Development and Validation of Qualitative and Quantitative   Descriptors in Gliomas         David A Gutman MD PhD    Depar...
Quick overview of Glioblastoma (GBM)Center for Comprehensive Informatics                                       • Most comm...
The Cancer Genome Atlas (TCGA)                                       • Characterize 500 tumors for each of a variety of ca...
TCGA and Imaging Data: Radiology and Pathology                                       • The Cancer Imaging Archive (TCIA) n...
Overall question…                                       • Do tumors that “look” different behave differently?Center for Co...
Genetic signatures can define tumor subtypesCenter for Comprehensive Informatics
Clustering identifies three morphological groups                                       • Analyzed 200 million nuclei from ...
Representative nucleiCenter for Comprehensive Informatics                                                         Large,  ...
How Does One Effectively Marry Imaging                                       Findings of a Tumor to its Genomics?Center fo...
VASARI Feature Set                                       • A set of 30 imaging characteristics to describe highCenter for ...
Defining a Rich Set of Qualitative and                                               Quantitative Image Biomarkers        ...
F5 – Proportion EnhancingCenter for Comprehensive Informatics                                       Visually, when scannin...
F7 – Proportion NecrosisCenter for Comprehensive Informatics   Visually, when scanning through the entire tumor volume, wh...
Capturing structured annotations and                                            markups/AIM Data ServiceCenter for Compreh...
For validation, focused on semi-quantitative                                       features                               ...
Correlating between quantitative and                                             qualitative features: Man vs MachineCente...
Agreement between qualitative and                                            quantitative feature setCenter for Comprehens...
Inter-rater agreement of relevant imaging                                       features between radiologists scores accor...
3d Slicer Volume Segmentation                                                           (R. Colen/MGH)Center for Comprehen...
Center for Comprehensive Informatics                                       Machine vs Machine?
Center for Comprehensive Informatics                                       Cleaning up the raw data from TCIA
Developed some tooling to help with image                                                   validation & QACenter for Comp...
Slicer Volumes vs Velocity Derived VolumesCenter for Comprehensive Informatics
Center for Comprehensive Informatics                                       Do image features predict outcome?
Combination of clinical and imaging featuresCenter for Comprehensive Informatics
Are imaging features equally distributed across                                              Verhaak classification subtyp...
Correlation of Volumetric Data with OutcomeCenter for Comprehensive Informatics
Future Work                                       • Working on extracting features from volumetricCenter for Comprehensive...
In Silico Brain Tumor Research Center Team                                       •   Emory University   •   Henry Ford Hos...
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Dr. David Gutman: Development and Validation of Radiology Descriptors in Gliomas

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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.

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Dr. David Gutman: Development and Validation of Radiology Descriptors in Gliomas

  1. 1. Development and Validation of Qualitative and Quantitative Descriptors in Gliomas David A Gutman MD PhD Department of Biomedical Informatics Emory University
  2. 2. Quick overview of Glioblastoma (GBM)Center for Comprehensive Informatics • 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. 3. The Cancer Genome Atlas (TCGA) • Characterize 500 tumors for each of a variety of cancers • Clinical recordsCenter for Comprehensive Informatics • Genomics: gene, miRNA expression, copy number, sequence, DNA methylation • Imaging: pathology and radiology
  4. 4. TCGA and Imaging Data: Radiology and Pathology • The Cancer Imaging Archive (TCIA) now containsCenter for Comprehensive Informatics 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. 5. Overall question… • Do tumors that “look” different behave differently?Center for Comprehensive Informatics – e.g. different outcome – Different genetic profiles Problems… – Need for a standardized method to describe what the tumors look like…
  6. 6. Genetic signatures can define tumor subtypesCenter for Comprehensive Informatics
  7. 7. Clustering identifies three morphological groups • Analyzed 200 million nuclei from 162 TCGA GBMs (462 slides)Center for Comprehensive Informatics • Named for functions of associated genes: Cell Cycle (CC), Chromatin Modification (CM), Protein Biosynthesis (PB) • Prognostically-significant (logrank p=4.5e-4)
  8. 8. Representative nucleiCenter for Comprehensive Informatics Large, Small light nuclei, Intermediate hyperchromatic Eosinophilic cyoplasm L Cooper nuclei
  9. 9. How Does One Effectively Marry Imaging Findings of a Tumor to its Genomics?Center for Comprehensive Informatics X Genetic Microarray A Flanders
  10. 10. VASARI Feature Set • A set of 30 imaging characteristics to describe highCenter for Comprehensive Informatics 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. 11. Defining a Rich Set of Qualitative and Quantitative Image Biomarkers • This has been a community-driven ontology developmentCenter for Comprehensive Informatics 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. 12. F5 – Proportion EnhancingCenter for Comprehensive Informatics 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. 13. F7 – Proportion NecrosisCenter for Comprehensive Informatics 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. 14. Capturing structured annotations and markups/AIM Data ServiceCenter for Comprehensive Informatics
  15. 15. For validation, focused on semi-quantitative features • Compared various outcome and genomic measuresCenter for Comprehensive Informatics 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. 16. Correlating between quantitative and qualitative features: Man vs MachineCenter for Comprehensive Informatics Results of univariate linear regression for agreement between VASARI measurements and measurements derived from quantitative volumetric analyses.
  17. 17. Agreement between qualitative and quantitative feature setCenter for Comprehensive Informatics
  18. 18. Inter-rater agreement of relevant imaging features between radiologists scores according to VASARI standardCenter for Comprehensive Informatics
  19. 19. 3d Slicer Volume Segmentation (R. Colen/MGH)Center for Comprehensive Informatics 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. 20. Center for Comprehensive Informatics Machine vs Machine?
  21. 21. Center for Comprehensive Informatics Cleaning up the raw data from TCIA
  22. 22. Developed some tooling to help with image validation & QACenter for Comprehensive Informatics
  23. 23. Slicer Volumes vs Velocity Derived VolumesCenter for Comprehensive Informatics
  24. 24. Center for Comprehensive Informatics Do image features predict outcome?
  25. 25. Combination of clinical and imaging featuresCenter for Comprehensive Informatics
  26. 26. Are imaging features equally distributed across Verhaak classification subtypes?Center for Comprehensive Informatics
  27. 27. Correlation of Volumetric Data with OutcomeCenter for Comprehensive Informatics
  28. 28. Future Work • Working on extracting features from volumetricCenter for Comprehensive Informatics 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. 29. In Silico Brain Tumor Research Center Team • Emory University • Henry Ford HospitalCenter for Comprehensive Informatics – 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

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