On May 9, Dr. David Gutman delivered a presentation titled "Development and Validation of Radiology Descriptors in Gliomas." Researchers at Emory University, in collaboration with investigators at the University of Virginia, Henry Ford Hospital, and Thomas Jefferson Hospital, have been working to develop the Visually Accessible Rembrandt Images (VASARI) feature set, a standardized set of qualitative imaging features used to describe high-grade gliomas.

1. Development and Validation of
Qualitative and Quantitative
Descriptors in Gliomas
David A Gutman MD PhD
Department of Biomedical Informatics
Emory University

2. Quick overview of Glioblastoma (GBM)
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• Most common primary brain
tumor in adults
• Median survival 50 weeks
• ISBTRC Goals:
– To leverage rich datasets to understand the
mechanisms of glioma progression through In Silico
analysis
– To manage, explore and share semantically complex
data among researchers

3. The Cancer Genome Atlas (TCGA)
• Characterize 500 tumors for each of a variety of cancers
• Clinical records
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• Genomics: gene, miRNA expression, copy number, sequence,
DNA methylation
• Imaging: pathology and radiology

4. TCGA and Imaging Data: Radiology and Pathology
• The Cancer Imaging Archive (TCIA) now contains
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radiology data on ~ 150 patients from the TCGA
GBM data set
• Pathology data is also available on ~ 200 patients
• Our extended group’s goal is to “mine” radiology
and pathology data for phenotypes that correlate
with genetic and clinical characteristics of the
patients
• Dr. Cooper presented some of our work correlating
pathology with genomics and outcomes
• Parallel effort has been underway for radiology data
sets

5. Overall question…
• Do tumors that “look” different behave differently?
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– e.g. different outcome
– Different genetic profiles
Problems…
– Need for a standardized method to describe what the
tumors look like…

6. Genetic signatures can define tumor subtypes
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7. Clustering identifies three morphological groups
• Analyzed 200 million nuclei from 162 TCGA GBMs (462 slides)
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• Named for functions of associated genes:
Cell Cycle (CC), Chromatin Modification (CM),
Protein Biosynthesis (PB)
• Prognostically-significant (logrank p=4.5e-4)

8. Representative nuclei
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Large, Small light nuclei, Intermediate
hyperchromatic Eosinophilic cyoplasm
L Cooper nuclei

9. How Does One Effectively Marry Imaging
Findings of a Tumor to its Genomics?
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X
Genetic Microarray
A Flanders

10. VASARI Feature Set
• A set of 30 imaging characteristics to describe high
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grade gliomas (GBM) using standardized vocabulary
that is reproducible and understandable by
neuroradiologists
• Effort led by Adam Flanders and Carl Jaffe involving
coordinating “reads” and feature set development
by ~ 8 neuroradiologists

11. Defining a Rich Set of Qualitative and
Quantitative Image Biomarkers
• This has been a community-driven ontology development
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project to create a comprehensive set of imaging
observations for GBM
– Collaboration with ASNR
• Collaborators were asked to provide a list of clinical or
literature observations that could be used to describe MRI
features of GBM
• Imaging features (26 features / 4 categories)
– Location of lesion
– Morphology of lesion margin (definition, thickness,
enhancement, diffusion)
– Morphology of lesion substance (enhancement, PS
characteristics, focality/multicentricity, necrosis, cysts, midline
invasion, cortical involvement, T1/FLAIR ratio)
– Alterations in vicinity of lesion (edema, edema crossing
midline, hemorrhage, pial invasion, ependymal invasion,
satellites, deep WM invasion, calvarial remodeling)

12. F5 – Proportion Enhancing
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Visually, when scanning through the entire tumor volume, what proportion of the
entire tumor would you estimate is enhancing? (Assuming that the entire
abnormality may be comprised of: (1) an enhancing component, (2) a non-enhancing
component, (3) a necrotic component and (4) a edema component.)

13. F7 – Proportion Necrosis
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Visually, when scanning through the entire tumor volume, what proportion of the tumor is estimated to represent necrosis?
Necrosis is defined as a region within the tumor that does not enhance or shows markedly diminished enhancement, is
high on T2W and proton density images, is low on T1W images, and has an irregular border). (Assuming that the entire
abnormality may be comprised of: (1) an enhancing component, (2) a non-enhancing component, (3) a necrotic component
and (4) a edema component.)

14. Capturing structured annotations and
markups/AIM Data Service
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15. For validation, focused on semi-quantitative
features
• Compared various outcome and genomic measures
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with these features
• Also did comparisons between qualitative and
quantitative volumetric measurements performed at
MGH by Colen et. al using 3D slicer, and
measurements done at Emory using the Velocity
Platform

16. Correlating between quantitative and
qualitative features: Man vs Machine
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Results of univariate linear regression for agreement between VASARI
measurements and measurements derived from quantitative
volumetric analyses.

17. Agreement between qualitative and
quantitative feature set
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18. Inter-rater agreement of relevant imaging
features between radiologists scores according
to VASARI standard
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19. 3d Slicer Volume Segmentation
(R. Colen/MGH)
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Visualization of quantitative volumetric segmentation methodology. Region
corresponding to edema/tumor infiltration (blue) was segmented from
FLAIR sequences whereas contrast enhancement (yellow) and necrosis
(orange) have been segmented from T1 post contrast weighted images

20. Center for Comprehensive Informatics
Machine vs Machine?

21. Center for Comprehensive Informatics
Cleaning up the raw data from TCIA

22. Developed some tooling to help with image
validation & QA
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23. Slicer Volumes vs Velocity Derived Volumes
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24. Center for Comprehensive Informatics
Do image features predict outcome?

25. Combination of clinical and imaging features
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26. Are imaging features equally distributed across
Verhaak classification subtypes?
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27. Correlation of Volumetric Data with Outcome
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28. Future Work
• Working on extracting features from volumetric
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images and doing pathway analysis
• Also Rajan Jain (TJU) and Scott Hwang (Emory)
have begun doing feature extraction/markups of
perfusion and DTI data
• Continue to collect imaging data from TCGA GBM
contributors (as we track them down)
• Continue to revise/simplify feature set
• Consider extending feature set to lower grade cases

29. In Silico Brain Tumor Research Center Team
• Emory University • Henry Ford Hospital
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– Lee Cooper – Tom Mikkelsen
– Joel Saltz – Lisa Scarpace
– Daniel Brat
– Carlos Moreno • Thomas Jefferson University
– Chad Holder – Adam Flanders
– Scott Hwang
– Doris Gao • SAIC Frederick
– William Dunn – John Freymann
– Tarun Aurora – Justin Kirby
• NCI
– Eric Huang
– Carl Jaffe
• MGH
– Rivka Colen