Dynamic Susceptibility Contrast MR Imaging Research Articles

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4)

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is likely to occur within surrounding non-enhancing T2 hyperintense areas even after gross total resection of contrast-enhancing lesions. Cerebral blood volume (CBV) obtained from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) is a parameter that is well-known to be a surrogate marker of both histologic and angiographic vascularity in tumors. We built two nnU-Net deep learning models for prediction of early local progression in adult-type diffuse glioma (grade 4), one using conventional MRI alone and one using multiparametric MRI, including conventional MRI and DSC-PWI. Local progression areas were annotated in a non-enhancing T2 hyperintense lesion on preoperative T2 FLAIR images, using the follow-up contrast-enhanced (CE) T1-weighted (T1W) images as the reference standard. The sensitivity was doubled with the addition of nCBV (80% vs. 40%, P = 0.02) while the specificity was decreased nonsignificantly (29% vs. 48%, P = 0.39), suggesting that fewer cases of early local progression would be missed with the addition of nCBV. While the diagnostic performance of CBV model is still poor and needs improving, the multiparametric deep learning model, which presumably learned from the subtle difference in vascularity between early local progression and non-progression voxels within perilesional T2 hyperintensity, may facilitate risk-adapted radiotherapy planning in adult-type diffuse glioma (grade 4) patients.

Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma.

An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoencoder-based pattern analysis could provide interpretable tissue labeling and prognostic value in isocitrate dehydrogenase (IDH) wild-type glioblastoma. Preoperative dynamic susceptibility contrast MR images were obtained from 272 patients with IDH wild-type glioblastoma (training and validation, 183 and 89 patients, respectively). The autoencoder was applied to the dynamic susceptibility contrast MR imaging time-signal intensity curves of tumor and peritumoral areas. Representative perfusion patterns were defined by voxelwise K-means clustering using autoencoder latent features. Perfusion patterns were labeled by comparing parameters with anatomic reference tissues for baseline, signal drop, and percentage recovery. In the validation set (n = 89), a survival model was created from representative patterns and clinical predictors using Cox proportional hazard regression analysis, and its performance was calculated using the Harrell C-index. Eighty-nine patients were enrolled. Five representative perfusion patterns were used to characterize tissues as high angiogenic tumor, low angiogenic/cellular tumor, perinecrotic lesion, infiltrated edema, and vasogenic edema. Of these, the low angiogenic/cellular tumor (hazard ratio, 2.18; P = .047) and infiltrated edema patterns (hazard ratio, 1.88; P = .009) in peritumoral areas showed significant prognostic value. The combined perfusion patterns and clinical predictors (C-index, 0.72) improved prognostication when added to clinical predictors (C-index, 0.55). The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.

Arterial spin labeling compared to dynamic susceptibility contrast MR perfusion imaging for assessment of ischemic penumbra: A systematic review.

Dynamic susceptibility contrast (DSC) MR imaging is commonly used to estimate penumbra size in acute ischemic stroke; this technique relies on the administration of gadolinium contrast, which has limited use in certain populations, such as those with impaired renal function or allergies. Arterial spin labeling (ASL) is a relatively new technique that can provide information on cerebral perfusion without need for exogenous contrast agents. This systematic review examines published studies that specifically compared ASL to DSC for assessment of ischemic penumbra. We searched PubMed, Embase, Web of Science, and the Cochrane Library for papers which compared ASL with DSC for assessment of ischemic penumbra in acute ischemic stroke among adult human populations. Two independent reviewers screened studies using predefined inclusion and exclusion criteria. Study characteristics and findings regarding the utility of ASL compared to DSC for identification of penumbra were then extracted and anlyzed for results and risk of bias. Seventeen articles met inclusion and exclusion criteria. Studies compared ASL with DSC on a range of metrics (hypoperfusion, hyperperfusion, mismatch, and reperfusion). Most studies concluded that agreement of ASL with DSC was moderate to very high. A small subset of studies found discrepancy in agreement of ASL with DSC for size or location of perfusion abnormalities. A heterogeneity of perfusion parameters studied for DSC was noted, along with the need for more standardization of research methods. ASL shows moderate to high agreement with DSC for detection of penumbra among ischemic stroke patients.

