T(1)- and T(2)(*)-dominant extravasation correction in DSC-MRI: part II-predicting patient outcome after a single dose of cediranib in recurrent glioblastoma patients

Structural and functional vascular dysfunction within brain metastases is linked to pembrolizumab inefficacy

Structurally and functionally aberrant vasculature is a hallmark of tumor angiogenesis and treatment resistance. Given the synergistic link between aberrant tumor vasculature and immunosuppression, we analyzed perfusion MRI for 44 patients with brain metastases (BM) undergoing treatment with pembrolizumab. To date, vascular-immune communication, or the relationship between immune checkpoint inhibitor (ICI) efficacy and vascular architecture, has not been well-characterized in human imaging studies. We found that ICI-responsive BM possessed a structurally balanced vascular makeup, which was linked to improved vascular efficiency and an immune-stimulatory microenvironment. In contrast, ICI-resistant BM were characterized by a lack of immune cell infiltration and a highly aberrant vasculature dominated by large-caliber vessels. Peri-tumor region analysis revealed early functional changes predictive of ICI resistance before radiographic evidence on conventional MRI. This study was one of the largest functional imaging studies for BM and establishes a foundation for functional studies that illuminate the mechanisms linking patterns of vascular architecture with immunosuppression, as targeting these aspects of cancer biology may serve as the basis for future combination treatments.

Prognostic Predictions for Patients with Glioblastoma after Standard Treatment: Application of Contrast Leakage Information from DSC-MRI within Nonenhancing FLAIR High-Signal-Intensity Lesions

BACKGROUND AND PURPOSE

Attempts have been made to quantify the microvascular leakiness of glioblastomas and use it as an imaging biomarker to predict the prognosis of the tumor. The purpose of our study was to evaluate whether the extraction fraction value from DSC-MR imaging within nonenhancing FLAIR hyperintense lesions was a better prognostic imaging biomarker than dynamic contrast-enhanced MR imaging parameters for patients with glioblastoma.

MATERIALS AND METHODS

A total of 102 patients with glioblastoma who received a preoperative dynamic contrast-enhanced MR imaging and DSC-MR imaging were included in this retrospective study. Patients were classified into the progression (n = 87) or nonprogression (n = 15) groups at 24 months after surgery. We extracted the means and 95th percentile values for the contrast leakage information parameters from both modalities within the nonenhancing FLAIR high-signal-intensity lesions.

RESULTS

The extraction fraction 95th percentile value was higher in the progression-free survival group of >24 months than at ≤24 months. The median progression-free survival of the group with an extraction fraction 95th percentile value of >13.32 was 17 months, whereas that of the group of ≤13.32 was 12 months. In addition, it was an independent predictor variable for progression-free survival in the patients regardless of their ages and genetic information.

CONCLUSIONS

The extraction fraction 95th percentile value was the only independent parameter for prognostic prediction in patients with glioblastoma among the contrast leakage information, which has no statistically significant correlations with the DCE-MR imaging parameters.

Analysis of postprocessing steps for residue function dependent dynamic susceptibility contrast (DSC)-MRI biomarkers and their clinical impact on glioma grading for both 1.5 and 3T

BACKGROUND

Dynamic susceptibility contrast (DSC)-MRI analysis pipelines differ across studies and sites, potentially confounding the clinical value and use of the derived biomarkers.

PURPOSE/HYPOTHESIS

To investigate how postprocessing steps for computation of cerebral blood volume (CBV) and residue function dependent parameters (cerebral blood flow [CBF], mean transit time [MTT], capillary transit heterogeneity [CTH]) impact glioma grading.

STUDY TYPE

Retrospective study from The Cancer Imaging Archive (TCIA).

POPULATION

Forty-nine subjects with low- and high-grade gliomas.

FIELD STRENGTH/SEQUENCE

1.5 and 3.0T clinical systems using a single-echo echo planar imaging (EPI) acquisition.

ASSESSMENT

Manual regions of interest (ROIs) were provided by TCIA and automatically segmented ROIs were generated by k-means clustering. CBV was calculated based on conventional equations. Residue function dependent biomarkers (CBF, MTT, CTH) were found by two deconvolution methods: circular discretization followed by a signal-to-noise ratio (SNR)-adapted eigenvalue thresholding (Method 1) and Volterra discretization with L-curve-based Tikhonov regularization (Method 2).

STATISTICAL TESTS

Analysis of variance, receiver operating characteristics (ROC), and logistic regression tests.

