

caIntegrator 2: Imaging Observations

1. Calvarial Remodeling

2. Cortical involvement

3. Cysts

4. Deep WM Invasion

5. Definition of the Enhancing Margin

6. Definition of the Non-Enhancing Margin

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

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

the cabig cancer genome atlas radiology project

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