Inflow-based vascular-space-occupancy (iVASO) might potentially predict IDH mutation status and tumor grade in diffuse cerebral gliomas

PurposeThe aim of the study is to assess the diagnostic performance of inflow-based vascular-space-occupancy (iVASO) MR imaging for differentiating glioblastomas (grade IV, GBM) and lower-grade diffuse gliomas (grade II and III, LGG) and its potential to predict IDH mutation status. MethodsOne hundred and two patients with diffuse cerebral glioma (56 males; median age, 43.5 years) underwent iVASO and dynamic susceptibility contrast (DSC) MR imaging. The iVASO-derived arteriolar cerebral blood volume (CBVa), relative CBVa (rCBVa), and the DSC-derived relative cerebral blood volume (rCBV) were obtained, and these measurements were compared between the GBM group (n = 43) and the LGG group (n = 59) and between the IDH-mutation group (n = 54) and the IDH-wild group (n = 48). ResultsSignificant correlation was observed between rCBV and CBVa (P < 0.001) or rCBVa (P < 0.001). Both CBVa (P < 0.001) and rCBVa (P < 0.001) were higher in the GBM group. Both CBVa (P < 0.001) and rCBVa (P < 0.001) were lower in the IDH-mutation group compared to the IDH-wild group. Receiver operating characteristic analyses showed the area under curve (AUC) of 0.95 with CBVa and 0.97 with rCBVa in differentiating GBM from LGG. The AUCs were 0.82 and 0.85 for CBVa and rCBVa in predicting IDH gene status, respectively, which were lower than that of rCBV (AUC = 0.90). Combined rCBV and rCBVa significantly improved the diagnostic performance (AUC = 0.95). ConclusionsiVASO MR imaging has the potential to predict IDH mutation and grade in glioma.

Validation study of perfusion parameter in hypervascular hepatocellular carcinoma and focal nodular hyperplasia using dynamic susceptibility magnetic resonance imaging with super-paramagnetic iron oxide: comparison with single level dynamic CT arteriography.

Dynamic susceptibility contrast MR imaging (DSC-MRI) offers direct evaluation of neo-vascularity. Ferucarbotran does not accumulate in the interstitial space, instead remaining in the intravascular space during early phase imaging. We investigate tracer kinetic analysis with DSC-MRI with ferucarbotran and single level CT during hepatic arteriography (SL-CTHA) in assessment of hypervascular hepatocellular lesions and evaluate the usefulness of DSC-MRI with ferucarbotran. Six patients having hypervascular hepatocellular carcinoma (HCC) and 3 patients having focal nodular hyperplasia (FNH) were included in the study. SL-CTHA was performed with the infusion of 3 mL of contrast media at a rate of 1 mL/s and scanned at a rate of 0.8 second per rotation. DSC-MRI was acquired with the echo-planar method at 1.5T system. A total dose of 1.4 mL (0.5 mol Fe/L) of ferucarbotran was used. Ferucarbotran was injected at a rate of 2 mL/s with 40 mL of physiological saline. Imaging was obtained at a temporal resolution of 1.2 or 0.46 seconds in 5 and 4 patients, respectively. For both CT and MRI modalities, a model-free analysis method was used to derive region of interest-based perfusion parameters. Plasma flow, distribution volume (DV) of contrast agent and estimated mean transit time (EMTT) were estimated. A strong correlation was obtained with plasma flow (r=0.8231, P=0.0064) between DSC-MRI and SL-CTHA. No significant correlation was obtained for DV and EMTT between DSC-MRI and SL-CTHA. All perfusion parameters showed no significant difference between SL-CTHA and DSC-MRI in FNH. On the other hand, in HCC, DV and EMTT showed significant differences (P=0.046 and 0.046), and plasma flow showed no significant difference between DSC-MRI and SL-CTHA. This pilot study demonstrates the possibility of quantitative analysis of liver tumor using superparamagnetic iron oxide (SPIO)-based agent and highlights the potential for SPIO-based agent in more precisely assessing the perfusion characteristic of hypervascular liver tumors than by using extracellular contrast media.

