Comparison of the Effect of Vessel Size Imaging and Cerebral Blood Volume Derived from Perfusion MR Imaging on Glioma Grading

Patients and Samples

We selected patients with suspected primary gliomas followed by undergoing prospective perfusion-weighted imaging between March 2013 and August 2014. Then, tumors were diagnosed and classified according to World Health Organization 2007 criteria as confirmed by surgery. Among the selected patients, at least 30 had undergone both SE-perfusion and VSI at 24- to 48-hour intervals; these patients were assigned to group 1, from which the optimal cutoff value of VSI and rCBV to distinguish HGG from LGG would be generated, respectively. Once the optimal cutoff values were obtained, we needed to evaluate their diagnostic accuracy rates in the grading of gliomas. Thus, we designed protocols for the enrollment of, at minimum, 40 patients with suspected primary gliomas, followed by those who underwent SE-perfusion (20 patients), assigned to group 2, or VSI (20 patients), assigned to group 3 (On-line Table 1). In our next step, we used the optimal cutoff values of rCBV and VSI to predict the glioma grading of patients in groups 2 and 3, prospectively. Finally, diagnostic accuracy was calculated by comparing calculated values with pathologic results.

The inclusion criteria were the following: 1) MR imaging examination before the histologic examination; 2) no therapy before the MR imaging examination; 3) gliomas confirmed histologically; and 4) patients in group 1, whose clinical symptoms occurred in lesser degrees, able to undergo both SE perfusion and VSI and agreeing to undergo perfusion twice. The exclusion criteria were the following: 1) patients older than 80 years of age; 2) gliomas with grade I and histologic results showing no gliomas or rare gliomas; 3) in group 1, patients not having enough time (>24 hours) to have both SE perfusion and VSI before undergoing their operation; 4) contraindications to MR imaging; and 5) patients having false teeth, which would result in poor image quality. The study was approved by the local ethics committee at our institution, and all patients completed a written consent form.

MR Imaging

A 3T scanner (Magnetom Verio; Siemens, Erlangen, Germany) was used to acquire conventional MR imaging and DWI (details shown in the On-line Appendix) and spin-echo echo-planar perfusion scans. VSI was performed by using a 1.5T imager (Sigma HDx; GE Healthcare, Milwaukee, Wisconsin). The location line for all axial MR imaging was the central intercommissural (anterior/posterior commissure) line as previously described^13,20 or parallel to this line.

Dynamic susceptibility contrast-enhanced perfusion MR imaging was performed by using the SE-EPI technique during administration of Gd-DTPA (Magnevist; Bayer HealthCare Pharmaceuticals, Wayne, New Jersey). The imaging parameters were as follows: TR/TE, 1500/30 ms; flip angle, 90°; matrix size, 128 × 128; NEX, 1.0; FOV, 23 × 23 cm. We selected 20 sections for perfusion imaging, in accordance with the T2-weighted imaging; at each section, 60 images were obtained. After 8 acquisitions, a bolus of gadobutrol (0.2 mmol per kilogram of body weight) was injected at a rate of 3 mL/s, immediately followed by a 20-mL bolus of saline at the same rate.

MR imaging data for VSI were acquired followed by intravenous injection of Gd-DTPA by using a gradient-echo and spin-echo sequence, with the following acquisition parameters: TR, 1500 ms; TE (gradient echo), 30 ms; TE (SE), 100 ms; flip angle, 90°; matrix size, 64 × 64; NEX, 1; FOV, 24 × 24 cm. Seven sections were selected across the tumor, with 120 images obtained at each section. In addition, VSI was obtained during injection of a bolus of 0.2 mmol/kg body weight of gadopentetate dimeglumine contrast agent at a rate of 3.5 mL/s with a time delay of 18 seconds, followed by a 20-mL bolus of saline.

VSI Measurements

The VSI images were analyzed by using an Advantage Workstation (Version 4.9; GE Healthcare) equipped with a dedicated software package (VSI; GE Healthcare). Using wide-bound or whole-observed brain as the input function and the generation of contrast-enhancement time–signal intensity curves for the input function, we calculated ▵R2*/▵R2 according to the decay curve. On the basis of the known observation that ▵R2*/▵R2 is a function of vessel size,²¹ VSI can be computed from this ratio according to the equation^4,19: VSI = 0.425(ADC/γ▵ × B0)^1/2× (ΔR2*/ΔR2)^3/2, where ADC is the diffusion coefficient, by using 0.8 μm²/ms as analog data¹⁸; γ is the gyromagnetic ratio, the gyromagnetic ratio of hydrogen proton being 42.58 MHz/T; Δγ is the change values in magnetization rate; and B0 is the stationary field, 1.5T. The software (VSI) generated color-coded VSI images, the threshold of which was adjusted to 0–80 μm or 0–120 μm.

