Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity

J Magn Reson Imaging. 2010 Mar;31(3):538-48. doi: 10.1002/jmri.22068.


Purpose: To present comprehensive examinations of the assumptions made in functional diffusion map (fDM) analyses and provide a biological basis for fDM classification.

Materials and methods: Sixty-nine patients with gliomas were enrolled in this study. To determine the sensitivity of apparent diffusion coefficients (ADCs) to cellularity, cell density from stereotactic biopsy specimens was correlated with preoperative ADC maps. For definition of ADC thresholds used for fDMs, the 95% confidence intervals (CI) for changes in voxel-wise ADC measurements in normal appearing tissue was analyzed. The sensitivity and specificity to progressing disease was examined using both radiographic and neurological criteria.

Results: Results support the hypothesis that ADC is inversely proportional to cell density with a sensitivity of 1.01 x 10(-7) [mm(2)/s]/[nuclei/mm(2)]. The 95% CI for white matter = 0.25 x 10(-3) mm(2)/s, gray matter = 0.31 x 10(-3) mm(2)/s, a mixture of white and gray matter = 0.40 x 10(-3) mm(2)/s, and a mixture of white matter, gray matter, and cerebrospinal fluid = 0.75 x 10(-3) mm(2)/s. Application of these measurements as ADC thresholds produce varying levels of sensitivity and specificity to disease progression, which were all significantly better than chance.

Conclusion: This study suggests fDMs are valid biomarkers for brain tumor cellularity.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Brain Neoplasms / pathology*
  • Brain Neoplasms / physiopathology*
  • Computer Simulation
  • Diffusion Magnetic Resonance Imaging / methods*
  • Female
  • Glioma / pathology*
  • Glioma / physiopathology*
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Male
  • Middle Aged
  • Models, Biological*
  • Reproducibility of Results
  • Sensitivity and Specificity