Comparative Study of Dynamic Susceptibility Contrast Perfusion MR Images between Warthin&amp;apos;s Tumor and Malignant Parotid Tumors

Objectives To identify diagnostically meaningful differences between Warthin's tumor and malignant masses in the parotid gland by dynamic susceptibility contrast (DSC) MR imaging. Methods Eleven malignant parotid tumors and 9 Warthin's tumors were included. MR imaging was performed on all patients. Signal intensity time curves of tumors were obtained by DSC MR imaging and dynamic susceptibility contrast percentages (DSC%) were calculated. Results No significant difference was observed between malignant tumors and Warthin's tumors (P = 0.437), although DSC% values tended to be higher for Warthin's tumors. Conclusions Warthin's tumor tended to have higher DSC% values than malignant parotid tumors, but this difference was not significantly different.

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.

MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach.

According to the revised World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) of 2016, oligodendrogliomas are now defined primarily by a specific molecular signature (presence of IDH mutation and 1p19q codeletion). The purpose of our study was to assess the value of dynamic susceptibility contrast MR imaging (DSC-MRI) and diffusion-weighted imaging (DWI) to characterize oligodendrogliomas and to distinguish them from astrocytomas. Seventy-one adult patients with untreated WHO grade II and grade III diffuse infiltrating gliomas and known 1p/19q codeletion status were retrospectively identified and analyzed using relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) maps based on whole-tumor volume histograms. The Mann-Whitney U test and logistic regression were used to assess the ability of rCBV and ADC to differentiate between oligodendrogliomas and astrocytomas both independently, but also related to the WHO grade. Prediction performance was evaluated in leave-one-out cross-validation (LOOCV). Oligodendrogliomas showed significantly higher microvascularity (higher rCBVMean ≥ 0.80, p = 0.013) and higher vascular heterogeneity (lower rCBVPeak ≤ 0.044, p = 0.015) than astrocytomas. Diffuse gliomas with higher cellular density (lower ADCMean ≤ 1094 × 10-6mm2/s, p = 0.009) were more likely to be oligodendrogliomas than astrocytomas. Histogram analysis of rCBV and ADC was able to differentiate between diffuse astrocytomas (WHO grade II) and anaplastic astrocytomas (WHO grade III). Histogram-derived rCBV and ADC parameter may be used as biomarkers for identification of oligodendrogliomas and may help characterize diffuse gliomas based upon their genetic characteristics.

Clinical Value of Vascular Permeability Estimates Using Dynamic Susceptibility Contrast MRI: Improved Diagnostic Performance in Distinguishing Hypervascular Primary CNS Lymphoma from Glioblastoma.

A small subset of primary central nervous system lymphomas exhibits high cerebral blood volume, which is indistinguishable from that in glioblastoma on dynamic susceptibility contrast MR imaging. Our study aimed to test whether estimates of combined perfusion and vascular permeability metrics derived from DSC-MR imaging can improve the diagnostic performance in differentiating hypervascular primary central nervous system lymphoma from glioblastoma. A total of 119 patients (with 30 primary central nervous system lymphomas and 89 glioblastomas) exhibited hypervascular foci using the reference method of leakage-corrected CBV (reference-normalized CBV). An alternative postprocessing method used the tissue residue function to calculate vascular permeability (extraction fraction), leakage-corrected CBV, cerebral blood flow, and mean transit time. Parameters were compared using Mann-Whitney U tests, and the diagnostic performance to distinguish primary central nervous system lymphoma from glioblastoma was calculated using the area under the curve from the receiver operating characteristic curve and was cross-validated with bootstrapping. Hypervascular primary central nervous system lymphoma showed similar leakage-corrected normalized CBV and leakage-corrected CBV compared with glioblastoma (P > .05); however, primary central nervous system lymphoma exhibited a significantly higher extraction fraction (P < .001) and CBF (P = .01) and shorter MTT (P < .001) than glioblastoma. The extraction fraction showed the highest diagnostic performance (the area under the receiver operating characteristic curve [AUC], 0.78; 95% confidence interval, 0.69-0.85) for distinguishing hypervascular primary central nervous system lymphoma from glioblastoma, with a significantly higher performance than both CBV (AUC, 0.53-0.59, largest P = .02) and CBF (AUC, 0.72) and MTT (AUC, 0.71). Estimation of vascular permeability with DSC-MR imaging further characterizes hypervascular primary central nervous system lymphoma and improves diagnostic performance in glioblastoma differentiation.

What Have We Learned from Perfusion MRI in Multiple Sclerosis?

