Dynamic Susceptibility Contrast-enhanced Magnetic Resonance Research Articles

Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).

Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.

Monitoring Posterior Cerebral Perfusion Changes With Dynamic Susceptibility Contrast-Enhanced Perfusion MRI After Anterior Revascularization Surgery in Pediatric Moyamoya Disease.

To determine whether dynamic susceptibility contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) can be used to evaluate posterior cerebral circulation in pediatric patients with moyamoya disease (MMD) who underwent anterior revascularization. This study retrospectively included 73 patients with MMD who underwent DSC perfusion MRI (age, 12.2 ± 6.1 years) between January 2016 and December 2020, owing to recent-onset clinical symptoms during the follow-up period after completion of anterior revascularization. DSC perfusion images were analyzed using a dedicated software package (NordicICE; Nordic NeuroLab) for the middle cerebral artery (MCA), posterior cerebral artery (PCA), and posterior border zone between the two regions (PCA-MCA). Patients were divided into two groups; the PCA stenosis group included 30 patients with newly confirmed PCA involvement, while the no PCA stenosis group included 43 patients without PCA involvement. The relationship between DSC perfusion parameters and PCA stenosis, as well as the performance of the parameters in discriminating between groups, were analyzed. In the PCA stenosis group, the mean follow-up duration was 5.3 years after anterior revascularization, and visual disturbances were a common symptom. Normalized cerebral blood volume was increased, and both the normalized time-to-peak (nTTP) and mean transit time values were significantly delayed in the PCA stenosis group compared with those in the no PCA stenosis group in the PCA and PCA-MCA border zones. TTPPCA (odds ratio [OR] = 6.745; 95% confidence interval [CI] = 2.665-17.074; P < 0.001) and CBVPCA-MCA (OR = 1.567; 95% CI = 1.021-2.406; P = 0.040) were independently associated with PCA stenosis. TTPPCA showed the highest receiver operating characteristic curve area in discriminating for PCA stenosis (0.895; 95% CI = 0.803-0.986). nTTP can be used to effectively diagnose PCA stenosis. Therefore, DSC perfusion MRI may be a valuable tool for monitoring PCA stenosis in patients with MMD.

Cerebral perfusion and its association with serum endothelin-1 in multiple sclerosis

ABSTRACT Multiple sclerosis (MS) is a chronic inflammatory, demyelinating and degenerative disease of the central nervous system. The exact pathogenesis of MS is incompletely understood. Cerebral vascular pathology is frequently verified in this disease. Perfusion studies may put light on the disease pathogenesis. In this study, we use dynamic susceptibility contrast-enhanced perfusion magnetic resonance imaging (DSC-MRI) to evaluate the cerebral perfusion in a group of MS patients. Thirty patients with relapsing–remitting multiple sclerosis (RRMS) and 20 healthy controls were investigated. The perfusion parameters were assessed for normal-appearing white matter (NAWM) and deep gray matter (DGM). Also, the serum level of the potent vasoconstrictor, endothelin-1(ET-1), was evaluated. There was reduction in cerebral perfusion in MS patients in comparison to healthy controls in the NAWM and (DGM). In addition, serum (ET-1) level was significantly elevated in the patient group and was correlated with the perfusion parameters. These findings suggest that cerebral hypoperfusion may play an important role in the pathogenesis of MS lesions. ET-1 has a role in cerebral hypoperfusion in both NAWM and DGM of RRMS patients. DSC-MRI could be an applicable objective measure to detect cerebral hemodynamic changes in MS.

Assessing the diagnostic accuracy and interobserver agreement of MRI perfusion in differentiating disease progression and pseudoprogression following treatment for glioblastoma in a tertiary UK centre

