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

How to evaluate perfusion imaging in post-treatment glioma: a comparison of three different analysis methods

INTRODUCTION

Dynamic susceptibility contrast (DSC) perfusion weighted (PW)-MRI can aid in differentiating treatment related abnormalities (TRA) from tumor progression (TP) in post-treatment glioma patients. Common methods, like the 'hot spot', or visual approach suffer from oversimplification and subjectivity. Using perfusion of the complete lesion potentially offers an objective and accurate alternative. This study aims to compare the diagnostic value and assess the subjectivity of these techniques.

METHODS

50 Glioma patients with enhancing lesions post-surgery and chemo-radiotherapy were retrospectively included. Outcome was determined by clinical/radiological follow-up or biopsy. Imaging analysis used the 'hot spot', volume of interest (VOI) and visual approach. Diagnostic accuracy was compared using receiving operator characteristics (ROC) curves for the VOI and 'hot spot' approach, visual assessment was analysed with contingency tables. Inter-operator agreement was determined with Cohens kappa and intra-class coefficient (ICC).

RESULTS

29 Patients suffered from TP, 21 had TRA. The visual assessment showed poor to substantial inter-operator agreement (κ = -0.72 - 0.68). Reliability of the 'hot spot' placement was excellent (ICC = 0.89), while reference placement was variable (ICC = 0.54). The area under the ROC (AUROC) of the mean- and maximum relative cerebral blood volume (rCBV) (VOI-analysis) were 0.82 and 0.72, while the rCBV-ratio ('hot spot' analysis) was 0.69. The VOI-analysis had a more balanced sensitivity and specificity compared to visual assessment.

CONCLUSIONS

VOI analysis of DSC PW-MRI data holds greater diagnostic accuracy in single-moment differentiation of TP and TRA than 'hot spot' or visual analysis. This study underlines the subjectivity of visual placement and assessment.

Arterial Spin-Labeling and DSC Perfusion Metrics Improve Agreement in Neuroradiologists' Clinical Interpretations of Posttreatment High-Grade Glioma Surveillance MR Imaging-An Institutional Experience

BACKGROUND AND PURPOSE

MR perfusion has shown value in the evaluation of posttreatment high-grade gliomas, but few studies have shown its impact on the consistency and confidence of neuroradiologists' interpretation in routine clinical practice. We evaluated the impact of adding MR perfusion metrics to conventional contrast-enhanced MR imaging in posttreatment high-grade glioma surveillance imaging.

MATERIALS AND METHODS

This retrospective study included 45 adults with high-grade gliomas who had posttreatment perfusion MR imaging. Four neuroradiologists assigned Brain Tumor Reporting and Data System scores for each examination on the basis of the interpretation of contrast-enhanced MR imaging and then after the addition of arterial spin-labeling-CBF, DSC-relative CBV, and DSC-fractional tumor burden. Interrater agreement and rater agreement with a multidisciplinary consensus group were assessed with κ statistics. Raters used a 5-point Likert scale to report confidence scores. The frequency of clinically meaningful score changes resulting from the addition of each perfusion metric was determined.

RESULTS

Interrater agreement was moderate for contrast-enhanced MR imaging alone (κ = 0.63) and higher with perfusion metrics (arterial spin-labeling-CBF, κ = 0.67; DSC-relative CBV, κ = 0.66; DSC-fractional tumor burden, κ = 0.70). Agreement between raters and consensus was highest with DSC-fractional tumor burden (κ = 0.66-0.80). Confidence scores were highest with DSC-fractional tumor burden. Across all raters, the addition of perfusion resulted in clinically meaningful interpretation changes in 2%-20% of patients compared with contrast-enhanced MR imaging alone.

CONCLUSIONS

Adding perfusion to contrast-enhanced MR imaging improved interrater agreement, rater agreement with consensus, and rater confidence in the interpretation of posttreatment high-grade glioma MR imaging, with the highest agreement and confidence scores seen with DSC-fractional tumor burden. Perfusion MR imaging also resulted in interpretation changes that could change therapeutic management in up to 20% of patients.

Advanced Imaging in the Diagnosis and Response Assessment of High-Grade Glioma: AJR Expert Panel Narrative Review

This AJR Expert Panel Narrative explores the current status of advanced MRI and PET techniques for the post-therapeutic response assessment of high-grade adult-type gliomas, focusing on ongoing clinical controversies in current practice. Discussed techniques that complement conventional MRI and aid the differentiation of recurrent tumor from post-treatment effects include DWI and diffusion tensor imaging; perfusion MRI techniques including dynamic susceptibility contrast (DSC), dynamic contrast-enhanced MRI, and arterial spin labeling; MR spectroscopy including assessment of 2-hydroxyglutarate (2HG) concentration; glucose- and amino acid (AA)-based PET; and amide proton transfer imaging. Updated criteria for Response Assessment in Neuro-Oncology are presented. Given the abundant supporting clinical evidence, the panel supports a recommendation that routine response assessment after HGG treatment should include perfusion MRI, particularly given the development of a consensus recommended DSC-MRI protocol. Although published studies support 2HG MRS and AA PET, these techniques' widespread adoption will likely require increased availability (for 2HG MRS) or increased insurance funding in the United States (for AA PET). The article concludes with a series of consensus opinions from the author panel, centered on the clinical integration of the advanced imaging techniques into posttreatment surveillance protocols.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions

