Dynamic Susceptibility Contrast-enhanced MRI Research Articles

Diagnostic algorithm for glioma grading using dynamic susceptibility contrast‑enhanced magnetic resonance perfusion and proton magnetic resonance spectroscopy.

The present retrospective study aimed to investigate the diagnostic capacity of and design a diagnostic algorithm for dynamic susceptibility contrast-enhanced MRI (DSCE-MRI) and proton magnetic resonance spectroscopy (1H-MRS) in grading low-grade glioma (LGG) and high-grade glioma (HGG). This retrospective study enrolled 57 patients, of which 14 had LGG and 43 had HGG, five had World Health Organization grade 1, nine had grade 2, 20 had grade 3 and 23 had grade 4 glioma. All subjects underwent a standard 3T MRI brain tumor protocol with conventional MRI (cMRI) and advanced techniques, including DSCE-MRI and 1H-MRS. The associations of grade categorization with parameters in tumor and peritumor regions in the DSCE-MRI were examined, including tumor relative cerebral blood volume (TrCBV) and peripheral relative (Pr)CBV, as well as Tr and Pr cerebral blood flow (CBF) and 1H-MRS, including the creatine (Cr) and N-acetyl aspartate (NAA) ratios of choline (Cho), i.e. the TCho/NAA, PCho/NAA, TCho/Cr and PCho/Cr metabolite ratios. The data were compared using the Mann-Whitney U-test, independent samples t-test, Chi-square test, Fisher's exact test and receiver operating characteristic curve analyses. Decision tree analysis established an algorithm based on cutoffs for specified significant parameters. The PrCBF had the highest performance in the preoperative prediction of histological glioma grading, followed by the TrCBV, PrCBF, TrCBV, PCho/NAA, PCho/Cr, TCho/NAA and TCho/Cr. An algorithm based on TrCBV, PrCBF and TCho/Cr had a diagnostic accuracy of 100% for LGG and 90.7% for HGG and a misclassification risk of 7%. The cutoffs (sensitivity and specificity) were 2.48 (86 and 100%) for TrCBV, 1.26 (83.7 and 100%) for PrCBF and 3.18 (69.8 and 78.6%) for TCho/Cr. In conclusion, the diagnostic algorithm using TrCBV, PrCBF and TCho/Cr values, which were obtained from DSCE-MRI and 1H-MRS, increased diagnostic accuracy to 100% for LGGs and 90.7% for HGGs compared to previous studies using conventional MRI. This non-invasive advanced MRI diagnostic algorithm is recommended for clinical application for constructing preoperative strategies and prognosis of patients with glioma.

Dynamics of cerebral blood volume during and after middle cerebral artery occlusion in rats – Comparison between ultrafast ultrasound and dynamic susceptibility contrast-enhanced MRI measurements

Tomographic perfusion imaging techniques are integral to translational stroke research paradigms that advance our understanding of the disease. Functional ultrasound (fUS) is an emerging technique that informs on cerebral blood volume (CBV) through ultrasensitive Doppler and flow velocity (CBFv) through ultrafast localization microscopy. It is not known how experimental results compare with a classical CBV-probing technique such as dynamic susceptibility contrast-enhanced perfusion MRI (DSC-MRI). To that end, we assessed hemodynamics based on uUS (n = 6) or DSC-MRI (n = 7) before, during and up to three hours after 90-minute filament-induced middle cerebral artery occlusion (MCAO) in rats. Recanalization was followed by a brief hyperperfusion response, after which CBV and CBFv temporarily normalized but progressively declined after one hour in the lesion territory. DSC-MRI data corroborated the incomplete restoration of CBV after recanalization, which may have been caused by the free-breathing anesthetic regimen. During occlusion, MCAO-induced hypoperfusion was more discrepant between either technique, likely attributable to artefactual signal mechanisms related to slow flow, and processing algorithms employed for either technique. In vivo uUS- and DSC-MRI-derived measures of CBV enable serial whole-brain assessment of post-stroke hemodynamics, but readouts from both techniques need to be interpreted cautiously in situations of very low blood flow.

Arterial Spin Labeling: Techniques, Clinical Applications, and Interpretation.