To assess the local diagnostic accuracy and interobserver agreement of dynamic susceptibility contrast-enhanced magnetic resonance perfusion (DSC MRP) reporting in differentiating between disease progression and pseudoprogression (PP) at a tertiary UK centre. This retrospective study included adults with histology-proven glioblastoma who underwent an index DSC MRP examination following treatment. Each index examination was evaluated by three reporters independently, including qualitative assessment and measurement of mean regional cerebral blood volume (rCBVmean) ratios. Consensus opinion was used as the reference standard and considered clinical, radiological and histological follow-up information. Examination reports were compared to each other and to the consensus opinion. Thirty-two cases were included (19 progression, 13 pseudoprogression). Interobserver agreement was fair for qualitative opinion (κ=0.58, 95% confidence interval [CI] 0.40-0.76) and good for rCBVmean ratio measurement (intraclass correlation coefficient [ICC, two-way random effects model] 0.63, 95% CI=0.43-0.78). Qualitative opinion showed diagnostic accuracies of 77.1% (95% CI=67.4-85.1) for progression and 75% (95% CI=65.1-83.3) for pseudoprogression. rCBVmean ratios were higher for progression (6.85±3.98) than pseudoprogression (3.71±3.40); a 3.0 threshold value maximised the sum of sensitivity (91.1%) and specificity (69.7%) on receiver operating characteristic analysis. DSC MRP and rCBVmean ratio measurement aid differentiation between progression and pseudoprogression following treatment for glioblastoma. Measurement of the rCBVmean ratio shows good interobserver agreement and can change opinion and improve confidence in DSC MRP reporting. Individual centres should validate their own threshold rCBVmean ratio values to optimise diagnostic accuracy.

Comparison of the Efficiency of Conventional Diffusion, Diffusion Tensor Imaging, and Dynamic Susceptibility Contrast-Enhanced Magnetic Resonance Perfusion Imaging in the Evaluation of Liver Fibrosis

Objective: Liver fibrosis is a dynamic, reversible process that can result in liver failure. There has been considerable interest in developing noninvasive methods for diagnosis and staging. To investigate the diffusion and perfusion changes of the fibrotic liver parenchyma with conventional diffusion-weighted imaging (CDI), diffusion tensor imaging (DTI), and T2*weighted dynamic susceptibility contrast-magnetic resonance perfusion imaging (DSC-MRPI) at 3Tesla MR scanner. Methods: Twenty-seven patients with chronic viral hepatitis and 24 volunteers were evaluated, prospectively. The standard MRI protocols of the abdomen, CDI, and DTI were performed. Apparent diffusion coefficient (ADC) maps were obtained, D and FA values were calculated for DTI. Signal Intensity(SI)-time curves were obtained and “blood volume”(BV), “blood flow” (BF), “time to peak”(TTP), “mean transit time”(MTT) were measured. All patients with hepatitis underwent liver biopsy. The efficacy of diffusion and perfusion parameters used in the diagnosis of fibrosis was analyzed with the receiver operating characteristic curve (ROC). Results: Patients had significantly lower liver ADC when compared to the control group, either with CDI and DTI. D values obtained from DTI were lower in patients than those of the normal volunteers, and the difference was statistically significant. On DSC-MRPI; BF, BV, MTT, and TTP of the liver were lower than those of the control group but only BV and MTT values showed statistical significance. Liver ADC, D, and BV values had a negative correlation with fibrosis. Conclusion: The results showed that the D values obtained from DTI, BV, and MTT values obtained from DSC-MRPI can be an efficient diagnostic tool for liver fibrosis in patients with chronic hepatitis.

A Novel Statistical Optimization Algorithm for Estimating Perfusion Curves in Susceptibility Contrast-Enhanced MRI.

Dynamic susceptibility contrast-enhanced magnetic resonance imaging is an important tool for evaluating intravascular indicator dynamics, which in turn is valuable for understanding brain physiology and pathophysiology. This procedure usually involves fitting a gamma-variate function to observed concentration-time curves in order to eliminate undesired effects of recirculation and the leakage of contrast agents. Several conventional curve-fitting approaches are routinely applied. The nonlinear optimization methods typically are computationally expensive and require reliable initial values to guarantee success, whereas a logarithmic linear least-squares (LL-LS) method is more stable and efficient, and does not suffer from the initial-value problem, but it can show degraded performance, especially when a few data or outliers are present. In this paper, we demonstrate, that the original perfusion curve-fitting problem can be transformed into a gamma-distribution-fitting problem by treating the concentration-time curves as a random sample from a gamma distribution with time as the random variable. A robust maximum-likelihood estimation (MLE) algorithm can then be readily adopted to solve this problem. The performance of the proposed method is compared with the nonlinear Levenberg-Marquardt (L-M) method and the LL-LS method using both synthetic and real data. The results show that the performance of the proposed approach is far superior to those of the other two methods, while keeping the advantages of the LL-LS method, such as easy implementation, low computational load, and dispensing with the need to guess the initial values. We argue that the proposed method represents an attractive alternative option for assessing intravascular indicator dynamics in clinical applications. Moreover, we also provide valuable suggestions on how to select valid data points and set the initial values in the two traditional approaches (LL-LS and nonlinear L-M methods) to achieve more reliable estimations.