RESULTS

MTT alone was unable to statistically differentiate glioma grade (P > 0.139). When normalized, tumor CBF, CTH, and CBV did not differ across field strengths (P > 0.141). Biomarkers normalized to automatically segmented regions performed equally (rCTH AUROC is 0.73 compared with 0.74) or better (rCBF AUROC increases from 0.74-0.84; rCBV AUROC increases 0.78-0.86) than manually drawn ROIs. By updating the current deconvolution steps (Method 2), rCTH can act as a classifier for glioma grade (P < 0.007), but not if processed by current conventional DSC methods (Method 1) (P > 0.577). Lastly, higher-order biomarkers (eg, rCBF and rCTH) along with rCBV increases AUROC to 0.92 for differentiating tumor grade as compared with 0.78 and 0.86 (manual and automatic reference regions, respectively) for rCBV alone.

DATA CONCLUSION

With optimized analysis pipelines, higher-order perfusion biomarkers (rCBF and rCTH) improve glioma grading as compared with CBV alone. Additionally, postprocessing steps impact thresholds needed for glioma grading.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:547-553.

Optimization of Acquisition and Analysis Methods for Clinical Dynamic Susceptibility Contrast MRI Using a Population-Based Digital Reference Object

BACKGROUND AND PURPOSE

The accuracy of DSC-MR imaging CBV maps in glioblastoma depends on acquisition and analysis protocols. Multisite protocol heterogeneity has challenged standardization initiatives due to the difficulties of in vivo validation. This study sought to compare the accuracy of routinely used protocols using a digital reference object.

MATERIALS AND METHODS

The digital reference object consisted of approximately 10,000 simulated voxels recapitulating typical signal heterogeneity encountered in vivo. The influence of acquisition and postprocessing methods on CBV reliability was evaluated across 6912 parameter combinations, including contrast agent dosing schemes, pulse sequence parameters, field strengths, and postprocessing methods. Accuracy and precision were assessed using the concordance correlation coefficient and coefficient of variation.

RESULTS

Across all parameter space, the optimal protocol included full-dose contrast agent preload and bolus, intermediate (60°) flip angle, 30-ms TE, and postprocessing with a leakage-correction algorithm (concordance correlation coefficient = 0.97, coefficient of variation = 6.6%). Protocols with no preload or fractional dose preload and bolus using these acquisition parameters were generally less robust. However, a protocol with no preload, full-dose bolus, and low (30°) flip angle performed very well (concordance correlation coefficient = 0.93, coefficient of variation = 8.7% at 1.5T and concordance correlation coefficient = 0.92, coefficient of variation = 8.2% at 3T).

CONCLUSIONS

Schemes with full-dose preload and bolus maximize CBV accuracy and reduce variability, which could enable smaller sample sizes and more reliable detection of CBV changes in clinical trials. When a lower total contrast agent dose is desired, use of a low flip angle, no preload, and full-dose bolus protocol may provide an attractive alternative.

Monitoring of tumor vascular normalization: the key points from basic research to clinical application

Tumor vascular normalization alleviates hypoxia in the tumor microenvironment, reduces the degree of malignancy, and increases the efficacy of traditional therapy. However, the time window for vascular normalization is narrow; therefore, how to determine the initial and final points of the time window accurately is a key factor in combination therapy. At present, the gold standard for detecting the normalization of tumor blood vessels is histological staining, including tumor perfusion, microvessel density (MVD), vascular morphology, and permeability. However, this detection method is almost unrepeatable in the same individual and does not dynamically monitor the trend of the time window; therefore, finding a relatively simple and specific monitoring index has important clinical significance. Imaging has long been used to assess changes in tumor blood vessels and tumor changes caused by the oxygen environment in clinical practice; some preclinical and clinical research studies demonstrate the feasibility to assess vascular changes, and some new methods were in preclinical research. In this review, we update the most recent insights of evaluating tumor vascular normalization.

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

BACKGROUND AND PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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).

CONCLUSIONS

Estimation of vascular permeability with DSC-MR imaging further characterizes hypervascular primary central nervous system lymphoma and improves diagnostic performance in glioblastoma differentiation.

Perfusion Magnetic Resonance Imaging Changes in Normal Appearing Brain Tissue after Radiotherapy in Glioblastoma Patients may Confound Longitudinal Evaluation of Treatment Response

Background

The aim of this study was assess acute and early delayed radiation-induced changes in normal-appearing brain tissue perfusion as measured with perfusion magnetic resonance imaging (MRI) and the dependence of these changes on the fractionated radiotherapy (FRT) dose level.