Arterial spin labeling (ASL) is an emerging noninvasive MRI technique for assessing cerebral perfusion. An important advantage of ASL perfusion is the lack of a requirement for an exogenous tracer. ASL uses magnetically labeled water protons from arterial blood as an endogenous diffusible tracer. For this reason, ASL is an attractive perfusion imaging modality for children and for patients with contraindications or adverse reactions to gadolinium, patients with renal failure, and those who need repeated follow-up imaging. Another advantage of ASL is the possibility of quantifying cerebral blood flow, which provides an opportunity for comparative analysis among multiple longitudinal studies, unlike other MR perfusion techniques, which are semiquantitative and yield relative perfusion parameters. Advances in MRI technology and pulse sequence design have translated ASL beyond the research arena to successful clinical implementation. However, ASL is still underused in routine clinical practice. Some disadvantages of ASL include a lower signal-to-noise ratio and a longer acquisition time than those with dynamic susceptibility contrast-enhanced MRI. Additional factors limiting the use of ASL include variations in existing techniques and pulse sequence design, the complexity of implementation and postprocessing, insufficient experience with and/or knowledge of the potential clinical applications, and the absence of interpretation guidelines. The authors review the technical and physiologic basis of ASL perfusion, as well as artifacts, pitfalls, and its current clinical applications. A practical approach for interpreting ASL findings is also suggested.

Association of cognitive function and hypoperfusion in Moyamoya disease patients without stroke

The influence of hypoperfusion on cognition in patients with Moyamoya disease (MMD) is unclear. This study investigated cognitive function changes in MMD patients without stroke and illustrated the relationship between cognitive impairment and hypoperfusion. We prospectively performed a structured battery of seven neurocognitive tests on 115 adult MMD patients without stroke and 82 healthy controls. Hemodynamic assessment was performed using dynamic susceptibility contrast-enhanced MRI. The best subset regression (BSR) strategy was used to identify risk factors. Global cognition (MoCA), speed of information processing (TMT-A), executive function (TMT-B), visuospatial function (CDT), and verbal memory (CAVLT) were significantly poorer in MMD patients without stroke than in healthy controls. The TMT-B score significantly correlated with cerebral blood flow (CBF) in the bilateral lateral frontal lobes, centrum semiovale, and temporal lobes. The TMT-A and CAVLT scores significantly correlated with CBF in the left centrum semiovale (L-CSO) and temporal lobes. According to the BSR results, age, education, white matter lesions, and hypoperfusion of the L-CSO were risk factors for cognitive impairment. Hypoperfusion leads to multiple cognitive impairments in MMD patients without stroke. The perfusion of particular areas may help evaluate the cognitive function of MMD patients and guide therapeutic strategies.

Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI.

To investigate the prognostic utility of DTI and DSC-PWI perfusion-derived parameters in brain metastases patients. Retrospective analyses of DTI-derived parameters (MD, FA, CL, CP, and CS) and DSC-perfusion PWI-derived rCBVmax from 101 patients diagnosed with brain metastases prior to treatment were performed. Using semi-automated segmentation, DTI metrics and rCBVmax were quantified from enhancing areas of the dominant metastatic lesion. For each metric, patients were classified as short- and long-term survivors based on analysis of the best coefficient for each parameter and percentile to separate the groups. Kaplan-Meier analysis was used to compare mOS between these groups. Multivariate survival analysis was subsequently conducted. A correlative histopathologic analysis was performed in a subcohort (n = 10) with DTI metrics and rCBVmax on opposite ends of the spectrum. Significant differences in mOS were observed for MDmin (p < 0.05), FA (p < 0.01), CL (p < 0.05), and CP (p < 0.01) and trend toward significance for rCBVmax (p = 0.07) between the two risk groups, in the univariate analysis. On multivariate analysis, the best predictive survival model was comprised of MDmin (p = 0.05), rCBVmax (p < 0.05), RPA (p < 0.0001), and number of lesions (p = 0.07). On histopathology, metastatic tumors showed significant differences in the amount of stroma depending on the combination of DTI metrics and rCBVmax values. Patients with high stromal content demonstrated poorer mOS. Pretreatment DTI-derived parameters, notably MDmin and rCBVmax, are promising imaging markers for prognostication of OS in patients with brain metastases. Stromal cellularity may be a contributing factor to these differences. The correlation of DTI-derived metrics and perfusion MRI with patient outcomes has not been investigated in patients with treatment naïve brain metastasis. DTI and DSC-PWI can aid in therapeutic decision-making by providing additional clinical guidance.

