Measurements of diagnostic examination performance and correlation analysis using microvascular leakage, cerebral blood volume, and blood flow derived from 3T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in glial tumor grading.

INTRODUCTION: To assess the diagnostic accuracy of microvascular leakage (MVL), cerebral blood volume (CBV) and blood flow (CBF) values derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging (DSC-MR imaging) for grading of cerebral glial tumors, and to estimate the correlation between vascular permeability/perfusion parameters and tumor grades. METHODS: A prospective study of 79 patients with cerebral glial tumors underwent DSC-MR imaging. Normalized relative CBV (rCBV) and relative CBF (rCBF) from tumoral (rCBVt and rCBFt), peri-enhancing region (rCBVe and rCBFe), and the value in the tumor divided by the value in the peri-enhancing region (rCBVt/e and rCBFt/e), as well as MVL, expressed as the leakage coefficient K(2) were calculated. Hemodynamic variables and tumor grades were analyzed statistically and with Pearson correlations. Receiver operating characteristic (ROC) curve analyses were also performed for each of the variables. RESULTS: The differences in rCBVt and the maximum MVL (MVL(max)) values were statistically significant among all tumor grades. Correlation analysis using Pearson was as follows: rCBVt and tumor grade, r = 0.774; rCBFt and tumor grade, r = 0.417; MVL(max) and tumor grade, r = 0.559; MVL(max) and rCBVt, r = 0.440; MVL(max) and rCBFt, r = 0.192; and rCBVt and rCBFt, r = 0.605. According to ROC analyses for distinguishing tumor grade, rCBVt showed the largest areas under ROC curve (AUC), except for grade III from IV. CONCLUSION: Both rCBVt and MVL(max) showed good discriminative power in distinguishing all tumor grades. rCBVt correlated strongly with tumor grade; the correlation between MVL(max) and tumor grade was moderate.

Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas.

PURPOSE: Tumor grading is very important both in treatment decision and evaluation of prognosis. While tissue samples are obtained as part of most therapeutic approaches, factors that may result in inaccurate grading due to sampling error (namely, heterogeneity in tissue sampling, as well as tumor-grade heterogeneity within the same tumor specimen), have led to a desire to use imaging better to ascertain tumor grade. The purpose in our study was to evaluate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and accuracy of diffusion-weighted MR imaging (DWI), proton MR spectroscopic imaging (MRSI) or both in grading primary cerebral gliomas. MATERIALS AND METHODS: We performed conventional MR imaging (MR), DWI, and MRSI in 74 patients with newly diagnosed brain gliomas: 59 patients had histologically verified high-grade gliomas: 37 glioblastomas multiform (GBM) and 22 anaplastic astrocytomas (AA), and 15 patients had low-grade gliomas. Apparent diffusion coefficient (ADC) values of tumor and peritumoral edema, and ADC ratios (ADC in tumor or peritumoral edema to ADC of contralateral white matter, as well as ADC in tumor to ADC in peritumoral edema) were determined from three regions of interest. The average of the mean, maximum, and minimum for ADC variables was calculated for each patient. The metabolite ratios of Cho/Cr and Cho/NAA at intermediate TE were assessed from spectral maps in the solid portion of tumor, peritumoral edema and contralateral normal-appearing white matter. Tumor grade determined with the two methods was then compared with that from histopathologic grading. Logistic regression and receiver operating characteristic (ROC) curve analysis were performed to determine optimum thresholds for tumor grading. Measures of diagnostic examination performance, such as sensitivity, specificity, PPV, NPV, AUC, and accuracy for identifying high-grade gliomas were also calculated. RESULTS: Statistical analysis demonstrated a threshold minimum ADC tumor value of 1.07 to provide sensitivity, specificity, PPV, and NPV of 79.7%, 60.0%, 88.7%, and 42.9% respectively, in determining high-grade gliomas. Threshold values of 1.35 and 1.78 for peritumoral Cho/Cr and Cho/NAA metabolite ratios resulted in sensitivity, specificity, PPV, and NPV of 83.3%, 85.1%, 41.7%, 97.6%, and 100%, 57.4%, 23.1% and 100% respectively for determining high-grade gliomas. Significant differences were noted in the ADC tumor values and ratios, peritumoral Cho/Cr and Cho/NAA metabolite ratios, and tumoral Cho/NAA ratio between low- and high-grade gliomas. The combination of mean ADC tumor value, maximum ADC tumor ratio, peritumoral Cho/Cr and Cho/NAA metabolite ratios resulted in sensitivity, specificity, PPV, and NPV of 91.5%, 100%, 100% and 60% respectively. CONCLUSION: Combining DWI and MRSI increases the accuracy of preoperative imaging in the determination of glioma grade. MRSI had superior diagnostic performance in predicting glioma grade compared with DWI alone. The predictive values are helpful in the clinical decision-making process to evaluate the histologic grade of tumors, and provide a means of guiding treatment.

Three-dimensional balanced steady state free precession imaging of the prostate: flip angle dependency of the signal based on a two component T2-decay model.

PURPOSE: To investigate the contrast of three-dimensional balanced steady state free precession (3D bSSFP) in the two component T2 model and to apply the results to optimize 3D bSSFP for prostate imaging at 1.5 Tesla. MATERIALS AND METHODS: In each of seven healthy volunteers, six 3D bSSFP acquisitions were performed with flip angles (alpha) equally spaced between 10 degrees and 110 degrees . Predictions of signal and contrast were obtained from synthetic bSSFP images calculated from relaxation parameters obtained from a multi-spin-echo acquisition. One biexponential and two monoexponential models were applied. Measured and predicted signals were compared by simple linear regression. RESULTS: The measured contrast to signal ratio increased continuously with alpha. Mean R(2) for the biexponential model was almost constant for alpha in the range 50-110 degrees . The biexponential model was a better predictor of the measured signal than the monoexponential model. A monoexponential model restricted to the echoes TE = 50-125 ms performed similar to the biexponential model. The predicted contrast peaked at alpha between 50 degrees and 90 degrees . CONCLUSION: Prostate imaging with bSSFP benefited from high flip angles. The biexponential model provided good signal prediction while predictions from the monoexponential models are dependent on the range of TE used for T2 determination.

Prostate magnetic resonance imaging: multiexponential T2 decay in prostate tissue.

PURPOSE: To investigate the T2 decay in prostate tissue for multiexponentiality and to assess how the biexponential model relates to established T2W contrast. MATERIALS AND METHODS: A 32-echo spin-echo sequence was performed on 16 volunteers. Six single-voxel decay curves were sampled from each prostate. Prediction accuracies were assessed by jackknifing for the mono-, bi-, and triexponential models. The differences were evaluated by cross-validated analysis of variance (CVANOVA). Multiple linear regression was performed to assess the relation between parameters in the biexponential model and the contrast in T2W images. RESULTS: Mono-, bi-, and triexponential models were preferred in 8 (10%), 72 (86%), and 4 (5%) cases, respectively. The biexponential short T2 was 64 msec (range 43 to 92 msec) and the long T2 was 490 msec (range 161 to 1319 msec). The fitted signal fraction, f, of the long T2 component was 27% (range 3% to 80%). The adjusted R(2) was 75.1% for the full regression model and decreased by 0.9%, 1.3%, and 39.2% when short T2, long T2, and f were removed from the model, respectively. CONCLUSION: Prostatic T2 decay was, in general, biexponential. The differences between the T2 components were large enough for accurate quantification. The T2W image contrast was primarily predicted by the biexponential signal fractions.