Patients and methods

Seventeen patients with glioma WHO grade III-IV treated with FRT were included in this prospective study, seven were excluded because of inconsistent FRT protocol or missing examinations. Dynamic susceptibility contrast MRI and contrast-enhanced 3D-T1-weighted (3D-T1w) images were acquired prior to and in average (standard deviation): 3.1 (3.3), 34.4 (9.5) and 103.3 (12.9) days after FRT. Pre-FRT 3D-T1w images were segmented into white- and grey matter. Cerebral blood volume (CBV) and cerebral blood flow (CBF) maps were calculated and co-registered patient-wise to pre-FRT 3D-T1w images. Seven radiation dose regions were created for each tissue type: 0-5 Gy, 5-10 Gy, 10-20 Gy, 20-30 Gy, 30-40 Gy, 40-50 Gy and 50-60 Gy. Mean CBV and CBF were calculated in each dose region and normalised (nCBV and nCBF) to the mean CBV and CBF in 0-5 Gy white- and grey matter reference regions, respectively.

Results

Regional and global nCBV and nCBF in white- and grey matter decreased after FRT, followed by a tendency to recover. The response of nCBV and nCBF was dose-dependent in white matter but not in grey matter.

Conclusions

Our data suggest that radiation-induced perfusion changes occur in normal-appearing brain tissue after FRT. This can cause an overestimation of relative tumour perfusion using dynamic susceptibility contrast MRI, and can thus confound tumour treatment evaluation.

An Update on the Approach to the Imaging of Brain Tumors

PURPOSE OF REVIEW

Neuroimaging plays a critical role in diagnosis of brain tumors and in assessment of response to therapy. However, challenges remain, including accurately and reproducibly assessing response to therapy, defining endpoints for neuro-oncology trials, providing prognostic information, and differentiating progressive disease from post-therapeutic changes particularly in the setting of antiangiogenic and other novel therapies.

RECENT FINDINGS

Recent advances in the imaging of brain tumors include application of advanced MRI imaging techniques to assess tumor response to therapy and analysis of imaging features correlating to molecular markers, grade, and prognosis. This review aims to summarize recent advances in imaging as applied to current diagnostic and therapeutic neuro-oncologic challenges.

ABCD1 dysfunction alters white matter microvascular perfusion

Cerebral X-linked adrenoleukodystrophy is a devastating neurodegenerative disorder caused by mutations in the ABCD1 gene, which lead to a rapidly progressive cerebral inflammatory demyelination in up to 60% of affected males. Selective brain endothelial dysfunction and increased permeability of the blood–brain barrier suggest that white matter microvascular dysfunction contributes to the conversion to cerebral disease. Applying a vascular model to conventional dynamic susceptibility contrast magnetic resonance perfusion imaging, we demonstrate that lack of ABCD1 function causes increased capillary flow heterogeneity in asymptomatic hemizygotes predominantly in the white matter regions and developmental stages with the highest probability for conversion to cerebral disease. In subjects with ongoing inflammatory demyelination we observed a sequence of increased capillary flow heterogeneity followed by blood–brain barrier permeability changes in the perilesional white matter, which predicts lesion progression. These white matter microvascular alterations normalize within 1 year after treatment with haematopoietic stem cell transplantation. For the first time in vivo, our studies unveil a model to assess how ABCD1 alters white matter microvascular function and explores its potential as an earlier biomarker for monitoring disease progression and response to treatment.

Pseudo-extravasation rate constant of dynamic susceptibility contrast-MRI determined from pharmacokinetic first principles

Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) is widely used to obtain informative perfusion imaging biomarkers, such as the relative cerebral blood volume (rCBV). The related post-processing software packages for DSC-MRI are available from major MRI instrument manufacturers and third-party vendors. One unique aspect of DSC-MRI with low-molecular-weight gadolinium (Gd)-based contrast reagent (CR) is that CR molecules leak into the interstitium space and therefore confound the DSC signal detected. Several approaches to correct this leakage effect have been proposed throughout the years. Amongst the most popular is the Boxerman-Schmainda-Weisskoff (BSW) K₂ leakage correction approach, in which the K₂ pseudo-first-order rate constant quantifies the leakage. In this work, we propose a new method for the BSW leakage correction approach. Based on the pharmacokinetic interpretation of the data, the commonly adopted R₂ * expression accounting for contributions from both intravascular and extravasating CR components is transformed using a method mathematically similar to Gjedde-Patlak linearization. Then, the leakage rate constant (K_L ) can be determined as the slope of the linear portion of a plot of the transformed data. Using the DSC data of high-molecular-weight (~750 kDa), iron-based, intravascular Ferumoxytol (FeO), the pharmacokinetic interpretation of the new paradigm is empirically validated. The primary objective of this work is to empirically demonstrate that a linear portion often exists in the graph of the transformed data. This linear portion provides a clear definition of the Gd CR pseudo-leakage rate constant, which equals the slope derived from the linear segment. A secondary objective is to demonstrate that transformed points from the initial transient period during the CR wash-in often deviate from the linear trend of the linearized graph. The inclusion of these points will have a negative impact on the accuracy of the leakage rate constant, and even make it time dependent.

Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges

Gliomas possess complex and heterogeneous vasculatures with abnormal hemodynamics. Despite considerable advances in diagnostic and therapeutic techniques for improving tumor management and patient care in recent years, the prognosis of malignant gliomas remains dismal. Perfusion-weighted magnetic resonance imaging techniques that could noninvasively provide superior information on vascular functionality have attracted much attention for evaluating brain tumors. However, nonconsensus imaging protocols and postprocessing analysis among different institutions impede their integration into standard-of-care imaging in clinic. And there have been very few studies providing a comprehensive evidence-based and systematic summary. This review first outlines the status of glioma theranostics and tumor-associated vascular pathology and then presents an overview of the principles of dynamic contrast-enhanced MRI (DCE-MRI) and dynamic susceptibility contrast-MRI (DSC-MRI), with emphasis on their recent clinical applications in gliomas including tumor grading, identification of molecular characteristics, differentiation of glioma from other brain tumors, treatment response assessment, and predicting prognosis. Current challenges and future perspectives are also highlighted.

Reliable estimation of microvascular flow patterns in patients with disrupted blood-brain barrier using dynamic susceptibility contrast MRI

PURPOSE

To present and quantify the performance of a method to compute tissue hemodynamic parameters from dynamic susceptibility contrast (DSC) MRI data in brain tissue with possible nonintact blood-brain barrier.

THEORY AND MATERIALS AND METHODS

We propose a Bayesian scheme to obtain perfusion metrics, including capillary transit-time heterogeneity (CTH), from DSC-MRI data in the presence of contrast agent extravasation. Initial performance assessment is performed through simulations. Next, we assessed possible over- or under correction for tracer extravasation in two patients receiving contrast agent preloading and two patients not receiving preloading. Perfusion metrics for N = 60 patients diagnosed with either grade III (N = 14) or grade IV gliomas (N = 46) were analyzed across tissue types to evaluate the ability to distinguish regions with different hemodynamic patterns. Finally, N = 4 patient cases undergoing anti-angiogenic treatment are evaluated qualitatively for treatment effects. All patient data were acquired at 3.0 Tesla.

RESULTS

The simulation studies showed good robustness against low signal-to-noise ratios, exemplified with Pearson correlations of R = 0.833 (mean transit time) and R = 0.738 (CTH) at signal-to-noise ratio = 20. Region-of-interest analysis of the N = 60 glioma patients showed that cerebral blood volume (CBV) significantly separated enhancing core from edema (grade IV: P < 10^-8 , grade III: P < 0.05) and enhancing core from normal appearing ipsilateral white matter (NAWM) (grade IV: P < 10^-8 , grade III: P < 0.05). The microvascular parameters were particularly good in separating edematous tissue from NAWM tissue in grade IV gliomas (P < 0.001). Finally, CTH separated grade III and grade IV core tissue (P < 0.05).

CONCLUSION

We have demonstrated robustness of the proposed Bayesian algorithm against experimental noise and demonstrated complementary value in microvascular parameters to the CBV parameter in separating tissue types in gliomas.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:537-549.

Response Assessment in Neuro-Oncology Criteria and Clinical Endpoints

The Response Assessment in Neuro-Oncology (RANO) Working Group is an international multidisciplinary group whose goal is to improve response criteria and define endpoints for neuro-oncology trials. The RANO criteria for high-grade gliomas attempt to address the issues of pseudoprogression, pseudoresponse, and nonenhancing tumor progression. Incorporation of advanced MR imaging may eventually help improve the ability of these criteria to define enhancing and nonenhancing disease better. The RANO group has also developed criteria for neurologic response and evaluation of patients receiving immunologic therapies. RANO criteria have been developed for brain metastases and are in progress for meningiomas, leptomeningeal disease, spinal tumors, and pediatric tumors.