In the follow-up treatment of high-grade gliomas (HGGs), differentiating true tumor progression from treatment-related effects, such as pseudoprogression and radiation necrosis, presents an ongoing clinical challenge. Conventional MRI with and without intravenous contrast serves as the clinical benchmark for the posttreatment surveillance imaging of HGG. However, many advanced imaging techniques have shown promise in helping better delineate the findings in indeterminate scenarios, as posttreatment effects can often mimic true tumor progression on conventional imaging. These challenges are further confounded by the histologic admixture that can commonly occur between tumor growth and treatment-related effects within the posttreatment bed. This review discusses the current practices in the surveillance imaging of HGG and the role of advanced imaging techniques, including perfusion MRI and metabolic MRI.

Evaluation of single bolus, dual-echo dynamic susceptibility contrast MRI protocols in brain tumor patients

Relative cerebral blood volume (rCBV) obtained from dynamic susceptibility contrast (DSC) MRI is adversely impacted by contrast agent leakage in brain tumors. Using simulations, we previously demonstrated that multi-echo DSC-MRI protocols provide improvements in contrast agent dosing, pulse sequence flexibility, and rCBV accuracy. The purpose of this study is to assess the in-vivo performance of dual-echo acquisitions in patients with brain tumors (n = 59). To verify pulse sequence flexibility, four single-dose dual-echo acquisitions were tested with variations in contrast agent dose, flip angle, and repetition time, and the resulting dual-echo rCBV was compared to standard single-echo rCBV obtained with preload (double-dose). Dual-echo rCBV was comparable to standard double-dose single-echo protocols (mean (standard deviation) tumor rCBV 2.17 (1.28) vs. 2.06 (1.20), respectively). High rCBV similarity was observed (CCC = 0.96), which was maintained across both flip angle (CCC = 0.98) and repetition time (CCC = 0.96) permutations, demonstrating that dual-echo acquisitions provide flexibility in acquisition parameters. Furthermore, a single dual-echo acquisition was shown to enable quantification of both perfusion and permeability metrics. In conclusion, single-dose dual-echo acquisitions provide similar rCBV to standard double-dose single-echo acquisitions, suggesting contrast agent dose can be reduced while providing significant pulse sequence flexibility and complementary tumor perfusion and permeability metrics.

Consensus recommendations for MRI and PET imaging of primary central nervous system lymphoma: guideline statement from the International Primary CNS Lymphoma Collaborative Group (IPCG)

Advanced molecular and pathophysiologic characterization of primary central nervous system lymphoma (PCNSL) has revealed insights into promising targeted therapeutic approaches. Medical imaging plays a fundamental role in PCNSL diagnosis, staging, and response assessment. Institutional imaging variation and inconsistent clinical trial reporting diminishes the reliability and reproducibility of clinical response assessment. In this context, we aimed to: (1) critically review the use of advanced positron emission tomography (PET) and magnetic resonance imaging (MRI) in the setting of PCNSL; (2) provide results from an international survey of clinical sites describing the current practices for routine and advanced imaging, and (3) provide biologically based recommendations from the International PCNSL Collaborative Group (IPCG) on adaptation of standardized imaging practices. The IPCG provides PET and MRI consensus recommendations built upon previous recommendations for standardized brain tumor imaging protocols (BTIP) in primary and metastatic disease. A biologically integrated approach is provided to addresses the unique challenges associated with the imaging assessment of PCNSL. Detailed imaging parameters facilitate the adoption of these recommendations by researchers and clinicians. To enhance clinical feasibility, we have developed both “ideal” and “minimum standard” protocols at 3T and 1.5T MR systems that will facilitate widespread adoption.

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation

PURPOSE

Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model.

METHODS

Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation.

RESULTS

Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error.

CONCLUSIONS

DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

Effect of Applying Leakage Correction on rCBV Measurement Derived From DSC-MRI in Enhancing and Nonenhancing Glioma

Purpose

Relative cerebral blood volume (rCBV) is the most widely used parameter derived from DSC perfusion MR imaging for predicting brain tumor aggressiveness. However, accurate rCBV estimation is challenging in enhancing glioma, because of contrast agent extravasation through a disrupted blood-brain barrier (BBB), and even for nonenhancing glioma with an intact BBB, due to an elevated steady-state contrast agent concentration in the vasculature after first passage. In this study a thorough investigation of the effects of two different leakage correction algorithms on rCBV estimation for enhancing and nonenhancing tumors was conducted.

