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Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. NAGM+ NAWM+ CSF. NAGM+ NAWM. NAWM. NAGM. High Sensitivity. High Specificity. B.M. Ellingson 1,2 , M.G. Malkin 1,3,4 , S.D. Rand 1,2 , J.M. Connelly 3 , C. Quinsey 4
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biomarker for human glioma cellularity
B.M. Ellingson1,2, M.G. Malkin1,3,4, S.D. Rand1,2, J.M. Connelly3, C. Quinsey4
P.S. LaViolette1,5, D.P. Bedekar1,2, and K.M. Schmainda1,2,5
1Translational Brain Tumor Program, 2Dept. of Radiology, 3Dept. of Neurology,
4Dept. of Neurosurgery, 5Dept. of Biophysics, Medical College of Wisconsin, Milwaukee, WI
Hypothesis 3: fDMs created with different ADC thresholds reflect different sensitivity and specificity to brain tumor progression
To comprehensively validate fDMs as a biomarker for glioma cellularity and clarify the biological and clinical sensitivity to tumor progression
Hypothesis 1: Glioma cell density is inversely proportional to ADC measurement
Figure 1: A) ADC map from a glioblastoma patient showing regions of edema, necrosis, and viabile tumor. B) Cartoon demonstrating how ADC changes for each tissue type within the tumor region. C) Calculation of functional diffusion maps from sequential apparent diffusion coefficient (ADC) maps. Voxels are classified as hypercellular (blue) if ADC decreases, hypocellular (red) if ADC increases, or there is no change (green) beyond a particular ADC threshold.
Figure 2: Correlation between spatially matched ADC measurements and cell density from stereotactic biopsy samples. A) Postoperative SPGR images showing biopsy location. B) Representative histological images (H&E, x20 mag) showing increasing cellularity with increasing tumor grade (scale bar = 50 um). C) Correlation between mean ADC and mean cell density in nuclei per HPF (Pearson’s correlation coefficient, r2 = 0.7933, P<0.0001).
Hypothesis 2: ADC variability across scan days must be determined to properly set ADC thresholds
ADC variability over time (Bartlett’s test, P>0.05); however,
ADC variability was dependent on tissue type.
1. Hypocellular (Red) ADC > ADCthresh
2. Hypercellular (Blue) ADC < - ADCthresh
3. No Change (Green)
Figure 4: Comparison of fDMs for different ADC thresholds and clinical sensitivity to tumor progression. A) Volume of hypercellularity showing differences in NAWM/NAGM ADC thresholds compared to standard threshold (n=5). B) Volume of hypocellularity shows differences between NAWM ADC threshold and standard threshold (n=5). C) ROC curves for different ADC thresholds (n=33). D) fDM volume characteristics for primary gliomas (n=50). E) Rate of change in hypercellular volume vs. Karnofsky performance status (KPS). F) Kaplan-Meier survival curves for progression-free survival (PFS) in 19 of 50 patients. * P < 0.05; ** P< 0.01; *** P<0.001
Figure 3: Distribution of ADC for different tissue types, polled across different post-baseline time points (n = 69 patients; post-baseline time points from 1 wk to 1 yr. A) Regions of interest (ROIs) containing mistues of different tissue types used in estimation of ADC variability.
1 Sugahara, JMRI, 1999; 2 Lyng, MRM, 2000; 3 Chenevert, JNCI, 2000; 4 Hayashida, AJNR, 2006; 5 Manenti, Radiol Med, 2008; 6 Gauvain, AJR, 2001; 7 Chenevert, Clin Cancer Res, 1997; 8 Moffat, Proc Nat Acad Sci, 2005; 9 Moffat, Neoplasia, 2006; 10 Hamstra, JCO, 2008; 11 Hamstra, Proc Nat Acad Sci, 2005.
Funding support was provided by NIH/NCI R01CA082500,
NIH/NCI R21 CA109280, MCW Translational Brain Tumor Program, Advancing a Healthier Wisconsin and a Fellowship from the MCW Cancer Center.