Prediction of IDH Mutation Status in High-grade Gliomas Using DWI and High T1-weight DSC-MRI

We aimed to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) parameters in the noninvasive prediction of the isocitrate dehydrogenase (IDH) mutation status in high-grade gliomas (HGGs). A total of 58 patients with histopathologically proved HGGs were included in this retrospective study. All patients underwent multiparametric MRI on 3-T, including DSC-MRI and DWI before surgery. The mean apparent diffusion coefficient (ADC), relative maximum cerebral blood volume (rCBV), and percentage signal recovery (PSR) of the tumor core were measured and compared depending on the IDH mutation status and tumor grade. The Mann-Whitney U test was used to detect statistically significant differences in parameters between IDH-mutant-type (IDH-m-type) and IDH-wild-type (IDH-w-type) HGGs. Receiver operating characteristic curve (ROC) analysis was performed to evaluate the diagnostic performance. The rCBV was significantly higher, and the PSR value was significantly lower in IDH-w-type tumors than in the IDH-m group (p = 0.002 and <0.001, respectively).The ADC value in IDH-w-type tumors was significantly lower compared with the one in IDH-m types (p = 0.023), but remarkable overlaps were found between the groups. The PSR showed the best diagnostic performance with an AUC of 0.938 and with an accuracy rate of 0.87 at the optimal cutoff value of 86.85. The combination of the PSR and the rCBV for the identification of the IDH mutation status increased the discrimination ability at the AUC level of 0.955. In terms of each tumor grade, the PSR and rCBV showed significant differences between the IDH-m and IDH-w groups (p ≤0.001). The rCBV and PSR from DSC-MRI may be feasible noninvasive imaging parameters for predicting the IDH mutation status in HGGs. The standardization of the imaging protocol is indispensable to the utility of DSC perfusion MRI in wider clinical usage.

Investigating dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in posterior fossa tumors: differences and similarities with supratentorial tumors.

To assess the accuracy of dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging in glioma grading and brain tumor characterization of infratentorial tumors, and to investigate differences from supratentorial tumors. This retrospective study, approved by the institutional review board, included 246 patients with brain tumors (184 supratentorial, 62 infratentorial), grouped by tumor type: high-grade gliomas (HGG), low-grade gliomas (LGG), metastases (Met), and primary central nervous system lymphoma (PCNSL). Relative cerebral blood volume (rCBV) and mean percentage of signal recovery (PSR) were calculated. For statistical analyses, lesions were grouped by location and histology. Differences were tested with Mann-Whitney U tests. From ROC curves, we calculated accuracy, sensitivity, specificity, PPV, and NPV, for rCBV and PSR. For infratentorial tumors, rCBV was highly accurate in differentiating HGG from LGG (AUC = 0.938). Mean PSR showed high accuracy in differentiating PCNSL and HGG from Met (AUC = 0.978 and AUC = 0.881, respectively). Infratentorial and supratentorial tumors had similarly high rCBV in HGG, high mean PSR in PCNSL, and low mean PSR in Met. The main differences were the optimum threshold rCBV values (3.04 for supratentorial, 1.77 for infratentorial tumors) and the mean PSR, which was significantly higher in LGG than in HGG in supratentorial (p = 0.035), but not infratentorial gliomas. Using infratentorial rCBV threshold values for supratentorial tumors decreased the sensitivity and specificity. rCBV and mean PSR were useful in grading and differentiating infratentorial tumors. Proper cutoff values were important in the accuracy of perfusion-weighted imaging in posterior fossa tumors.