Assessment of the hypervascularized fraction of glioblastomas using a volume analysis of dynamic susceptibility contrast-enhanced MRI may help to identify pseudoprogression.

PurposeAlthough perfusion magnetic resonance imaging (MRI) is widely used to identify pseudoprogression, this advanced technique lacks clinical reliability. Our aim was to develop a parameter assessing the hypervascularized fraction of glioblastomas based on volume analysis of dynamic susceptibility contrast-enhanced MRI and evaluate its performance in the diagnosis of pseudoprogression.MethodsPatients with primary glioblastoma showing lesion progression on the first follow-up MRI after chemoradiotherapy were enrolled retrospectively. On both initial and first follow-up MRIs, the leakage-corrected cerebral blood volume (CBV) maps were post-processed using the conventional hot-spot method and a volume method, after manual segmentation of the contrast-enhanced delineated lesion. The maximum CBV (rCBVmax) was calculated with both methods. Secondly, the threshold of 2 was applied to the CBV values contained in the entire segmented volume, defining our new parameter: %rCBV>2. The probability of pseudoprogression based on rCBVmax and %rCBV>2 was calculated in logistic regression models and diagnostic performance assessed by receiving operator characteristic curves.ResultsOut of 25 patients, 11 (44%) were classified with pseudoprogression and 14 (56%) with true progression based on the Response Assessement in Neuro-Oncology criteria. rCBVmax was lower for pseudoprogression (3.4 vs. 7.6; p = 0.033) on early follow-up MRI. %rCBV>2, was lower for pseudoprogression on both initial (57.5% vs. 71.3%; p = 0.033) and early follow-up MRIs (22.1% vs. 51.8%; p = 0.0006). On early follow-up MRI, %rCBV>2 had the largest area under the curve for the diagnosis of pseudoprogression: 0.909 [0.725–0.986].ConclusionThe fraction of hypervascularization of glioblastomas as assessed by %rCBV>2 was lower in tumours that subsequently developed pseudoprogression both on the initial and early follow-up MRIs. This fractional parameter may help identify pseudoprogression with greater accuracy than rCBVmax.

Comparison of dynamic susceptibility contrast enhanced MR and FDG-PET brain studies in patients with Alzheimer’s disease and amnestic mild cognitive impairment

BackgroundThe aim of this study was to compare Dynamic Susceptibility Contrast Enhanced MRI (DSC-MRI) and PET with [18F]flurodeoxyglucose (FDG-PET) in the diagnosis of Alzheimer’s Disease (AD) and amnestic Mild Cognitive Impairment (aMCI).MethodsTwenty-seven age-and sex-matched patients with AD, 39 with aMCI and 16 controls underwent brain DSC-MRI followed by FDG-PET. Values of relative Cerebral Blood Volume (rCBV) and rCBV z-scores from frontal, temporal, parietal and PCG cortices were correlated with the rate of glucose metabolism from PET. Sensitivity, specificity and accuracy of DSC-MRI and FDG-PET in the diagnosis of AD and aMCI were assessed and compared.ResultsIn AD, hypoperfusion was found within all the examined locations, while in aMCI in both parietal and temporal cortices and left PCG. FDG-PET showed the greatest hypometabolism in parietal, temporal and left PCG regions in both AD and aMCI. FDG-PET was more accurate in distinguishing aMCI from the controls than DSC-MRI. In the AD and combined group (AD + aMCI) there were numerous correlations between DSC-MRI and FDG-PET results.ConclusionsIn AD the patterns of hypoperfusion and glucose hypometabolism are similar, thus DSC-MRI may be a competitive method to FDG-PET. FDG-PET is a more accurate method in the diagnosis of aMCI.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques.

ObjectiveSummarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.MethodsExperts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).ResultsHigh-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.ConclusionConsiderable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

Diffusion kurtosis imaging combined with dynamic susceptibility contrast-enhanced MRI in differentiating high-grade glioma recurrence from pseudoprogression

ObjectivesTo compare the added value of diffusion kurtosis imaging (DKI) with the combination of dynamic susceptibility contrast-enhanced (DSC) MRI in differentiating glioma recurrence from pseudoprogression. MethodsThirty-four patients with high-grade gliomas developing new and/or increasing enhanced lesions within six months after surgery and chemoradiotherapy were retrospectively analyzed. All patients were pathologically confirmed to have recurrent glioma (n = 22) or pseudoprogression (n = 12). The DKI and DSC MRI parameters were calculated based on the enhanced lesions on contrast-enhanced T1WI. ROC analysis was performed on significant variables to determine their diagnostic performance. Multivariate logistic regression was used to determine the best prediction model for discrimination. ResultsThe relative mean kurtosis (rMK), relative axial kurtosis (rKa), relative cerebral blood volume (rCBV), and relative mean transit time (rMTT) of glioma recurrence were higher than those of pseudoprogression (all, P < 0.05). The AUCs and diagnostic accuracy were 0.879 and 82.35% for rMK, 0.723 and 70.59% for rKa, 0.890 and 82.35% for rCBV, 0.765 and 73.53% for rMTT, respectively. A multivariate logistic regression model showed a significant contribution of rMK (P = 0.006) and rCBV (P = 0.009) as independent imaging classifiers for discrimination. The combined use of rMK and rCBV improved the AUC to 0.924 (P < 0.001) and the diagnostic accuracy to 88.24%. ConclusionDKI may be a valuable non-invasive tool in differentiating glioma recurrence from pseudoprogression, and its use in combination with DSC MRI can improve diagnostic performance in assessing treatment response compared with either technique alone.

Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging

Glioblastoma (GBM) has high metabolic demands, which can lead to acidification of the tumor microenvironment. We hypothesize that a machine learning model built on temporal principal component analysis (PCA) of dynamic susceptibility contrast-enhanced (DSC) perfusion MRI can be used to estimate tumor acidity in GBM, as estimated by pH-sensitive amine chemical exchange saturation transfer echo-planar imaging (CEST-EPI). We analyzed 78 MRI scans in 32 treatment naïve and post-treatment GBM patients. All patients were imaged with DSC-MRI, and pH-weighting that was quantified from CEST-EPI estimation of the magnetization transfer ratio asymmetry (MTRasym) at 3 ppm. Enhancing tumor (ET), non-enhancing core (NC), and peritumoral T2 hyperintensity (namely, edema, ED) were used to extract principal components (PCs) and to build support vector machines regression (SVR) models to predict MTRasym values using PCs. Our predicted map correlated with MTRasym values with Spearman’s r equal to 0.66, 0.47, 0.67, 0.71, in NC, ET, ED, and overall, respectively (p < 0.006). The results of this study demonstrates that PCA analysis of DSC imaging data can provide information about tumor pH in GBM patients, with the strongest association within the peritumoral regions.

Comparison of Blood Perfusion Parameters in the Ipsilateral and Contralateral Sides of the Brain in Known Cases of Mesial Temporal Lobe Epilepsy

Background: Temporal lobe epilepsy (TLE) is the most common form of focal seizures. To localize the epileptic site for surgery, different neuroimaging tools are used. Perfusion magnetic resonance imaging (MRI) is one of the modalities used to evaluate the cerebral hemodynamics and localize intracranial neoplasia and cerebrovascular events. Two contrast-based perfusion imaging sequences are described, including dynamic susceptibility contrast-enhanced MRI (DSC-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). The most commonly measured parameters include the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT). Objectives: This study aimed to evaluate the blood perfusion parameters, such as rCBV and rCBF, in patients diagnosed with mesial temporal lobe epilepsy (MTLE), using DSC-perfusion MRI to determine whether there is a change in these parameters and if this modality can be used to diagnose and localize the epileptic side. Patients and Methods: Twenty-two patients, who were diagnosed with TLE clinically and electrophysiologically by a neurologist, were investigated in this study. The patients were examined for the presence of any other lesions, such as tumors or cerebrovascular disease as the exclusion criteria. Perfusion images were processed by the Siemens perfusion software, and the rCBV and rCBF maps were generated based on the gamma variate fit. For qualitative analysis, coronal reconstruction of rCBV and rCBF maps was performed. For quantitative analysis, a single neuroradiologist placed the region of interest (ROI) on the hippocampus and the parahippocampal gyrus on T1W images at the same level of DSC images. After determining the CBV and CBF values relative to the ROI of each side, the asymmetry index (AI) was calculated. Results: In patients with unilateral epilepsy, the blood perfusion parameters in the ipsilateral side of the brain were significantly lower than the contralateral side (P &lt; 0.0001); the mean values of both parameters were significantly lower in the affected side as compared to the opposite side. Conclusion: In patients with TLE, significantly lower blood perfusion parameters in the affected side of the brain can help radiologists and neurologists to lateralize the MTLE side.

