Summary
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The Cancer Genome Atlas (TCGA) Glioma Phenotype Research Group is part of the |
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Join the Research Group
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 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.
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 Research Project - 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.
- DSC T2* MR Perfusion Analysis - 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.
- Prediction of outcome using clinical, imaging and genetic information - This project seeks to use the VASARI Research Project output in combination with data from the TCGA Data Portal to evaluate survival and time to recurrence. This project is being led by Max Wintermark and Manal Nicolas Jilwan of the University of Virginia.
- Mapping of Edema/Cellular Invasion to MR Phenotypes - This project set out to present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM. 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
Publications
Citation | TCIA Shared Lists | Supporting Materials |
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Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, et al. 2011 | 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.
Conference Abstracts
RSNA 2011 (Nov 27-Dec 2, 2011, Chicago, IL)
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Presentation Time (CST) and Location
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Supporting Materials
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A Coordinated Method for Clinical Trials Research: Multireader Assessment of MR Imaging Features of Human Gliomas
A E Flanders, MD, Philadelphia, PA; E Huang, PhD; M Wintermark, MD; M Nicolas-Jilwan, MD; P Raghavan, MD; C A Holder, MD; et al.
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Tue Nov 29 2011 9:25AM - 9:35AM ROOM E451B
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Computer-aided Visual Image Analysis of Glioblastomas and Genomic Features
S N Hwang, PhD,MD, Atlanta, GA; C A Holder, MD; E Huang, PhD; R A Clifford; D Hammoud, MD; P Raghavan, MD; et al.
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A Novel Statistical Method for Lossless Compression of Diagnostic Imaging Features
E Huang, PhD, Rockville, MD; J B Freymann, BS; J Kirby; R A Clifford; C Jaffe, MD; A E Flanders, MD
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Wed Nov 30 2011 12:15PM - 12:45PM ROOM Lakeside Learning Center
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; an effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to tissue specimens analyzed for The Cancer Genome Atlas (TCGA). |
Imaging Source Site (ISS) Groups are being formed and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Current The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) (glioblastoma) and The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG) (low grade glioma) source sites include:
- Thomas Jefferson University
- Henry Ford Hospital
- University of California, San Francisco
- MD Anderson Cancer Center
- Emory University
- Mayo Clinic
- Case Western Reserve University School of Medicine
A number of other collaborators have also been invited to join the group by its members. Please contact Adam Flanders (adam.flanders@jefferson.edu) if you have scientific questions for TCGA-GBM/LGG ISS or if you are interested in collaborating with their group.
Publications
TCGA Glioma Phenotype Research Group Publications
Wangaryattawanich, P., M. Hatami, et al. "Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival." Neuro-oncology, (2015): nov117 .
Colen RR, Wang J, Singh SK, Gutman DA, Zinn PO. Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death. Radiology. 2014.
- Colen RR, Vangel M, Wang J, Gutman DA, Hwang SN, Wintermark M, Rajan J, Jilwan-Nicola M, Chen JY, Raghavan P, Holder CA, Rubin D, Huang E, Kirby J, Freymann J, Jaffee CC, Flanders A, Zinn PO. Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project.BMC Medical Genomics, 2014. 7(1):30. doi:10.1186/1755-8794-7-30 (link)
- Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, Chesier SH, Napel S, Zaharchuk G, Plevritis SK. Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features. Radiology, 2014. doi: 10.1148/radiol.14131731 (link)
- Jain R, Poisson L, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Jaffe C, Mikkelsen T, Flanders A. Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. Radiology. 2014 Aug;272(2):484-93. doi: 10.1148/radiol.14131691. Epub 2014 Mar 19. 2014 (link)
- Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. Journal of Neuroradiology, July 2014. doi: 10.1016/j.neurad.2014.02.006
- Wassal E, Zinn P, Colen R. DIFFUSION AND CONVENTIONAL MR IMAGING GENOMIC BIOMARKER SIGNATURE FOR EGFR MUTATION IDENTIFICATION IN GLIOBLASTOMA. Neuro-Oncology. 2014;16(suppl 5):v156-v7.
Wassal E, Zinn P, Colen R. DIFFUSION AND CONVENTIONAL MR IMAGING GENOMIC BIOMARKER SIGNATURE PREDICTS IDH-1 MUTATION IN GLIOBLASTOMA PATIENTS. Neuro-Oncology. 2014;16(suppl 5):v157-v.
Amer A, Zinn P, Colen R. IMMEDIATE POST OPERATIVE VOLUME OF ABNORMAL FLAIR SIGNAL PREDICTS PATIENT SURVIVAL IN GLIOBLASTOMA PATIENTS. Neuro-Oncology. 2014;16(suppl 5):v138-v.
- Amer A, Zinn P, Colen R. IMMEDIATE POST-RESECTION PERICAVITARIAN DWI HYPERINTENSITY IN GLIOBLASTOMA PATIENTS IS PREDICTIVE OF PATIENT OUTCOME. Neuro-Oncology. 2014;16(suppl 5):v138-v9.
- Gutman DA, Cooper LAD, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Monqkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ. MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set. Radiology. 2013 May:267(2):560-569,doi:10.1148/radiol.13120118 (link)
- Jain R, Poisson L, Narang J, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Brat DJ, Jaffe C, Mikkelsen T. Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers. Radiology, 2013 Apr:267(1):212 –220, doi:10.1148/radiol.12120846 (link)
- Zinn PO, Colen RR. Imaging Genomic Mapping in Glioblastoma. Neurosurgery 60:126-130. Aug 2013 (link)
- Jain R, Poisson L, Narang J, Scarpace L, Rosenblum ML, Rempel S, Mikkelson T. Correlation of Perfusion Parameters with Genes Related to Angiogenesis Regulation in Glioblastoma: A Feasibility Study. American Journal of Neuroradiology, 2012. 33(7):1343-1348 [Epub ahead of print] (link)
- Zinn PO, Sathyan P, Mahajan B, Bruyere J, Hegi M, et al. (2012) A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature. PLoS ONE, 2012 7(8): e41522. doi:10.1371/journal.pone.0041522 (link)
- Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, et al. 2011Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme. PLoS ONE, 2011 6(10): e25451. doi:10.1371/journal.pone.0025451 (link)
Zinn, P. O., M. Hatami, et al. (2015). "138 Diffusion MRI ADC Mapping of Glioblastoma Edema/Tumor Invasion and Associated Gene Signatures." Neurosurgery 62: 210.
Publications written by other members of the research community can be found on our TCIA Publications page. Please contact us at help@cancerimagingarchive.net if you have a publication you would like us to add.
TCGA Genomics Publications
Read the 2008 Nature paper and 2013 Cell paper to learn more about the GBM genomic study. Read the New England Journal of Medicine LGG paper to learn more about the LGG genomic study. Additional TCGA publications can be found at: http://cancergenome.nih.gov/publications.
Publication Policies
Per TCGA and TCIA Guidelines, formal permission requests are no longer required to submit publications using TCGA-GBM or TCGA-LGG data. Please see the following links for more information about the freedom-to-publish criteria for these data sets:
Data Source | Status |
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No restrictions; all data available without limitations. | |
No restrictions; all data available without limitations. |
Please contact us at help@cancerimagingarchive.net if you have any questions about these policies
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Prediction of Glioblastoma Multiforme (GBM) Time to Recurrence Using MRI Image Features and Gene Expression
M Nicolasjilwan, MD, Charlottesville, VA; R A Clifford; A E Flanders, MD; L Scarpace; P Raghavan, MD; D Hammoud, MD; et al.
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Thu Dec 01 2011 11:50AM - 12:00PM ROOM N229
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Radiogenomic Mapping in GBM: A Novel Quantitative Merge between Imaging and Genomics - The Creation of a Signature for Tumor Necrosis Using Image Genomic Analysis in 12, 764 genes and 555 microRNAs
R R Colen, MD, Boston, MA; P O Zinn, MD; J R Bruyere, BS,MA; B Mahajan, MBBS; F A Jolesz, MD
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Tue Nov 29 2011 11:00 AM - 11:10 AM ROOM E451B
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SNO 2011 (Nov 17-20, 2011, Orange County, CA)
Title | Supporting Materials |
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Radiogenomic Mapping in GBM in Patients with High versus Low Edema/Tumor Infiltration using An Image- Genomic Analysis of 12, 764 genes and 555 microRNAs | |
Survival prediction using molecular classification of glioblastomas: Role of tumor blood volume estimation using DSC T2* MR perfusion |
The Cancer Genome Atlas Network Symposium (Nov 17-18, 2011, Washington, DC)
Title | Supporting Materials |
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Neuroimaging Predictors of Survival, Pathology, and Molecular Profiles in TCGA Glioblastomas |
ASNR 2011 (June 4-9, 2011, Seattle, WA)
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 collection utilized in this research project:
- Henry Ford
- UCSF
- MDACC
- Emory
caBIG Tools for TCGA-GBM Analysis
Informatics software for use with this data has also been developed as part of the caBIG TCGA Enterprise Use-Case project. This caBIG enterprise use-case enabled TCGA images stored in NBIA (the same software powering the Cancer Imaging Archive) to be displayed on three different free and/or open source DICOM viewer workstations that possess annotation and markup capabilities based on Annotation Imaging Markup (AIM). These workstations were customized to allow retrieval of images from NBIA over the caGrid (from the NCI CBIIT deployed NBIA server only), markup by AIM standards, and storage back to an AIM-E Grid data service. Some of these tools have been leveraged as part of the CIP TCGA Radiology Initiative where possible.