Can we predict intraoperative blood loss in meningioma patients? Application of dynamic susceptibility contrast-enhanced magnetic resonance imaging

PurposeTo evaluate the potential of quantitative dynamic susceptibility contrast (DSC) perfusion MR imaging parameters as imaging biomarkers for predicting intraoperative blood loss in meningioma. MethodsFifty-one non-embolized meningioma patients who had undergone preoperative DSC perfusion MR imaging were retrospectively included. The corrected relative cerebral blood volume (rCBV) and leakage coefficient (K2) of the entire enhanced tumor were obtained using leakage correction. Tumor volume, location, grade, and other clinical variables, were also analyzed. To investigate the vascularity and vascular permeability of meningiomas, and their correlation with predicting estimated blood loss (EBL) using preoperative DSC perfusion MR imaging, the authors proposed an index reflecting the inherent tendency of meningiomas to bleed after controlling volume (i.e., EBL/cm3). Simple regression was performed to identify predictors of EBL/cm3; subsequently, the relevant variables included in the stepwise multiple linear regression. ResultsOn univariate analysis, EBL/cm3 was correlated with rCBV (r=0.677; P<0.001), K2 (r=0.294; P=0.036), and tumor volume (r=–0.312, P=0.026). EBL/cm3 was not correlated with age (P=0.873), sex (P=0.404), tumor location (P=0.327), or histological grade (P=0.230). On multiple linear regression, rCBV (β=0.663 [0.463–0.864], B=1.293 [0.903–1.684; P<0.001) and K2 (β=0.260 [0.060–0.460], B=2.277 [0.523–4.031], P=0.012), were the only independent predictors of EBL/cm3. ConclusionThe rCBV and K2 derived from DSC perfusion MR imaging in meningiomas may serve as feasible tools for clinicians to predict intraoperative blood loss and facilitate surgical planning.

Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke.

Acute ischemic stroke is one of the primary causes of death worldwide. Recent studies have shown that the assessment of collateral status could aid in improving the treatment for patients with acute ischemic stroke. We present a 3D deep regression neural network to automatically generate the collateral images from dynamic susceptibility contrast-enhanced magnetic resonance perfusion (DSC-MRP) in acute ischemic stroke. This retrospective study includes 144 subjects with acute ischemic stroke (stroke cases) and 201 subjects without acute ischemic stroke (controls). DSC-MRP images of these subjects were manually inspected for collateral assessment in arterial, capillary, early and late venous, and delay phases. The proposed network was trained on 205 subjects, and the optimal model was chosen using the validation set of 64 subjects. The predictive power of the network was assessed on the test set of 76 subjects using the squared correlation coefficient (R-squared), mean absolute error (MAE), Tanimoto measure (TM), and structural similarity index (SSIM). The proposed network was able to predict the five phase maps with high accuracy. On average, 0.897 R-squared, 0.581 × 10-1 MAE, 0.946 TM, and 0.846 SSIM were achieved for the five phase maps. No statistically significant difference was, in general, found between controls and stroke cases. The performance of the proposed network was lower in the arterial and venous phases than the other three phases. The results suggested that the proposed network performs equally well for both control and acute ischemic stroke groups. The proposed network could help automate the assessment of collateral status in an efficient and effective manner and improve the quality and yield of diagnosis of acute ischemic stroke. The follow-up study will entail the clinical evaluation of the collateral images that are generated by the proposed network.

Combination value of diffusion-weighted imaging and dynamic susceptibility contrast-enhanced MRI in astrocytoma grading and correlation with GFAP, Topoisomerase IIα and MGMT.

The present study aimed to investigate the value of diffusion-weighted imaging (DWI) combined with dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI) scans in astrocytoma grading, and correlated MRI scan parameters of values of apparent diffusion coefficient (ADC) and relative cereberal blood volume (rCBV) with the immunohistochemical (IHC) indices of glial fibrillary acidic protein (GFAP), topoisomerase IIα (Topo IIα) and O 6-methylguanine-DNA methyltransferase (MGMT). A total of 123 patients with pathologically confirmed astrocytomas of differing grades underwent DWI and DSC scans. The values of the ADC and relative cerebral blood volume rCBV were compared with the grade II–IV astrocytomas. Receiver operating characteristic curves were used to compare astrocytoma grading efficiency of ADC, rCBV and the combination of the two values. The parameters of ADC and rCBV with GFAP, Topo IIα and MGMT indices were then correlated. The differences in ADC values were significant between the grades II, III and IV astrocytomas, and the rCBV values for grades II, III and IV were also significant. The combination of DWI and DSC demonstrated the highest values for area under curve in identifying grades II and III, and identifying grades III and IV, respectively. GFAP displayed a positive correlation with ADC and a negative correlation with rCBV. Topo IIα exhibited a negative correlation with ADC, and a positive correlation with rCBV. No correlation was observed between MGMT and ADC or rCBV. The combined application of DWI and DSC may increase astrocytoma grading accuracy. Values of ADC and rCBV exhibit certain correlations with IHC indices, and may predict degree of malignancy of astrocytoma.