Simultaneous Mapping of Vasculature, Hypoxia, and Proliferation Using Dynamic Susceptibility Contrast MRI, 18F-FMISO PET, and 18F-FLT PET in Relation to Contrast Enhancement in Newly Diagnosed Glioblastoma.

Conventional MRI plays a key role in the management of patients with high-grade glioma, but multiparametric MRI and PET tracers could provide further information to better characterize tumor metabolism and heterogeneity by identifying regions having a high risk of recurrence. In this study, we focused on proliferation, hypervascularization, and hypoxia, all factors considered indicative of poor prognosis. They were assessed by measuring uptake of 18F-3'-deoxy-3'-18F-fluorothymidine (18F-FLT), relative cerebral blood volume (rCBV) maps, and uptake of 18F-fluoromisonidazole (18F-FMISO), respectively. For each modality, the volumes and high-uptake subvolumes (hot spots) were semiautomatically segmented and compared with the contrast enhancement (CE) volume on T1-weighted gadolinium-enhanced (T1w-Gd) images, commonly used in the management of patients with glioblastoma. Methods: Dynamic susceptibility contrast-enhanced MRI (31 patients), 18F-FLT PET (20 patients), or 18F-FMISO PET (20 patients), for a total of 31 patients, was performed on preoperative glioblastoma patients. Volumes and hot spots were segmented on SUV maps for 18F-FLT PET (using the fuzzy locally adaptive bayesian algorithm) and 18F-FMISO PET (using a mean contralateral image + 3.3 SDs) and on rCBV maps (using a mean contralateral image + 1.96 SDs) for dynamic susceptibility contrast-enhanced MRI and overlaid on T1w-Gd images. For each modality, the percentages of the peripheral volumes and the peripheral hot spots outside the CE volume were calculated. Results: All tumors showed highly proliferated, hypervascularized, and hypoxic regions. The images also showed pronounced heterogeneity of both tracers regarding their uptake and rCBV maps, within each individual patient. Overlaid volumes on T1w-Gd images showed that some proliferative, hypervascularized, and hypoxic regions extended beyond the CE volume but with marked differences between patients. The ranges of peripheral volume outside the CE volume were 1.6%-155.5%, 1.5%-89.5%, and 3.1%-78.0% for 18F-FLT, rCBV, and 18F-FMISO, respectively. All patients had hyperproliferative hot spots outside the CE volume, whereas hypervascularized and hypoxic hot spots were detected mainly within the enhancing region. Conclusion: Spatial analysis of multiparametric maps with segmented volumes and hot spots provides valuable information to optimize the management and treatment of patients with glioblastoma.

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903–1.000) for local recurrence; 0.864 (0.726–0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p = 0.03) in multivariate Cox regression on progression-free survival.

Prognostic Prediction Based on Dynamic Contrast-Enhanced MRI and Dynamic Susceptibility Contrast-Enhanced MRI Parameters from Non-Enhancing, T2-High-Signal-Intensity Lesions in Patients with Glioblastoma.