On the Use of DSC-MRI for Measuring Vascular Permeability

BACKGROUND AND PURPOSE

Contrast agent extravasation has been shown to confound brain tumor perfusion measurements with DSC-MR imaging, necessitating the use of correction techniques (eg, Weisskoff, Bjornerud). Leakage parameters (K2 and K(a)) postulated to reflect vessel permeability can be extracted from these correction methods; however, the biophysical interpretation of these parameters and their relationship to commonly used MR imaging measures of vascular permeability (eg, contrast agent volume transfer constant, [K(trans)]) remain unclear. Given that vascular density, as assessed by blood volume, and vascular permeability, as reflected by K(trans) (and potentially K2 or K(a)), report on unique and clinically informative vascular characteristics, there is a compelling interest to simultaneously assess these features.

MATERIALS AND METHODS

We acquired multiecho DSC-MR imaging data, allowing the simultaneous computation and voxelwise comparison of single- and dual-echo derived measures of K2, K(a) and K(trans) in patients with glioma. This acquisition enabled the investigation of competing T1 and T2* leakage effects and TE dependency on these parameters.

RESULTS

K2 and K(a) displayed nonsignificant (P = .150 and P = .060, respectively) voxelwise linear correlations with K(trans), while a significant (P < .001) inverse relationship was observed between K2 and Ka (coefficient of determination [r(2)] = 0.466-0.984). Significantly different (P < .005) mean estimates were found between voxels exhibiting predominately T1 and T2* effects for K2 and K(a). K(trans), however, was observed to be similar between these voxels (0.109 versus 0.092 minutes(-1)). Significant differences (P < .001) in extracellular-extravascular volume fraction (v(e)) (0.285 versus 0.167) were also observed between cohorts. Additionally, K2 and K(a) were found to have a significant quadratic relationship (P = .031 and P = .005, respectively) with v(e).

CONCLUSIONS

Estimates of vascular permeability in brain tumors may be simultaneously acquired from multiple-echo DSC-MR imaging via K(trans); however, caution should be used in assuming a similar relationship for K2 and K(a).

Brain perfusion: computed tomography and magnetic resonance techniques

Cerebral perfusion imaging provides assessment of regional microvascular hemodynamics in the living brain, enabling in vivo measurement of a variety of different hemodynamic parameters. Perfusion imaging techniques that are used in the clinical setting usually rely upon X-ray computed tomography (CT) or magnetic resonance imaging (MRI). This chapter reviews CT- and MRI-based perfusion imaging techniques, with attention to image acquisition, clinically relevant aspects of image postprocessing, and fundamental differences between CT- and MRI-based techniques. Correlations with cerebrovascular physiology and potential clinical applications of perfusion imaging are reviewed, focusing upon the two major classes of neurologic disease in which perfusion imaging is most often performed: primary perfusion disorders (including ischemic stroke, transient ischemic attack, and reperfusion syndrome), and brain tumors.

Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma

BACKGROUND AND PURPOSE

Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.

MATERIALS AND METHODS

This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose- and preload dose-corrected DSC acquisitions, with 16 patients undergoing preload dose-corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV > 1.75, and percentage of voxels with relative CBV > 1.0 (fractional tumor burden). We determined Pearson (r) and Spearman (ρ) correlations between non-preload dose- and preload dose-corrected metrics. In a subset of patients with recurrent glioblastoma (n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies (n = 29) in 12 patients.

RESULTS

With IB Neuro, relative CBV metrics correlated highly between non-preload dose- and preload dose-corrected conditions for fractional tumor burden (r = 0.96, ρ = 0.94), percentage > 1.75 (r = 0.93, ρ = 0.91), mean (r = 0.87, ρ = 0.86), and mode (r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) (P < .01). The highest relative CBV-microvessel area correlations required preload dose and IB Neuro (r = 0.64, ρ = 0.58, P = .001).

CONCLUSIONS

Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.

Lessons from anti-vascular endothelial growth factor and anti-vascular endothelial growth factor receptor trials in patients with glioblastoma