Using MR imaging, perfusion can be assessed either by dynamic susceptibility contrast MR imaging or arterial spin-labeling. Alterations of cerebral perfusion have repeatedly been described in multiple sclerosis compared with healthy controls. Acute lesions exhibit relative hyperperfusion in comparison with normal-appearing white matter, a finding mostly attributed to inflammation in this stage of lesion development. In contrast, normal-appearing white and gray matter of patients with MS has been mostly found to be hypoperfused compared with controls, and correlations with cognitive impairment as well as fatigue in multiple sclerosis have been described. Mitochondrial failure, axonal degeneration, and vascular dysfunction have been hypothesized to underlie the perfusion MR imaging findings. Clinically, perfusion MR imaging could allow earlier detection of the acute focal inflammatory changes underlying relapses and new lesions, and could constitute a marker for cognitive dysfunction in MS. Nevertheless, the clinical relevance and pathogenesis of the brain perfusion changes in MS remain to be clarified.

Differentiating between Central Nervous System Lymphoma and High-grade Glioma Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced MR Imaging with Histogram Analysis.

Purpose:We evaluated the diagnostic performance of histogram analysis of data from a combination of dynamic susceptibility contrast (DSC)-MRI and dynamic contrast-enhanced (DCE)-MRI for quantitative differentiation between central nervous system lymphoma (CNSL) and high-grade glioma (HGG), with the aim of identifying useful perfusion parameters as objective radiological markers for differentiating between them.Methods:Eight lesions with CNSLs and 15 with HGGs who underwent MRI examination, including DCE and DSC-MRI, were enrolled in our retrospective study. DSC-MRI provides a corrected cerebral blood volume (cCBV), and DCE-MRI provides a volume transfer coefficient (Ktrans) for transfer from plasma to the extravascular extracellular space. Ktrans and cCBV were measured from a round region-of-interest in the slice of maximum size on the contrast-enhanced lesion. The differences in t values between CNSL and HGG for determining the most appropriate percentile of Ktrans and cCBV were investigated. The differences in Ktrans, cCBV, and Ktrans/cCBV between CNSL and HGG were investigated using histogram analysis. Receiver operating characteristic (ROC) analysis of Ktrans, cCBV, and Ktrans/cCBV ratio was performed.Results:The 30th percentile (C30) in Ktrans and 80th percentile (C80) in cCBV were the most appropriate percentiles for distinguishing between CNSL and HGG from the differences in t values. CNSL showed significantly lower C80 cCBV, significantly higher C30 Ktrans, and significantly higher C30 Ktrans/C80 cCBV than those of HGG. In ROC analysis, C30 Ktrans/C80 cCBV had the best discriminative value for differentiating between CNSL and HGG as compared to C30 Ktrans or C80 cCBV.Conclusion:The combination of Ktrans by DCE-MRI and cCBV by DSC-MRI was found to reveal the characteristics of vascularity and permeability of a lesion more precisely than either Ktrans or cCBV alone. Histogram analysis of these vascular microenvironments enabled quantitative differentiation between CNSL and HGG.

Defining the endpoints: how to measure the efficacy of drugs that are active against central nervous system metastases.

Brain metastases (BMs) are the most common cause of malignant central nervous system (CNS) tumors in adults. In the recent past, patients with BMs were excluded from clinical trials, but now, with the advent of new biological and immunological drugs, their inclusion is more common. In the last era response and progression criteria used across clinical trials have defined the importance to consider not only measurement changes of brain lesions but also the modification of parameters related to the metastases such as metabolism of tissue and its pathological features. Magnetic resonance imaging (MRI) represents the first choice in the evaluation of BMs; the computed tomography (CT) scan will be made only in case of MRI's contraindication. CT, MRI and positron emission tomography (PET-CT), may be used to monitor response to treatment as part of clinical and radiological follow up. In the evaluation of response to treatment, MRI shows superior accuracy in comparison to CT; PET-CT is useful in particularly in cases of BMs underwent to locoregional therapies in the differential diagnosis between recurrence or radionecrosis. Now is possible to use functional imaging as CT-perfusion, dynamic susceptibility contrast (DSC) MR imaging, dynamic contrast-enhanced (DCE) MR imaging, diffusion-weighted MR imaging and MR-Spectroscopy in the evaluation of treatment response; these imaging techniques can provide qualitative and quantitative imaging parameters that allow pathophysiologic correlation. In the evaluation of the response to immunotherapy treatments, the immune-related response criteria (irRC) are considered as the gold standard. The irRC utilizes bidimensional measurements, quantifying the tumor dimension using a product of the longest diameter and the longest perpendicular diameter. We analyze clinical and radiological criteria to better define outcome of drugs for BMs from solid tumors in the new era of biological and immunological therapies.