ObjectiveFew attempts have been made to investigate the prognostic value of dynamic contrast-enhanced (DCE) MRI or dynamic susceptibility contrast (DSC) MRI of non-enhancing, T2-high-signal-intensity (T2-HSI) lesions of glioblastoma multiforme (GBM) in newly diagnosed patients. This study aimed to investigate the prognostic values of DCE MRI and DSC MRI parameters from non-enhancing, T2-HSI lesions of GBM.Materials and MethodsA total of 76 patients with GBM who underwent preoperative DCE MRI and DSC MRI and standard treatment were retrospectively included. Six months after surgery, the patients were categorized into early progression (n = 15) and non-early progression (n = 61) groups. We extracted and analyzed the permeability and perfusion parameters of both modalities for the non-enhancing, T2-HSI lesions of the tumors. The optimal percentiles of the respective parameters obtained from cumulative histograms were determined using receiver operating characteristic (ROC) curve and univariable Cox regression analyses. The results were compared using multivariable Cox proportional hazards regression analysis of progression-free survival.ResultsThe 95th percentile value (PV) of Ktrans, mean Ktrans, and median Ve were significant predictors of early progression as identified by the ROC curve analysis (area under the ROC curve [AUC] = 0.704, p = 0.005; AUC = 0.684, p = 0.021; and AUC = 0.670, p = 0.0325, respectively). Univariable Cox regression analysis of the above three parametric values showed that the 95th PV of Ktrans and the mean Ktrans were significant predictors of early progression (hazard ratio [HR] = 1.06, p = 0.009; HR = 1.25, p = 0.017, respectively). Multivariable Cox regression analysis, which also incorporated clinical parameters, revealed that the 95th PV of Ktrans was the sole significant independent predictor of early progression (HR = 1.062, p < 0.009).ConclusionThe 95th PV of Ktrans from the non-enhancing, T2-HSI lesions of GBM is a potential prognostic marker for disease progression.

Coagulation/Complement Activation and Cerebral Hypoperfusion in Relapsing-Remitting Multiple Sclerosis.

IntroductionMultiple sclerosis (MS) is a demyelinating disease of the central nervous system with an underlying immune-mediated and inflammatory pathogenesis. Innate immunity, in addition to the adaptive immune system, plays a relevant role in MS pathogenesis. It represents the immediate non-specific defense against infections through the intrinsic effector mechanism “immunothrombosis” linking inflammation and coagulation. Moreover, decreased cerebral blood volume (CBV), cerebral blood flow (CBF), and prolonged mean transit time (MTT) have been widely demonstrated by MRI in MS patients. We hypothesized that coagulation/complement and platelet activation during MS relapse, likely during viral infections, could be related to CBF decrease. Our specific aims are to evaluate whether there are differences in serum/plasma levels of coagulation/complement factors between relapsing-remitting (RR) MS patients (RRMS) in relapse and those in remission and healthy controls as well as to assess whether brain hemodynamic changes detected by MRI occur in relapse compared with remission. This will allow us to correlate coagulation status with perfusion and demographic/clinical features in MS patients.Materials and MethodsThis is a multi-center, prospective, controlled study. RRMS patients (1° group: 30 patients in relapse; 2° group: 30 patients in remission) and age/sex-matched controls (3° group: 30 subjects) will be enrolled in the study. Patients and controls will be tested for either coagulation/complement (C3, C4, C4a, C9, PT, aPTT, fibrinogen, factor II, VIII, and X, D-dimer, antithrombin, protein C, protein S, von-Willebrand factor), soluble markers of endothelial damage (thrombomodulin, Endothelial Protein C Receptor), antiphospholipid antibodies, lupus anticoagulant, complete blood count, viral serological assays, or microRNA microarray. Patients will undergo dynamic susceptibility contrast-enhanced MRI using a 3.0-T scanner to evaluate CBF, CBV, MTT, lesion number, and volume.Statistical AnalysisANOVA and unpaired t-tests will be used. The level of significance was set at p ≤ 0.05.DiscussionIdentifying a link between activation of coagulation/complement system and cerebral hypoperfusion could improve the identification of novel molecular and/or imaging biomarkers and targets, leading to the development of new effective therapeutic strategies in MS.Clinical Trial Registration Clinicaltrials.gov, identifier NCT04380220.

Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging

The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enhanced (DSCE) or T1-weighted dynamic contrast enhanced (DCE) perfusion MRI are important tools for diagnosis. They play a crucial role in providing pre-operative assessment of tumor histology, grading, and biopsy guidance. However, the manual contouring of these neoplasms is tedious, expensive, time-consuming, and vulnerable to inter-observer variability. In this work, we propose a 3D mask region-based convolutional neural network (R-CNN) method to automatically segment brain tumors in DSCE MRI perfusion images. As our goal is to simultaneously localize and segment the tumor, our training process contained both a region-of-interest (ROI) localization and regression with voxel-wise segmentation. The combination of classification loss, ROI location and size regression loss, and segmentation loss were used to supervise the proposed network. We retrospectively investigated 21 patients’ perfusion images, with between 50 and 70 perfusion time point volumes, a total of 1260 3D volumes. Tumor contours were automatically segmented by our proposed method and compared against other state-of-the-art methods and those delineated by physicians as the ground truth. The results of our method demonstrated good agreement with the ground truth contours. The average DSC, precision, recall, Hausdorff distance, mean surface distance (MSD), root MSD, and center of mass distance were 0.90 ± 0.04, 0.91 ± 0.04, 0.90 ± 0.06, 7.16 ± 5.78 mm, 0.45 ± 0.34 mm, 1.03 ± 0.72 mm, and 0.86 ± 0.91 mm, respectively. These results support the feasibility of our method in accurately localizing and segmenting brain tumors in DSCE perfusion MRI. Our 3D Mask R-CNN segmentation method in DSCE perfusion imaging has great promise for future clinical use.

Differentiation of progressive disease from pseudoprogression using 3D PCASL and DSC perfusion MRI in patients with glioblastoma.

To use 3D pseudocontinuous arterial spin labeling (3D PCASL) and dynamic susceptibility contrast-enhanced (DSC) perfusion MRI to differentiate progressive disease from pseudoprogression in patients with glioblastoma (GBM). Thirty-two patients with GBM who developed progressively enhancing lesions within the radiation field following resection and chemoradiation were included in this retrospective, single-institution study. The updated modified RANO criteria were used to establish progressive disease or pseudoprogression. Following 3D PCASL and DSC MR imaging, perfusion parameter estimates of cerebral blood flow (ASL-nCBF and DSC-nrCBF) and cerebral blood volume (DSC-nrCBV) were calculated. Additionally, contrast enhanced volumes were measured. Mann-Whitney U tests were used to compare groups. Linear discriminant analysis (LDA) and area under receiver operator characteristic curve (AUC) analyses were used to evaluate performance of each perfusion parameter and to determine optimal cut-off points. All perfusion parameter measurements were higher in patients with progressive disease (mean, 95% CI ASL-nCBF 2.48, [2.03, 2.93]; DSC-nrCBF = 2.27, [1.85, 2.69]; DSC-nrCBV = 3.51, [2.37, 4.66]) compared to pseudoprogression (mean, 95% CI ASL-nCBF 0.99, [0.47, 1.52]; DSC-nrCBF = 1.05, [0.36, 1.74]; DSC-nCBV = 1.19, [0.34, 2.05]), and findings were significant at the p < 0.0125 level (p = 0.001, 0.003, 0.002; effect size: Cohen's d = 1.48, 1.27, and 0.92). Contrast enhanced volumes were not significantly different between groups (p > 0.447). All perfusion parameters demonstrated high AUC (0.954 for ASL-nCBF, 0.867 for DSC-nrCBF, and 0.891 for DSC-nrCBV), however, ASL-nCBF demonstrated the highest AUC and misclassified the fewest cases (N = 6). Lesions correctly classified by ASL but misclassified by DSC were located along the skull base or adjacent to large resection cavities with residual blood products, at areas of increased susceptibility. Both 3D PCASL and DSC perfusion MRI techniques have nearly equivalent performance for the differentiation of progressive disease from pseudoprogression in patients with GBM. However, 3D PCASL is less sensitive to susceptibility artifact and may allow for improved classification in select cases.

Prognostication of anaplastic astrocytoma patients: application of contrast leakage information of dynamic susceptibility contrast-enhanced MRI and dynamic contrast-enhanced MRI.