Treatment of glioblastoma (GBM), the most common primary malignant brain tumor in adults, remains a significant unmet need in oncology. Historically, cytotoxic treatments provided little durable benefit, and tumors recurred within several months. This has spurred a substantial research effort to establish more effective therapies for both newly diagnosed and recurrent GBM. In this context, antiangiogenic therapy emerged as a promising treatment strategy because GBMs are highly vascular tumors. In particular, GBMs overexpress vascular endothelial growth factor (VEGF), a proangiogenic cytokine. Indeed, many studies have demonstrated promising radiographic response rates, delayed tumor progression, and a relatively safe profile for anti-VEGF agents. However, randomized phase III trials conducted to date have failed to show an overall survival benefit for antiangiogenic agents alone or in combination with chemoradiotherapy. These results indicate that antiangiogenic agents may not be beneficial in unselected populations of patients with GBM. Unfortunately, biomarker development has lagged behind in the process of drug development, and no validated biomarker exists for patient stratification. However, hypothesis-generating data from phase II trials that reveal an association between increased perfusion and/or oxygenation (ie, consequences of vascular normalization) and survival suggest that early imaging biomarkers could help identify the subset of patients who most likely will benefit from anti-VEGF agents. In this article, we discuss the lessons learned from the trials conducted to date and how we could potentially use recent advances in GBM biology and imaging to improve outcomes of patients with GBM who receive antiangiogenic therapy.

Pitfalls in the neuroimaging of glioblastoma in the era of antiangiogenic and immuno/targeted therapy - detecting illusive disease, defining response

Glioblastoma, the most common malignant primary brain tumor in adults is a devastating diagnosis with an average survival of 14–16 months using the current standard of care treatment. The determination of treatment response and clinical decision making is based on the accuracy of radiographic assessment. Notwithstanding, challenges exist in the neuroimaging evaluation of patients undergoing treatment for malignant glioma. Differentiating treatment response from tumor progression is problematic and currently combines long-term follow-up using standard magnetic resonance imaging (MRI), with clinical status and corticosteroid-dependency assessments. In the clinical trial setting, treatment with gene therapy, vaccines, immunotherapy, and targeted biologicals similarly produces MRI changes mimicking disease progression. A neuroimaging method to clearly distinguish between pseudoprogression and tumor progression has unfortunately not been found to date. With the incorporation of antiangiogenic therapies, a further pitfall in imaging interpretation is pseudoresponse. The Macdonald criteria that correlate tumor burden with contrast-enhanced imaging proved insufficient and misleading in the context of rapid blood–brain barrier normalization following antiangiogenic treatment that is not accompanied by expected survival benefit. Even improved criteria, such as the RANO criteria, which incorporate non-enhancing disease, clinical status, and need for corticosteroid use, fall short of definitively distinguishing tumor progression, pseudoresponse, and pseudoprogression. This review focuses on advanced imaging techniques including perfusion MRI, diffusion MRI, MR spectroscopy, and new positron emission tomography imaging tracers. The relevant image analysis algorithms and interpretation methods of these promising techniques are discussed in the context of determining response and progression during treatment of glioblastoma both in the standard of care and in clinical trial context.

Delayed contrast extravasation MRI: a new paradigm in neuro-oncology

BACKGROUND

Conventional magnetic resonance imaging (MRI) is unable to differentiate tumor/nontumor enhancing tissues. We have applied delayed-contrast MRI for calculating high resolution treatment response assessment maps (TRAMs) clearly differentiating tumor/nontumor tissues in brain tumor patients.

METHODS

One hundred and fifty patients with primary/metastatic tumors were recruited and scanned by delayed-contrast MRI and perfusion MRI. Of those, 47 patients underwent resection during their participation in the study. Region of interest/threshold analysis was performed on the TRAMs and on relative cerebral blood volume maps, and correlation with histology was studied. Relative cerebral blood volume was also assessed by the study neuroradiologist.

RESULTS

Histological validation confirmed that regions of contrast agent clearance in the TRAMs >1 h post contrast injection represent active tumor, while regions of contrast accumulation represent nontumor tissues with 100% sensitivity and 92% positive predictive value to active tumor. Significant correlation was found between tumor burden in the TRAMs and histology in a subgroup of lesions resected en bloc (r(2) = 0.90, P < .0001). Relative cerebral blood volume yielded sensitivity/positive predictive values of 51%/96% and there was no correlation with tumor burden. The feasibility of applying the TRAMs for differentiating progression from treatment effects, depicting tumor within hemorrhages, and detecting residual tumor postsurgery is demonstrated.

CONCLUSIONS

The TRAMs present a novel model-independent approach providing efficient separation between tumor/nontumor tissues by adding a short MRI scan >1 h post contrast injection. The methodology uses robust acquisition sequences, providing high resolution and easy to interpret maps with minimal sensitivity to susceptibility artifacts. The presented results provide histological validation of the TRAMs and demonstrate their potential contribution to the management of brain tumor patients.