Improving Perfusion Measurement in DSC-MR Imaging with Multiecho Information for Arterial Input Function Determination.

Clinical measurements of cerebral perfusion have been increasingly performed with multiecho dynamic susceptibility contrast-MR imaging techniques due to their ability to remove confounding T1 effects of contrast agent extravasation from perfusion quantification. However, to this point, the extra information provided by multiecho techniques has not been used to improve the process of estimating the arterial input function, which is critical to accurate perfusion quantification. The purpose of this study is to investigate methods by which multiecho DSC-MRI data can be used to automatically avoid voxels whose signal decreases to the level of noise when calculating the arterial input function. Here we compare postprocessing strategies for clinical multiecho DSC-MR imaging data to test whether arterial input function measures could be improved by automatically identifying and removing voxels exhibiting signal attenuation (truncation) artifacts. In a clinical pediatric population, we found that the Pearson correlation coefficient between ΔR2* time-series calculated from each TE individually was a valuable criterion for automated estimation of the arterial input function, resulting in higher peak arterial input function values while maintaining smooth and reliable arterial input function shapes. This work is the first to demonstrate that multiecho information may be useful in clinically important automatic arterial input function estimation because it can be used to improve automatic selection of voxels from which the arterial input function should be measured.

Grading of Glioma Tumors by Analysis of Minimum Apparent Diffusion Coefficient and Maximum Relative Cerebral Blood Volume

Article type: Original Article Background: Gliomas are the most common primary neoplasms of the central nervous system. Relative cerebral blood volume (rCBV) could estimate high-grade Gliomas computed with dynamic susceptibility contrast MR imaging which it is artificially lowered by contrast extravasation through a disrupted blood-brain barrier. Objectives: Our intent was to clarify the usefulness of diffusion-weighted magnetic resonance imaging (DWI) and perfusion weighted magnetic resonance imaging (PWI) in the grading of Gliomas. Materials and Methods: Both PWI and DWI with a three-tesla scanner investigated nineteen consecutive patients with Gliomas. The means of rCBV and ADC values have been compared among the tumor groups with t-test and ROC curve analysis to determine threshold values of Gliomas grading. Results: Mean maximum rCBV were 2.71±1.41 for low grades (I & II), and 8.14±2.58 for high grades (III & IV) Gliomas (p=0.001). Mean minimum ADC were 1.47±.46 ×10 3 mm 2 /s for low grades (I & II), and .47±.38×10 3 mm 2 /s for high grades (III & IV) Gliomas (p=0.001). We can get 0.94×10 3 mm 2 /s for minimum ADC and 3.85 for maximum rCBV as a difference cutoff point between low and high-grade Gliomas. Conclusion: Combination of both DWI and PWI techniques, with measurement of minimum ADC and maximum rCBV can be used to distinguish between high grade and low-grade Glioma tumors.

Mapping of cerebral metabolic rate of oxygen using dynamic susceptibility contrast and blood oxygen level dependent MR imaging in acute ischemic stroke.

