Summary

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. A pilot project was performed utilizing clinical and genomic data from REMBRANDT and the correlative imaging data from VASARI. Since then the group has begun detailed analysis of similar data from the TCGA Data Portal and the correlative TCGA-GBM imaging data stored within TCIA. This has led to a wider research focus with a number of sub groups focused on different aspects of analysis.

Accepted Abstracts

RSNA 2011 (Nov 27-Dec 2, 2011, Chicago, IL)

ASNR 2011 (June 4-9, 2011, Seattle, WA)

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Relationship between MR Imaging Features, Gene Expression Subtype, and Histopathologic Features of Glioblastomas
Hwang SN, Clifford R, Huang E, Hammoud D, Jilwan M, Raghavan P, Wintermark M, Gutman DA, Moreno C, Cooper L, Freymann J, Kirby J, Krishnan A, Dehkharghani S, Jaffe C, Saltz JH, Flanders A, Brat DJ, Holder CA

"Results suggest an association between Proneural subtype of glioblastoma and smaller proportion of tumor enhancement. Overall tumor size and proportion of necrosis (derived from MR images) were associated with the presence of microvascular hyperplasia. This supports the observation that hypoxia related to necrosis induces microvascular hyperplasia."

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Associations Between MR Imaging and Genomic Features of Glioblastomas
Hwang SN, Holder CA Huang E, Clifford R, Hammoud D, Raghavan P, Jilwan M, Wintermark M, Gutman DA, Cooper L, Moreno C, Kirby J, Freymann J, Dehkharghani S, Krishnan A, Jaffe C, Flanders A, Saltz JH, Brat DJ

"EGFR mutant tumors were significantly larger than TP53 mutant tumors, and were more likely to demonstrate pial involvement. CDKN2A homozygous deletion was associated with an ill-defined nonenhancing tumor margin and enhancing pial involvement."

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A Methodology for Multi-reader Assessment of MR Imaging Features of Gliomas in Clinical Trials
Flanders, A. Huang E, Wintermark M, Hammoud D, Jilwan, M. Raghaven, Holder C, Hwang S, Clifford R, Freymann J, Kirby J, Jaffe C C

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Summary

A Methodology for Multi-reader Assessment of MR Imaging Features of Gliomas in Clinical Trials

Flanders, A. Huang E, Wintermark M, Hammoud D, Jilwan, M. Raghaven, Holder, C

Hwang, S. Clifford, R. Freymann, J, Kirby, J, Jaffe, C. C.

ASNR 49th Annual Meeting & NER Foundation Symposium 2011 June 4 - 9, Seattle, WA, Scientific Paper (Oral Presentation)

PURPOSE

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 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.

MATERIALS & METHODS

A controlled terminology for describing the MR features of human gliomas was devised based upon prior work (VASARI/Rembrant 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.

RESULTS

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).

CONCLUSION

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)."

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Prediction of Glioblastoma Multiforme (GBM) Patient Survival Using MRI Image Features and Gene Expression
Nicolasjilwan M, Clifford R, Raghavan P, Wintermark M, Hammoud D, Huang E, Jaffe C, Freymann J, Kirby J, Buetow K, Huang S, Holder C

"A subset of VASARI imaging features correlate well with patient survival. Linear regression models incorporating multiple imaging features or a single VASARI feature (ependymal extension) and tumor gene expression can be used to predict patient survival."

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caBIG Tools for TCGA-GBM Analysis

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