Differentiation of brain abscesses from glioblastomas and metastatic brain tumors: comparisons of diagnostic performance of dynamic susceptibility contrast-enhanced perfusion MR imaging before and after mathematic contrast leakage correction

Purpose

To compare the diagnostic performance of dynamic susceptibility contrast-enhanced perfusion MRI before and after mathematic contrast leakage correction in differentiating pyogenic brain abscesses from glioblastomas and/or metastatic brain tumors.

Materials and Methods

Cerebral blood volume (CBV), leakage-corrected CBV and leakage coefficient K₂ were measured in enhancing rims, perifocal edema and contralateral normal appearing white matter (NAWM) of 17 abscesses, 19 glioblastomas and 20 metastases, respectively. The CBV and corrected CBV were normalized by dividing the values in the enhancing rims or edema to those of contralateral NAWM. For each study group, a paired t test was used to compare the K₂ of the enhancing rims or edema with those of NAWM, as well as between CBV and corrected CBV of the enhancing rims or edema. ANOVA was used to compare CBV, corrected CBV and K₂ among three lesion types. The diagnostic performance of CBV and corrected CBV was assessed with receiver operating characteristic (ROC) curve analysis.

Results

The CBV and correction CBV of enhancing rim were 1.45±1.17 and 1.97±1.01 for abscesses, 3.85±2.19 and 4.39±2.33 for glioblastomas, and 2.39±0.90 and 2.97±0.78 for metastases, respectively. The CBV and corrected CBV in the enhancing rim of abscesses were significantly lower than those of glioblastomas and metastases (P = 0.001 and P = 0.007, respectively). In differentiating abscesses from glioblastomas and metastases, the AUC values of corrected CBV (0.822) were slightly higher than those of CBV (0.792).

Conclusions

Mathematic leakage correction slightly increases the diagnostic performance of CBV in differentiating pyogenic abscesses from necrotic glioblastomas and cystic metastases. Clinically, DSC perfusion MRI may not need mathematic leakage correction in differentiating abscesses from glioblastomas and/or metastases.

Assessing tumor cytoarchitecture using multiecho DSC-MRI derived measures of the transverse relaxivity at tracer equilibrium (TRATE)

PURPOSE

In brain tumor dynamic susceptibility contrast (DSC)-MRI studies, multiecho acquisition methods are used to quantify the dynamic changes in T1 and T2 * that occur when contrast agent (CA) extravasates. Such methods also enable the estimation of the effective tissue CA transverse relaxivity. The goal of this study was to evaluate the sensitivity of the transverse relaxivity at tracer equilibrium (TRATE) to tumor cytoarchitecture.

METHODS

Computational and in vitro studies were used to evaluate the biophysical basis of TRATE. In 9L, C6, and human brain tumors, TRATE, the apparent diffusion coefficient (ADC), the CA transfer constant (K(trans) ), the extravascular extracellular volume fraction (ve ), and histological data were compared.

RESULTS

Simulations and in vitro results indicate that TRATE is highly sensitive to variations in cellular properties such as cell size and density. The histologic cell density and TRATE values were significantly higher in 9L tumors as compared to C6 tumors. In animal and human tumors, a voxel-wise comparison of TRATE with ADC, ve , and K(trans) maps showed low spatial correlation.

CONCLUSION

The assessment of TRATE is clinically feasible and its sensitivity to tissue cytoarchitectural features not present in other imaging methods indicate that it could potentially serve as a unique structural signature or "trait" of cancer.

Evaluation of microvascular permeability with dynamic contrast-enhanced MRI for the differentiation of primary CNS lymphoma and glioblastoma: radiologic-pathologic correlation

BACKGROUND AND PURPOSE

Dynamic contrast-enhanced MR imaging can provide in vivo assessment of the microvasculature in intracranial tumors. The aim of the present study was to evaluate the diagnostic performance of dynamic contrast-enhanced MR imaging derived vascular permeability parameters, including the volume transfer constant, the volume of extravascular extracellular space, and the flux rate constant between the extravascular extracellular space and plasma, for the differentiation of primary CNS lymphoma and glioblastoma.

MATERIALS AND METHODS

Sixty glioblastomas and 11 primary central nervous system lymphomas were included. Pretreatment T1-weighted dynamic contrast-enhanced MR imaging with a 3D T1-weighted spoiled gradient-echo sequence was performed on a 3T MR imaging scanner. Perfusion parameters (volume transfer constant, the volume of extravascular extracellular space, and the flux rate constant) were measured on the basis of the Tofts-Kernmode model. The Mann-Whitney U test and receiver operating characteristic analysis were used to compare those parameters between primary central nervous system lymphoma and glioblastoma. Histopathologic correlation of dynamic contrast-enhanced MR imaging findings was performed by using reticulin staining and CD31 immunohistochemistry.