Blood Oxygen Level–Dependent Response Changes in the Ipsilateral Primary Somatosensory Cortex and Thalamus of Patients With Moyamoya Disease During Median Nerve Electrical Stimulation

The objective of this study was to investigate the changes in the blood oxygen level-dependent (BOLD) response in the ipsilateral primary somatosensory cortex (SI) and thalamus of patients with moyamoya disease (MMD) during sensory stimulation. Sixty-four MMD patients, and 15 healthy volunteers were enrolled. Thirty-three MMD patients exhibited paroxysmal numbness or hypoesthesia in the unilateral limbs. Fifteen patients with acroparesthesia underwent unilateral encephaloduroarteriosynangiosis (EDAS). All volunteers underwent BOLD functional magnetic resonance imaging (BOLD-fMRI) under median nerve electrical stimulation (MNES). Blood oxygen level-dependent fMRI data were processed to obtain time-signal intensity curves in the activation areas of the bilateral SI and thalamus. Processed dynamic susceptibility contrast-enhanced magnetic resonance imaging data were used to measure the time to peak of the BOLD response in the regions of interest, including the bilateral SI, thalamus, and cerebellum. Changes in the time-signal intensity curve-related hemodynamic parameters in the ipsilateral SI and thalamus were examined between healthy controls, nonacroparesthesia patients, and asymptomatic and symptomatic sides of unilateral acroparesthesia patients during MNES. Changes in these parameters in MMD patients before and after EDAS were examined. Compared with healthy volunteers, 3 groups of MMD patients exhibited an increased peak of the positive BOLD response in the ipsilateral thalamus during MNES (0.65 ± 0.24 vs 0.79 ± 0.35, 0.94 ± 0.57, and 0.89 ± 0.50; P = 0.0335). The positive response peak in the ipsilateral SI markedly increased in MMD patients with acroparesthesia during MNES on the asymptomatic side (0.56 ± 0.37 vs 0.38 ± 0.27, P = 0.0243). The time to peak negative response in the ipsilateral SI was prolonged during MNES on the symptomatic side after EDAS (12.14 ± 8.90 seconds vs 18.86 ± 9.20 seconds, P = 0.0201). During sensory stimulation treatment, BOLD response changes occurred in the ipsilateral SI and thalamus of MMD patients. These changes enabled the contralateral hemisphere of the brain to better deal with sensory stimuli.

Differentiating Between Primary Central Nervous System Lymphomas and Glioblastomas: Combined Use of Perfusion-Weighted and Diffusion-Weighted Magnetic Resonance Imaging.

The purpose of this study was to determine whether combined diffusion-weighted imaging and dynamic susceptibility contrast-enhanced perfusion-weighted imaging magnetic resonance imaging can be used to differentiate between common malignant brain tumors, including lymphomas and high-grade gliomas. We evaluated 87 patients with histologically confirmed brain tumors, including 33 primary central nervous system lymphomas (PCNSLs) and 54 glioblastomas (GBMs). All patients underwent conventional magnetic resonance imaging, diffusion-weighted imaging, and perfusion-weighted imaging before surgical removal of the lesion or stereotactic biopsy. The maximum relative cerebral blood volume (rCBV) ratios of GBMs were significantly higher than those of PCNSLs (P < 0.0001). The maximum rCBVs helped to distinguish PCNSLs from GBMs with 97.0% sensitivity, 90.7% specificity, and 0.98 area under the curve. The minimum apparent diffusion coefficients (ADCs) of PCNSLs were significantly lower than those of GBMs (P < 0.0001). At an rCBV cutoff value of 4.0 and a minimum ADC of 1.0× 10-3 mm2/second, it was possible to differentiate between PCNSLs and GBMs. The combination of rCBV and ADC can facilitate the differentiation between PCNSLs and GBMs.

Slice-accelerated gradient-echo echo planar imaging dynamic susceptibility contrast-enhanced MRI with blipped CAIPI: effect of increasing temporal resolution.

