Correlations between perfusion MR imaging cerebral blood volume, microvessel quantification, and clinical outcome using stereotactic analysis in recurrent high-grade glioma.

BACKGROUND AND PURPOSE: Quantifying MVA rather than MVD provides better correlation with survival in HGG. This is attributed to a specific "glomeruloid" vascular pattern, which is better characterized by vessel area than number. Despite its prognostic value, MVA quantification is laborious and clinically impractical. The DSC-MR imaging measure of rCBV offers the advantages of speed and convenience to overcome these limitations; however, clinical use of this technique depends on establishing accurate correlations between rCBV, MVA, and MVD, particularly in the setting of heterogeneous vascular size inherent to human HGG. MATERIALS AND METHODS: We obtained preoperative 3T DSC-MR imaging in patients with HGG before stereotactic surgery. We histologically quantified MVA, MVD, and vascular size heterogeneity from CD34-stained 10-µm sections of stereotactic biopsies, and we coregistered biopsy locations with localized rCBV measurements. We statistically correlated rCBV, MVA, and MVD under conditions of high and low vascular-size heterogeneity and among tumor grades. We correlated all parameters with OS by using Cox regression. RESULTS: We analyzed 38 biopsies from 24 subjects. rCBV correlated strongly with MVA (r = 0.83, P < .0001) but weakly with MVD (r = 0.32, P = .05), due to microvessel size heterogeneity. Among samples with more homogeneous vessel size, rCBV correlation with MVD improved (r = 0.56, P = .01). OS correlated with both rCBV (P = .02) and MVA (P = .01) but not with MVD (P = .17). CONCLUSIONS: rCBV provides a reliable estimation of tumor MVA as a biomarker of glioma outcome. rCBV poorly estimates MVD in the presence of vessel size heterogeneity inherent to human HGG.

Optimized preload leakage-correction methods to improve the diagnostic accuracy of dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in posttreatment gliomas.

BACKGROUND AND PURPOSE: Relative cerebral blood volume (rCBV) accuracy can vary substantially depending on the dynamic susceptibility-weighted contrast-enhanced (DSC) acquisition and postprocessing methods, due to blood-brain barrier disruption and resulting T1-weighted leakage and T2- and/or T2*-weighted imaging (T2/T2*WI) residual effects. We set out to determine optimal DSC conditions that address these errors and maximize rCBV accuracy in differentiating posttreatment radiation effect (PTRE) and tumor. MATERIALS AND METHODS: We recruited patients with previously treated high-grade gliomas undergoing image-guided re-resection of recurrent contrast-enhancing MR imaging lesions. Thirty-six surgical tissue samples were collected from 11 subjects. Preoperative 3T DSC used 6 sequential evenly timed acquisitions, each by using a 0.05-mmol/kg gadodiamide bolus. Preload dosing (PLD) and baseline subtraction (BLS) techniques corrected T1-weighted leakage and T2/T2*WI residual effects, respectively. PLD amount and incubation time increased with each sequential acquisition. Corresponding tissue specimen stereotactic locations were coregistered to DSC to measure localized rCBV under varying PLD amounts, incubation times, and the presence of BLS. rCBV thresholds were determined to maximize test accuracy (average of sensitivity and specificity) in distinguishing tumor (n = 21) and PTRE (n = 15) samples under the varying conditions. Receiver operator characteristic (ROC) areas under the curve (AUCs) were statistically compared. RESULTS: The protocol that combined PLD (0.1-mmol/kg amount, 6-minute incubation time) and BLS correction methods maximized test AUC (0.99) and accuracy (95.2%) compared with uncorrected rCBV AUC (0.85) and accuracy (81.0%) measured without PLD and BLS (P = .01). CONCLUSIONS: Combining PLD and BLS correction methods for T1-weighted and T2/T2*WI errors, respectively, enables highly accurate differentiation of PTRE and tumor growth.

Efficient CO oxidation at low temperature on Au(111).

The rate of CO oxidation to CO2 depends strongly on the reaction temperature and characteristics of the oxygen overlayer on Au(111). The factors that contribute to the temperature dependence in the oxidation rate are (1) the residence time of CO on the surface, (2) the island size containing Au-O complexes, and (3) the local properties, including the degree of order of the oxygen layer. Three different types of oxygen--defined as chemisorbed oxygen, a surface oxide, and a bulk oxide--are identified and shown to have different reactivity. The relative populations of the various oxygen species depend on the preparation temperature and the oxygen coverage. The highest rate of CO oxidation was observed for an initial oxygen coverage of 0.5 monolayers that was deposited at 200 K where the density of chemisorbed oxygen is maximized. The rate decreases when two-dimensional islands of the surface oxide are populated and further decreases when three-dimensional bulk gold oxide forms. Our results are significant for designing catalytic processes that use Au for CO oxidation, because they suggest that the most efficient oxidation of CO occurs at low temperature--even below room temperature--as long as oxygen could be adsorbed on the surface.

A comparison of some allele-sharing based linkage analysis methods for detecting complex trait loci.

Using randomly selected sib pairs from a subset of the GAW11 simulated data in Problem 2, we compared the results of some linkage analysis methods based on allele sharing. One method was the Haseman-Elston test for a binary disease outcome (unaffected vs. mild or severe). The other methods, which analyzed the trinary ordered outcome unaffected/mild/severe were the Haseman-Elston test, an extended Haseman-Elston incorporating sib-pair sums, variance components analysis, and regression analysis. Our analysis was done without knowledge of the generating model.

Comparison of evidence for linkage from different analytic methods.

In a randomly chosen replicate of extended pedigrees from GAW10, we conducted robust multipoint genome scans for linkage using a dense marker map. For analysis of the quantitative traits, we selected sibships from the pedigrees, and for analysis of disease status, small families of affected relatives were selected. Lod-score likelihood analyses were conducted in the full pedigrees and in the affected relative families for selected regions. We located a flanking marker for MG1 on chromosome 5, and identified marker regions including MG2, MG4, and MG5 on chromosomes 8 and 9. The analytic methods were consistent for the major gene with a strong effect; false positive errors on chromosomes 1 and 10 could have been eliminated by requiring evidence from more than one method.