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The TCGA Glioma Phenotype Research Group is part of the CIP TCGA Radiology Initiative. The group began as an ad hoc multi-institutional research team dedicated to discovering the value of applying controlled terminology to the MR imaging features of patients with gliomas (see: VASARI Research Project). Research trials that incorporate imaging present unique challenges due to nonstandard use of terminologies, absence of uniform data collection and validation. These obstacles traditionally limit the impact of imaging as an effective biomarker in oncology. The original purpose of this project was to assess reliability of tools and terminology developed by the Cancer Bioinformatics Grid (caBIG) initiative when performing a multireader simultaneous assessments of glioblastoma MR imaging features. |
The controlled "VASARI" terminology for describing the MR features of human gliomas was devised based upon prior work (REMBRANDT project). This comprehensive featureset consists of 24 observations familiar to neuroradiologists to describe the morphology of brain tumors on routine contrast-enhanced MRI. The National Biomedical Imaging Archive (NBIA) was used to store the de-identified baseline MRI studies for 78 glioblastomas collected for The Cancer Genome Atlas (TCGA) initiative. Six neuroradiologists in three disparate geographic locations were recruited and trained in the use of the featureset using a visual guidebook. Training cases were employed to assess competency and to ensure agreement. A open-source PACS workstation (Clear Canvas) was customized for clinical imaging research evaluation and deployed at each of the three centers. Networking tools built into the workstation were used to securely download studies from NBIA (caGRID). As studies were evaluated, scores were simultaneously uplinked to a single remote AIM (Annotation and Image Markup) repository for QC checks and interim analysis. Case assignments were deliberately staged in a staggered fashion to ensure that a minimum of three evaluations were efficiently obtained. Administrative tools were employed by coordinators in a fourth location. Qualitative assessments included: (1) effectiveness of training, (2) ease of deployment & functionality of the informatics solutions and (3) efficiency of the process. Inter-observer variation for each feature was assessed with the generalized kappa statistic of Berry&Miekle.
Training, deployment of resources and completion of three evaluations per case were accomplished in 30 days. Functionality of the IT solutions was rated superior in qualitative assessment. The results indicated strong overall average inter-observer agreement among all six readers. Agreement was highest for tumor side (generalized kappa statistic k=0.943, 95% CI 0.915-0.982) and tumor location (k=0.837, 95% CI 0.807-0.902). Other features with high agreement included proportion enhancing tumor (k=0.656, 95% CI 0.596-0.757), presence of satellites (k=0.663, 95% CI 0.591-0.780), and diffusion (k=0.730, 95% CI 0.664-0.828). Of the remaining, only three features (12.5%) showed low agreement (k<0.4): presence of calvarial remodeling (k=0.366, 95% CI 0.124-0.626), cortical involvement (k=0.167, 95% CI 0.157-0.335), and definition of non-enhancing margin (k=0.374, 95% CI 0.347-0.514).
Inclusion of vetted, tested and validated controlled terminologies into imaging arms of clinical trials is essential in adding value of imaging as a biomarker in cross-cutting correlative studies. Controlled terminologies such as the one described herein for assessment of gliomas can be effectively used by domain experts following a relatively short training period. Technologies developed through the caBIG initiative provide an effective and efficient framework for federated imaging assessments that can expedite cross-correlative analysis with other data repositories (e.g. genomics / proteomics / pathology).
Updates to the TCGA-GBM imaging data set are being stored within TCIA and the research group is continuing to expand on their work.
It has since grown into a number of diverse research initiatives conducted by a geographically disparate open science research team. |
Join the Research Group
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This is an open/ad-hoc research group which has seen participation from many different people over the life of the project. We hold weekly teleconference meetings on Tuesdays at 2pm ET. Please contact the CIP Informatics Team us at cancerimagingarchive@mail.nih.gov if you would like to join our calls or be kept in the loop as this effort moves forward. You can reach us by emailing John Freymann or Justin Kirby at:
- freymanj (at) mail (dot) nih (dot) gov
- kirbyju (at) mail (dot) nih (dot) gov
Participants:
- TJU
- Adam Flanders
- UVA
- Max Wintermark
- Manal Jilwan
- Prashant Raghavan
- Guangming Zhu
- Emory
- Chad Holder
- Scott Hwang
- David Gutman
- Joel Saltz
- Henry Ford
- Rajan Jain
- Jayant Narang
- Lisa Scarpace
- BWH
- Rivka Colen
- Boston University
- Carl Jaffe
- Sage Bionetworks
- Andrew Trister
Data Providers
We would like to thank the following institutions for contributing images to the TCGA-GBM collection utilized in this research project:
Group Projects
This is a listing of ongoing projects. Feel free to join one of our teleconferences to tell us about how you intend to use this data and discuss how we might be able to collaborate.
- VASARI Feature Analysis - Multiple readers reviewing TCGA brain cases and evaluating them based on the VASARI feature set and evaluating the results for reader agreement along with possible connection to related clinical/genetic/pathology data collected for the TCGA. This project is being led by Adam Flanders at Thomas Jefferson University.
- Survival prediction using molecular classification of glioblastomas using DSC T2* MR perfusion - This project has been accepted/presented at multiple conferences (see below) and is being led by Rajan Jain at Henry Ford Hospital.
- Mapping of Edema/Cellular Invasion to MR Phenotypes - A paper has already been published on this in PLoS ONE (referenced below). This project was led by Pascal Zinn and Rivka Colen of MDACC and BWH respectively.
- Man-machine correlation of VASARI features between human and machine observers - This project is being led by Dave Gutman (Emory) and Rivka Colen (BWH).
- Analysis of Diffusion-Sensitized MRI for Predicting the Histopathologic, Genomic, and Clinical Features - This is a newer project being initiated and led by Scott Hwang at Emory University
- CAD Texture Analysis - Led by Brad Erickson at Mayo
- Growth Kinetics - Led by Andrew Trister at Sage Bionetworks
- Henry Ford
- UCSF
- MDACC
- Emory
Publications
Citation | TCIA Shared Lists | Supporting Materials |
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Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, et al. 2011 | TCGA-GBM Round 1 Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes |
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Note: For more information on how Shared Lists are used to cite and share data please view our TCIA Citation Guidelines.
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Title | Supporting Materials |
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Relationship between MR Imaging Features, Gene Expression Subtype, and Histopathologic Features of Glioblastomas | |
Associations Between MR Imaging and Genomic Features of Glioblastomas | |
A Methodology for Multi-reader Assessment of MR Imaging Features of Gliomas in Clinical Trials | |
Prediction of Glioblastoma Multiforme (GBM) Patient Survival Using MRI Image Features and Gene Expression |
Data Providers
We would like to thank the following institutions for contributing images to the TCGA-GBM|display/Public/TCGA-GBM\ collection utilized in this research project:
- Henry Ford
- UCSF
- MDACC
- Emory
caBIG Tools for TCGA-GBM Analysis
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