To assess the influence of high temporal resolution on the perfusion measurements and image quality of perfusion maps, by applying simultaneous-multi-slice acquisition (SMS) dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI). DSC-MRI data using SMS gradient-echo echo planar imaging sequences in 10 subjects with no intracranial abnormalities were retrospectively analyzed. Three additional data sets with temporal resolution of 1.0, 1.5, and 2.0s were created from the raw data sets of 0.5s. Cerebral blood flow (CBF), cerebral blood volume, mean transit time (MTT), time to peak (TTP), and time to maximum tissue residue function (T max) measurements were performed, as was visual perfusion map analysis. The perfusion parameter for temporal resolution of 0.5s (reference) was compared with each synthesized perfusion parameter. CBF, MTT, and TTP values at temporal resolutions of 1.5 and 2.0s differed significantly from the reference. The image quality of MTT, TTP, and T max maps deteriorated with decreasing temporal resolution. The temporal resolution of DSC-MRI influences perfusion parameters and SMS DSC-MRI provides better image quality for MTT, TTP, and T max maps.

Normalised time-to-peak-distribution curves correlate with cerebral white matter hyperintensities – Could this improve early diagnosis?

Parameter-free assessment of the time-to-peak (TTP) histogram, termed ‘TTP-distribution curve’ (TDC), of dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) was introduced as a robust method to evaluate cerebral perfusion. TDC-assessment works fully automatically without the need of an arterial input function, thereby providing full comparability between different measurements. In the investigated sample of 106 patients, a strong dependency of TDC on the hemodynamic state of cerebral microvessels and the arterio-venous bolus-transit time was demonstrated. Accordingly, TDC-derived was 3.3–3.7 s for control patients and 4.4 s for cerebral small vessel disease patients. Measurements of associated bolus spread velocities ν and accelerations additionally revealed a direct effect from spin–spin relaxation time T2-weighted white matter hyperintensity volume, considered to indicate microangiopathy in cerebral small vessel disease, on the TDC-measurements. This strongly supports the prevailing hypothesis that cerebral small vessel disease directly influences DSC-measurements, where the degree could be estimated from an analysis of TDC. While this may be used to correct DSC-parameters for undesirable effects from cerebral small vessel disease, it could also serve to potentially identify patients at risk for cerebral small vessel disease at an early stage, since a subset of patients without yet significant WHM-volume, but clearly altered hemodynamics in TDC-measurements, was identified in this study.

Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.

Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor. To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival. Preoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. This technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning. Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.

Integrative Analysis of mRNA, microRNA, and Protein Correlates of Relative Cerebral Blood Volume Values in GBM Reveals the Role for Modulators of Angiogenesis and Tumor Proliferation.

Dynamic susceptibility contrast-enhanced magnetic resonance imaging is routinely used to provide hemodynamic assessment of brain tumors as a diagnostic as well as a prognostic tool. Recently, it was shown that the relative cerebral blood volume (rCBV), obtained from the contrast-enhancing as well as -nonenhancing portion of glioblastoma (GBM), is strongly associated with overall survival. In this study, we aim to characterize the genomic correlates (microRNA, messenger RNA, and protein) of this vascular parameter. This study aims to provide a comprehensive radiogenomic and radioproteomic characterization of the hemodynamic phenotype of GBM using publicly available imaging and genomic data from the Cancer Genome Atlas GBM cohort. Based on this analysis, we identified pathways associated with angiogenesis and tumor proliferation underlying this hemodynamic parameter in GBM.

Differentiation between primary central nervous system lymphoma and glioblastoma: a comparative study of parameters derived from dynamic susceptibility contrast-enhanced perfusion-weighted MRI

