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Summary

Imaging biomarkers and particularly tumor blood volume estimates have been shown to provide additional patient prognostic information (1-6), even independent of the histological grade in gliomas and also specifically within the high-grade glioma group.

Raw and post-processed image subsets of the TCGA-GBM collection can be used to evaluate the role of tumor blood volume estimated using DSC T2* magnetic resonance (MR) perfusion in GBM. This data can be correlated with information in genomic publications or information from the TCGA Data Portal for survival prediction and comparison of other genomic and clinical results.

The post-processed studies were generated with nordicICE software (NordicImagingLab AS) using the FDA approved DSCT2* perfusion module. The module corrects for contrast agent leakage from the intravascular to extracellular space using the method published by Boxerman et al (7). Normalized relative cerebral blood volume (rCBV) maps with leakage correction were produced by the software, which normalizes the CBV relative to a globally determined mean value.

All the regions of interest (ROI) were drawn by Rajan Jain and Jayant Narang (Henry Ford Hospital) in consensus on the rCBV maps fused with post-contrast T1W images and FLAIR images. rCBV mean, rCBV max and rCBVNEL were measured from the rCBV maps and stored in a spreadsheet. For measuring rCBV mean ROI's were drawn on the contrast enhancing portion of the tumor (excluding any areas of necrosis and vessels) on all the slices which contained the tumor and a mean of these was obtained. For measuring rCBVmax an ROI of 10 x 10 voxels was placed on the hottest appearing part of the tumor based on the qualitative perfusion maps. An ROI of 10 x 10 voxels was placed on three spots on non-enhancing FLAIR abnormality within 1 cm of the edge of the enhancing lesion to measure rCBVNEL and obtain a mean.

This work was published in the following manuscript:

Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers.
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.
Radiology. 2013 Apr;267(1):212-20. doi: 10.1148/radiol.12120846. Epub 2012 Dec 13. (link)

Note: References listed at the bottom of this page

Supporting Documentation and Metadata

The following supporting documentation is available for download.  This information was updated 2012-02-27 and includes information relevant to the 55 processed cases in the archive.  More data is expected in the future.

  • Spreadsheet - contains scaling factors, rCBV values, and scanner info
  • Text files – contains text dumps of DICOM elements for nordicICE perfusion image studies

Shared Lists

The following 2 Shared Lists provide an easy way to download only the raw and post-processed image subsets of the TCGA-GBM collection described in the project summary.

  • TCGA-GBM DSC T2* MR Perfusion - contains the raw perfusion image studies
  • TCGA-GBM DSC T2* nordicICE - contains the post-processed nordiceICE perfusion image studies

If you are not familiar with TCIA's Shared List functionality more information can be found in section 3.7 of The Cancer Imaging Archive User's Guide.

Acknowledgements

TCIA would like to thank Dr. Rajan Jain and Dr. Jayant Narang for processing and uploading the studies generated they generated with nordicICE, as well as providing the associated spreadsheet and text files.

References

  1. Aronen HJ, Gazit IE, Louis DN, et al. Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 1994;191(1):41-51.
  2. Lev MH, Ozsunar Y, Henson JW, et al. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas corrected. AJNR Am J Neuroradiol 2004;25(2):214-221.
  3. Law M, Oh S, Babb JS, et al. Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging--prediction of patient clinical response. Radiology 2006;238(2):658-667.
  4. Law M, Young RJ, Babb JS, et al. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 2008;247(2):490-498.
  5. Bisdas S, Kirkpatrick M, Giglio P, Welsh C, Spampinato MV, Rumboldt Z. Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease? AJNR Am J Neuroradiol 2009;30(4):681-688.
  6. Mills SJ, Patankar TA, Haroon HA, Baleriaux D, Swindell R, Jackson A. Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? AJNR Am J Neuroradiol 2006;27(4):853-858.
  7. Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006;27(4):859-867.
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