To examine the applicability of contrast leakage information from dynamic susceptibility contrast-enhanced (DSC) MRI and dynamic contrast-enhanced (DCE) MRI to determine which one is the most valuable surrogate imaging biomarker for predicting disease progression in anaplastic astrocytoma (AA) patients. This study was approved by the institutional review board (IRB), which waived informed consent. A total of seventy-three AA patients who had undergone preoperative DCE and DSC MRI and received standard treatment, including partial resection or biopsy followed by radiation therapy, were included in this retrospective study. Based on Response Assessment in Neuro-Oncology (RANO), patients were sorted into progression (n = 21) and non-progression (n = 52) groups. Tumor boundaries were defined as high-signal intensity (SI) lesions on fluid-attenuated inversion recovery (FLAIR) imaging, where we analyzed mean pharmacokinetic parameters (Ktrans, Vp, and Ve) from DCE MRI and contrast leakage information (mean extraction fraction (EF)) from DSC MRI. Mean Ve and mean EF were significantly higher in patients with progression-free survival (PFS) < 18 months than in those with PFS ≥ 18 months. For distinguishing the group with PFS < 18 months, AUC values were calculated using the mean Ve value (AUC = 0.716). The Kaplan-Meier survival analysis revealed that mean Ve value was significantly correlated with PFS. In Cox proportional-hazards regression, only the mean Ve value was found to be significantly associated with PFS. We found that the mean Ve value based on high-SI tumor lesions on FLAIR imaging was capable of predicting outcomes of AA patients as a potential surrogate imaging biomarker. • Mean Ve(2.152 ± 1.857 vs. 1.173 ± 1.408) was significantly higher in anaplastic astrocytoma patients with PFS < 18 months that in those with PFS ≥ 18 months (p = 0.02). • Cox proportional-hazards regression showed that only mean Ve(p = 0.034) was significantly associated with PFS, regardless of IDH mutation status, in anaplastic astrocytoma patients.

Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images: Introduction.

Glioblastoma is the most aggressive brain tumor with poor prognosis. The purpose of this study is to improve the tissue characterization of these highly heterogeneous tumors using delta-radiomic features of images from dynamic susceptibility contrast enhanced (DSC) magnetic resonance imaging (MRI). Twenty-five patients with histopathologically confirmed to be 13 high-grade (HG) and 12 low-grade (LG) gliomas who underwent the standard brain tumor MRI protocol, including DSC MRI, were included. Tumor regions on all DSC MRI images were registered to and contoured in T2-weighted fluid-attenuated inversion recovery (FLAIR) images. These contours and its contralateral regions of the normal tissue were used to extract delta-radiomic features before applying feature selection. The most informative and non-redundant features were selected to train a random forest to differentiate HG and LG gliomas. Then a leave-one-out cross-validation random forest was applied to classify these tumors for grading. Finally, a majority-voting method was applied to reduce binarization bias and to combine the results of various feature lists. Analysis of the predictions showed that the reported method consistently predicted the tumor grade of 24 out of 25 patients correctly (0.96). Finally, the mean prediction accuracy was 0.950±0.091 for HG and 0.850±0.255 for LG. The area under the receiver operating characteristic curve (AUC) was 0.94. This study shows that delta-radiomic features derived from DSC MRI data can be used to characterize and determine the tumor grades. The radiomic features from DSC MRI may be used to elucidate the underlying tumor biology and response to therapy.

Revealing hemodynamic heterogeneity of gliomas based on signal profile features of dynamic susceptibility contrast-enhanced MRI.

Dynamic susceptibility contrast enhanced magnetic resonance imaging (DSC MRI) is widely used for studying blood perfusion in brain tumors. While the time-dependent change of MRI signals related to the concentration of the tracer is used to derive the hemodynamic parameters such as regional blood volume and flow into tumors, the tissue-specific information associated with variations in profiles of signal time course is often overlooked. We report a new approach of combining model free independent component analysis (ICA) identification of specific signal profiles of DSC MRI time course data and extraction of the features from those time course profiles to interrogate time course data followed by calculating the region specific blood volume based on selected individual time courses. Based on the retrospective analysis of DSC MRI data from 38 patients with pathology confirmed low (n = 18) and high (n = 20) grade gliomas, the results reveal the spatially defined intra-tumoral hemodynamic heterogeneity of brain tumors based on features of time course profiles. The hemodynamic heterogeneity as measured by the number of independent components of time course data is associated with the tumor grade. Using 8 selected signal profile features, machine-learning trained algorithm, e.g., logistic regression, was able to differentiate pathology confirmed low intra-tumoral and high grade gliomas with an accuracy of 86.7%. Furthermore, the new method can potentially extract more tumor physiological information from DSC MRI comparing to the traditional model-based analysis and morphological analysis of tumor heterogeneity, thus may improve the characterizations of gliomas for better diagnosis and treatment decisions.