RESULTS

Median volume transfer constant and flux rate constant values were significantly higher in primary central nervous system lymphoma (0.145 ± 0.057 and 0.396 ± 0.088) than in glioblastoma (0.064 ± 0.021 and 0.230 ± 0.058) (P < .001, respectively). Median volume of extravascular extracellular space values did not differ significantly between primary central nervous system lymphoma (0.434 ± 0.165) and glioblastoma (0.319 ± 0.107). On receiver operating characteristic analysis, volume transfer constant had the best discriminative value for differentiating primary central nervous system lymphoma and glioblastoma (threshold, 0.093; sensitivity, 90.9%; specificity, 95.0%). Histopathologic evaluation revealed intact vascular integrity in glioblastoma despite endothelial proliferation, whereas primary central nervous system lymphoma demonstrated destroyed vessel architecture, thereby promoting vascular disintegrity.

CONCLUSIONS

Primary central nervous system lymphoma demonstrated significantly higher volume transfer constant and flux rate constant values compared with glioblastoma, implying a higher vascular permeability in primary central nervous system lymphoma. These findings confirm initial observations from perfusion CT and dynamic contrast-enhanced MR imaging studies, correlating with underlying histopathologic features, and may be useful in distinguishing primary central nervous system lymphoma from glioblastoma.

CT imaging correlates of genomic expression for oral cavity squamous cell carcinoma

BACKGROUND AND PURPOSE

Imaging correlates of genetic expression have been found for prognostic and predictive biomarkers of some malignant diseases, including breast and brain tumors. This study tests the hypothesis that imaging findings correlate with relevant genomic biomarkers in oral cavity squamous cell carcinoma.

MATERIALS AND METHODS

Surplus frozen tissue from 27 untreated patients with oral cavity squamous cell carcinoma who underwent preoperative CT imaging was analyzed for gene expression. A team of neuroradiologists blinded to the genomic analysis results reviewed an extensive list of CT findings. The imaging correlated with genomic expression for cyclin D1, angiogenesis-related genes (vascular endothelial growth factor receptors and ligands), which relate to enhancement on the basis of other tumor types; and epidermal growth factor receptor, which may relate to proliferation and mass effect.

RESULTS

Expression of vascular endothelial growth factor receptors 1 and 2 correlated with the enhancement of the primary tumor (P = .018 and P = .025, respectively), whereas the epidermal growth factor receptor correlated with mass effect (P = .03). Other exploratory correlations included epidermal growth factor receptor to perineural invasion (P = .05), and certain vascular endothelial growth factor receptors and ligands to mass effect (P = .03) and increased (P = .01) or decreased (P = .02) primary tumor size.

CONCLUSIONS

We report that CT imaging correlates with gene expression in untreated oral cavity squamous cell carcinoma. Enhancement of the primary tumor and degree of mass effect correlate with relevant genomic biomarkers, which are also potential drug targets. Eventually, treatment decisions may be aided by combining imaging findings into meaningful phenotypes that relate directly to genomic biomarkers.

The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI

Dynamic susceptibility contrast (DSC) MRI is the most commonly used MRI method to assess cerebral perfusion and other related haemodynamic parameters. Although the technique is well established and used routinely in clinical centres, there are still many problems that impede accurate perfusion quantification. In this review article, we present 39 steps which guide the reader through the theoretical principles, practical decisions, potential problems, current limitations and latest advances in DSC-MRI. The 39 steps span the collection, analysis and interpretation of DSC-MRI data, expounding issues and possibilities relating to the contrast agent, the acquisition of DSC-MRI data, data pre-processing, the contrast concentration-time course, the arterial input function, deconvolution, common perfusion parameters, post-processing possibilities, patient studies, absolute versus relative quantification and automated analysis methods.

Magnetic Resonance Imaging of Glioma in the Era of Antiangiogenic Therapy

Since it supplanted computed tomography in the early 1990s, magnetic resonance (MR) imaging has remained the standard tool to evaluate disease status in patients with brain tumors. With the recent adoption of antiangiogenic therapy for gliomas, it has become increasingly clear that leakiness of the blood-brain barrier, the physiologic correlate of contrast enhancement, is affected by a multitude of pathophysiologic processes, not all of which correlate with tumor burden. To address this issue, physiologic imaging including diffusion and perfusion MR imaging has been investigated as an avenue to acquire predictive and prognostic biomarkers useful in the evaluation of high-grade gliomas.