The preoperative and postoperative MR images were compared to outline the extent of surgery to increase the consistency between the regions of MR imaging and pathology measurements. A hand-drawn ROI of constant size (56 mm²) corresponding to the greatest visualized regions of perfusion was achieved by repeatedly moving the region in adjacent parts at least 5 times until the highest value was determined. We obtained approximately 10 values from at least 3 regions of the different cross-sectional images, while avoiding the vessels and necrotic tissue, recording the mean and maximal (max) values of all data as the VSI_mean and VSI_max values for each patient. Measurements were performed by 2 neuroradiologists (H.-Y.K. and J.-Q.F, with 7 and 6 years of experience, respectively). Each observer, who was blinded to the histopathologic diagnosis, conducted each measurement twice and independently.

rCBV Measurements

The original SE images were processed with a commercial software package (SyngoMMWP VE36A; Siemens) and were used to generate color-coded CBV maps.^3,22,23 The enhanced T1-weighted images were used as a guide for the location of the tumors to avoid the large vessels and necrotic tissue. The highest CBV values were obtained, and the rCBV_max normalization to the contralateral unaffected white matter CBV value was based on previously published methods.^1,23,24 Measurements were performed by 2 neuroradiologists (H.-Y.K. and J.-Q.F). Each ROI was independently checked for accuracy by another observer, and any changes were made by joint agreement. The size of the ROIs was kept constant (radius = 2.6 mm). All analyses were performed without knowledge of tissue analyses.

Immunohistology Analysis

Each paraffin block from the 30 patients of group 1 was processed into a 4-μm-thick section, with 2–6 paraffin sections produced per patient. To measure the diameter of microvessels, microvessel area (MVA), and microvessel density (MVD) for grades II, III, or IV gliomas, we performed a histologic evaluation by immunohistochemistry for the CD34 antigen, which identified vascular endothelial cells as previously described.^3,25 The reagent PV-6000-G Polymer Detection System for Immuno-Histologic Staining was bought from Beijing Zhong Shan-Golden Bridge Biologic Technology Company (Beijing, China). All tissue sections were digitized by using a fluorescence microscope (BX41; Olympus, Rungis, France) and the CellSens Standard software (http://www.scientific-computing.com/press-releases/product_details.php?product_id=809).

We examined entire sections at ×40 magnification, covering an area of 1.308 × 1.757 mm/field, to identify “hot spot” regions of microvascular diameter, MVA, and MVD, from which 5–10 hot spots were selected for imaging.²⁶ By definition, the smallest countable blood vessels were used to estimate microvessel parameters.²⁷ Microvascular diameter and MVA were measured at ×200 magnification, and MVD, at ×100 magnification.²⁸ Quantitative microvascular analysis was performed by using Image-Pro Plus 5.0 software (http://www.mediacy.com/index.aspx?page=IPP), the microvessel diameter and MVA were determined by using the feret (min) and area (polygon) function,³ but the vessels with thick walls and lumens >72 μm were excluded.²⁷ To minimize bias in the correlation between MR imaging and the histologic estimates, we analyzed the average diameter of all microvessels, MVD, and total area of all hot spot regions of each tissue section, and we compared sections from each patient to identify the highest values. Two experienced neuropathologists (H.-L.X and S.-G.F., with >10 years of experience) were blinded to the MR imaging results and completed the histologic measurements.

Statistical Analysis

Statistical analyses were performed by using commercially available software (SPSS, Version 19.0; IBM, Armonk, New York) to compare the MR imaging and pathologic data of different grades of glioma from patient group 1. Due to part of the data not being well-represented by a normal distribution, the results have been expressed as mean ± SD or median ± quartile range. The Kruskal-Wallis H test was used to evaluate differences in the VSI_mean values, microvascular diameter, MVA, and MVD in patients with glioma of different grades, and the Nemenyi method of the multiple comparison was performed. Multiple comparisons between groups were corrected by using the Bonferroni method. The ANOVA and the Least Significant Difference test were used to analyze differences in VSI_max and rCBV_max values in patients with glioma of different grades. P values < .05 were statistically significant. Pearson correlation analysis was used to evaluate the relationship among VSI values, rCBV_max values, microvascular diameter, MVA, and MVD. Receiver operating characteristic curves were used to assess the specificity and sensitivity of the VSI_max, VSI_mean, and rCBV_max values in distinguishing LGG from HGG. Interrater reliability between the 2 independent VSI observers and the two measurements of interobserver was assessed by using an intraclass correlation coefficient.