MR-derived cerebral metabolic rate of oxygen utilization (CMRO(2)) has been suggested to be analogous to PET-derived CMRO(2) and therefore may be used for detection of viable tissue at risk for infarction. The purpose of this study was to evaluate MR-derived CMRO(2) mapping in acute ischemic stroke in relation to established diffusion- and perfusion-weighted imaging. In 23 patients (mean age 63 ± 18.7 years, 11 women) with imaging findings for acute ischemic stroke, relative oxygen extraction fraction was calculated from quantitative transverse relaxation times (T2, T2*) and relative cerebral blood volume using a quantitative blood oxygenation level dependent (BOLD) approach in order to detect a local increase of deoxyhemoglobin. Relative CMRO(2) (rCMRO(2)) maps were calculated by multiplying relative oxygen extraction fraction (rOEF) by cerebral blood flow, derived from PWI. After co-registration, rCMRO(2) maps were evaluated in comparison with apparent diffusion coefficient (ADC) and time-to-peak (TTP) maps. Mean rCMRO(2) values in areas with diffusion-restriction or TTP/ADC mismatch were compared with rCMRO(2) values in the contralateral tissue. In tissue with diffusion restriction, mean rCMRO(2) values were significantly decreased compared to perfusion-impaired (17.9 [95 % confidence interval 10.3, 25.0] vs. 58.1 [95 % confidence interval 50.1, 70.3]; P < 0.001) and tissue in the contralateral hemisphere (68.2 [95 % confidence interval 61.4, 75.0]; P < 0.001). rCMRO(2) in perfusion-impaired tissue showed no significant change compared to tissue in the contralateral hemisphere (58.1 [95 % confidence interval 50.1, 70.3] vs. 66.7 [95 % confidence interval 53.4, 73.4]; P = 0.34). MR-derived CMRO(2) was decreased within diffusion-restricted tissue and stable within perfusion-impaired tissue, suggesting that this technique may be adequate to reveal different pathophysiological stages in acute stroke.

Repeatability of Standardized and Normalized Relative CBV in Patients with Newly Diagnosed Glioblastoma.

For more widespread clinical use advanced imaging methods such as relative cerebral blood volume must be both accurate and repeatable. The aim of this study was to determine the repeatability of relative CBV measurements in newly diagnosed glioblastoma multiforme by using several of the most commonly published estimation techniques. The relative CBV estimates were calculated from dynamic susceptibility contrast MR imaging in double-baseline examinations for 33 patients with treatment-naïve and pathologically proved glioblastoma multiforme (men = 20; mean age = 55 years). Normalized and standardized relative CBV were calculated by using 6 common postprocessing methods. The repeatability of both normalized and standardized relative CBV, in both tumor and contralateral brain, was examined for each method with metrics of repeatability, including the repeatability coefficient and within-subject coefficient of variation. The minimum sample size required to detect a parameter change of 10% or 20% was also determined for both normalized relative CBV and standardized relative CBV for each estimation method. When ordered by the repeatability coefficient, methods using postprocessing leakage correction and ΔR2*(t) techniques offered superior repeatability. Across processing techniques, the standardized relative CBV repeatability in normal-appearing brain was comparable with that in tumor (P = .31), yet inferior in tumor for normalized relative CBV (P = .03). On the basis of the within-subject coefficient of variation, tumor standardized relative CBV estimates were less variable (13%-20%) than normalized relative CBV estimates (24%-67%). The minimum number of participants needed to detect a change of 10% or 20% is 118-643 or 30-161 for normalized relative CBV and 109-215 or 28-54 for standardized relative CBV. The ΔR2* estimation methods that incorporate leakage correction offer the best repeatability for relative CBV, with standardized relative CBV being less variable and requiring fewer participants to detect a change compared with normalized relative CBV.

Prediction of Response to Concurrent Chemoradiotherapy with Temozolomide in Glioblastoma: Application of Immediate Post-Operative Dynamic Susceptibility Contrast and Diffusion-Weighted MR Imaging.

ObjectiveTo determine whether histogram values of the normalized apparent diffusion coefficient (nADC) and normalized cerebral blood volume (nCBV) maps obtained in contrast-enhancing lesions detected on immediate post-operative MR imaging can be used to predict the patient response to concurrent chemoradiotherapy (CCRT) with temozolomide (TMZ).Materials and MethodsTwenty-four patients with GBM who had shown measurable contrast enhancement on immediate post-operative MR imaging and had subsequently undergone CCRT with TMZ were retrospectively analyzed. The corresponding histogram parameters of nCBV and nADC maps for measurable contrast-enhancing lesions were calculated. Patient groups with progression (n = 11) and non-progression (n = 13) at one year after the operation were identified, and the histogram parameters were compared between the two groups. Receiver operating characteristic (ROC) analysis was used to determine the best cutoff value for predicting progression. Progression-free survival (PFS) was determined with the Kaplan-Meier method and the log-rank test.ResultsThe 99th percentile of the cumulative nCBV histogram (nCBV C99) on immediate post-operative MR imaging was a significant predictor of one-year progression (p = 0.033). ROC analysis showed that the best cutoff value for predicting progression after CCRT was 5.537 (sensitivity and specificity were 72.7% and 76.9%, respectively). The patients with an nCBV C99 of < 5.537 had a significantly longer PFS than those with an nCBV C99 of ≥ 5.537 (p = 0.026).ConclusionThe nCBV C99 from the cumulative histogram analysis of the nCBV from immediate post-operative MR imaging may be feasible for predicting glioblastoma response to CCRT with TMZ.