To evaluate the diagnostic performance of parameters derived from dynamic susceptibility contrast-enhanced perfusion-weighted magnetic resonance imaging, including first-pass slope ratio (FSR), which is potentially easier to derive than the other proposed parameters in this study, for differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma. Twenty-eight patients (10 PCNSLs and 18 glioblastomas) were analysed. Six perfusion parameters - corrected cerebral blood volume ratio (cCBVR), uncorrected CBV ratio (uCBVR), FSR, leakage coefficient (K2), percentage of signal-intensity recovery measured at the end of the first-pass (PSRend), and PSR measured using mean signal-intensity after the first-pass (PSRmean) - were derived from enhancing areas selected semi-automatically. Comparisons of cCBVR and uCBVR and of PSRend and PSRmean were conducted. The differences between PCNSL and glioblastoma were compared for the six parameters, and their diagnostic performance was evaluated by receiver operating characteristic curve analysis. For both tumours, cCBVR was significantly higher than uCBVR, and PSRend was significantly lower than PSRmean. PCNSL demonstrated lower cCBVR, uCBVR and FSR, and higher K2, PSRend and PSRmean compared with glioblastoma (p=0.0044 or less). On receiver operating characteristic curve analysis, the areas under the curve were 0.822 for cCBVR, 0.944 for uCBVR, 0.917 for FSR, 0.917 for K2, 0.933 for PSRend, and 0.894 for PSRmean. No significant difference was observed among the parameters, except cCBVR, which was significantly inferior to uCBVR. PCNSL can be differentiated from glioblastoma with high diagnostic value using any of the parameters, except cCBVR. FSR demonstrates high differential performance comparable to the other parameters.

Diagnostic Value of Semiquantitative Analysis of Dynamic Susceptibility Contrast Magnetic Resonance Imaging with GD-EOB-DTPA in Focal Liver Lesions Characterization: A Feasibility Study.

Purpose. To assess the diagnostic accuracy of dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSCE-MRI) in differentiation between benign and malignant liver lesions by assessment of tumoral perfusion parameters. Methods Materials. Seventy-three patients with known focal liver lesions, including 45 benign (16 FNH, 27 angiomas, and 2 abscesses) and 28 malignant ones (17 metastases, 9 HCCs, and 2 cholangiocarcinoma) underwent 1.5 T MRI upper abdominal study, with standard protocol that included dynamic contrast-enhanced sequences. On dedicated workstation, time-intensity curves were determined and the following perfusion parameters were calculated: relative arterial, venous and late enhancement (RAE, RVE, RLE), maximum enhancement (ME), relative enhancement (RE), and time to peak (TTP). Results. All diagnoses were established either by histopathology or imaging follow-up. Perfusion mean values calculated in benign lesions were RAE 33.8%, RVE 66.03%, RLE 80.63%, ME 776.00%, MRE 86.27%, and TTP 146.95 sec. Corresponding perfusion values calculated in malignant lesions were RAE 22.47%, RVE 40.54%, RLE 47.52%, ME 448.78%, MRE 49.85%, and TTP 183.79 sec. Statistical difference (p < 0.05) was achieved in all the perfusion parameters calculated, obtaining different cluster of perfusion kinetics between benign and malignant lesions. Conclusions. DSCE-MRI depicts kinetic differences in perfusion parameters among the different common liver lesions, related to tumour supply and microvascular characteristics.

Detection of glioma recurrence by 11C-methionine positron emission tomography and dynamic susceptibility contrast-enhanced magnetic resonance imaging

This study aimed to compare the diagnostic value of ¹¹C-methionine (¹¹C-MET) PET and dynamic susceptibility contrast-enhanced (DSCE) MRI in detecting glioma recurrence by meta-analysis. Databases such as PubMed (MEDLINE included), EMBASE, Science Direct, Springerlink, EBSCO, and Cochrane Database of Systematic Review were searched for relevant original articles on the detection of recurrent glioma using DSCE MRI or ¹¹C-MET PET with or without computed tomography. No restriction was imposed over the types and grades of glioma. The included studies were assessed for methodological quality. Results from histopathological analysis and/or close clinical and/or radiological follow-up for at least 3 months were used as the reference standard. The data were extracted by two reviewers independently to analyze the sensitivity, specificity, summary receiver-operating characteristic curve, area under the curve, and heterogeneity. The present study analyzed a total of 17 selected articles including different types and grades of glioma and showed that ¹¹C-MET PET and DSCE MRI had comparable sensitivity (0.870 and 0.884, respectively), specificity (0.813 and 0.853, respectively), positive likelihood ratio (4.355 and 5.806, respectively), negative likelihood ratio (0.192 and 0.134, respectively), and diagnostic odds ratio (21.857 and 41.918, respectively) without statistically significant differences, except for the fact that DSCE MRI displayed higher area under the curve and Q* index compared with ¹¹C-MET PET (P<0.05). Both ¹¹C-MET PET and DSCE MRI are accurate tools for detecting glioma recurrence. Although DSCE MRI seems to be superior to ¹¹C-MET PET, the latter can also be used to assess glioma recurrence when the former is not available.