Focal hemodynamic patterns of status epilepticus detected by susceptibility weighted imaging (SWI).

To investigate pathological findings in the susceptibility weighted imaging (SWI) of patients experiencing convulsive (CSE) or non-convulsive status epilepticus (NCSE) with focal hyperperfusion in the acute setting. Twelve patients (six with NCSE confirmed by electroencephalogram (EEG) and six patients with CSE with seizure event clinically diagnosed) underwent MRI in this acute setting (mean time between onset of symptoms and MRI was 3h 8min), including SWI, dynamic susceptibility contrast MR imaging (DSC) and diffusion-weighted imaging (DWI). MRI sequences were retrospectively evaluated and compared with EEG findings (10/12 patients), and clinical symptoms. Twelve out of 12 (100%) patients showed a focal parenchymal area with pseudo-narrowed cortical veins on SWI, associated with focal hyperperfused areas (increased cerebral blood flow (CBF) and mean transit time (MTT) shortening), and cortical DWI restriction in 6/12 patients (50%). Additionally, these areas were associated with ictal or postical EEG patterns in 8/10 patients (80%). Most frequent acute clinical findings were aphasia and/or hemiparesis in eight patients, and all of them showed pseudo-narrowed veins in those parenchymal areas responsible for these symptoms. In this study series with CSE and NCSE patients, SWI showed focally pseudo-narrowed cortical veins in hyperperfused and ictal parenchymal areas. Therefore, SWI might have the potential to identify an ictal region in CSE/NCSE. • The focal ictal brain regions show hyperperfusion in DSC MR-perfusion imaging. • SWI shows focally diminished cortical veins in hyperperfused ictal regions. • SWI has the potential to identify a focal ictal region in CSE/NCSE.

Cerebral Blood Volume Calculated by Dynamic Susceptibility Contrast-Enhanced Perfusion MR Imaging: Preliminary Correlation Study with Glioblastoma Genetic Profiles

PurposeTo evaluate the usefulness of dynamic susceptibility contrast (DSC) enhanced perfusion MR imaging in predicting major genetic alterations in glioblastomas.Materials and MethodsTwenty-five patients (M:F = 13∶12, mean age: 52.1±15.2 years) with pathologically proven glioblastoma who underwent DSC MR imaging before surgery were included. On DSC MR imaging, the normalized relative tumor blood volume (nTBV) of the enhancing solid portion of each tumor was calculated by using dedicated software (Nordic TumorEX, NordicNeuroLab, Bergen, Norway) that enabled semi-automatic segmentation for each tumor. Five major glioblastoma genetic alterations (epidermal growth factor receptor (EGFR), phosphatase and tensin homologue (PTEN), Ki-67, O6-methylguanine-DNA methyltransferase (MGMT) and p53) were confirmed by immunohistochemistry and analyzed for correlation with the nTBV of each tumor. Statistical analysis was performed using the unpaired Student t test, ROC (receiver operating characteristic) curve analysis and Pearson correlation analysis.ResultsThe nTBVs of the MGMT methylation-negative group (mean 9.5±7.5) were significantly higher than those of the MGMT methylation-positive group (mean 5.4±1.8) (p = .046). In the analysis of EGFR expression-positive group, the nTBVs of the subgroup with loss of PTEN gene expression (mean: 10.3±8.1) were also significantly higher than those of the subgroup without loss of PTEN gene expression (mean: 5.6±2.3) (p = .046). Ki-67 labeling index indicated significant positive correlation with the nTBV of the tumor (p = .01).ConclusionWe found that glioblastomas with aggressive genetic alterations tended to have a high nTBV in the present study. Thus, we believe that DSC-enhanced perfusion MR imaging could be helpful in predicting genetic alterations that are crucial in predicting the prognosis of and selecting tailored treatment for glioblastoma patients.

Dynamic susceptibility contrast MR imaging of regional cerebral blood volume in Alzheimer's disease

Dynamic susceptibility contrast MR imaging of regional cerebral blood volume in Alzheimer's disease