Methods

Two datasets were used retrospectively in this study: 1. A publicly available TCIA dataset (49 patients with 35 enhancing and 14 nonenhancing glioma); 2. A dataset acquired clinically at Erasmus MC (EMC, Rotterdam, NL) (47 patients with 20 enhancing and 27 nonenhancing glial brain lesions). The leakage correction algorithms investigated in this study were: a unidirectional model-based algorithm with flux of contrast agent from the intra- to the extravascular extracellular space (EES); and a bidirectional model-based algorithm additionally including flow from EES to the intravascular space.

Results

In enhancing glioma, the estimated average contrast-enhanced tumor rCBV significantly (Bonferroni corrected Wilcoxon Signed Rank Test, p < 0.05) decreased across the patients when applying unidirectional and bidirectional correction: 4.00 ± 2.11 (uncorrected), 3.19 ± 1.65 (unidirectional), and 2.91 ± 1.55 (bidirectional) in TCIA dataset and 2.51 ± 1.3 (uncorrected), 1.72 ± 0.84 (unidirectional), and 1.59 ± 0.9 (bidirectional) in EMC dataset. In nonenhancing glioma, a significant but smaller difference in observed rCBV was found after application of both correction methods used in this study: 1.42 ± 0.60 (uncorrected), 1.28 ± 0.46 (unidirectional), and 1.24 ± 0.37 (bidirectional) in TCIA dataset and 0.91 ± 0.49 (uncorrected), 0.77 ± 0.37 (unidirectional), and 0.67 ± 0.34 (bidirectional) in EMC dataset.

Conclusion

Both leakage correction algorithms were found to change rCBV estimation with BBB disruption in enhancing glioma, and to a lesser degree in nonenhancing glioma. Stronger effects were found for bidirectional leakage correction than for unidirectional leakage correction.

Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.

Comparing dynamic susceptibility contrast perfusion post-processing with different clinically available software among patients affected of a high-grade glioma

BACKGROUND AND PURPOSE

The main purpose of this retrospective study was to evaluate inter-software variability in patients affected of a high-grade glioma for the post-processing of dynamic susceptibility contrast (DSC¹) perfusion imaging in MRI.² MATERIALS AND METHODS: The included patients were either anaplastic astrocytoma (WHO³ grade III) or glioblastoma (WHO grade IV) located in the cerebral parenchyma. The postprocessing of 54 MRI-DSC imaging from 46 patients using both Intellispace© (Philips) and Olea© (Olea Medical) software was performed. The hemodynamic parameter studied was the normalised relative cerebral blood volume corrected for the T1 leakage effect (nrCBVc⁴). The inter-operator variabilities were also evaluated.

RESULTS

Regarding inter-software reproducibility, Cohen's Kappa from therapeutic follow-up obtained were 0.61, close to the recommended limit (0.60). Subgroups were created to complete the analysis and to evaluate the partial volume effect. Even if necrosis or vascular structures from regions of interest (ROI⁵) were avoided, results did not improve. ROI of a minimum area of 250 mm² yielded a Cohen's Kappa of 0.65. The inter-operator reproducibility on Intellispace and Olea were 0.90 and 0.73 respectively, which is satisfactory.

CONCLUSION

The reproducibility between Intellispace and Olea was below recommended threshold in a clinical context. This discrepancy can be explained by the partial volume effect and the models used. ROI with an area of at least 250 mm² improves this reproducibility and becomes acceptable.

Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas

Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Background

Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.

Methods

Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.

Results

Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1).

Conclusions

Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.

Performance of Standardized Relative CBV for Quantifying Regional Histologic Tumor Burden in Recurrent High-Grade Glioma: Comparison against Normalized Relative CBV Using Image-Localized Stereotactic Biopsies

BACKGROUND AND PURPOSE

Perfusion MR imaging measures of relative CBV can distinguish recurrent tumor from posttreatment radiation effects in high-grade gliomas. Currently, relative CBV measurement requires normalization based on user-defined reference tissues. A recently proposed method of relative CBV standardization eliminates the need for user input. This study compares the predictive performance of relative CBV standardization against relative CBV normalization for quantifying recurrent tumor burden in high-grade gliomas relative to posttreatment radiation effects.

MATERIALS AND METHODS

We recruited 38 previously treated patients with high-grade gliomas (World Health Organization grades III or IV) undergoing surgical re-resection for new contrast-enhancing lesions concerning for recurrent tumor versus posttreatment radiation effects. We recovered 112 image-localized biopsies and quantified the percentage of histologic tumor content versus posttreatment radiation effects for each sample. We measured spatially matched normalized and standardized relative CBV metrics (mean, median) and fractional tumor burden for each biopsy. We compared relative CBV performance to predict tumor content, including the Pearson correlation (r), against histologic tumor content (0%-100%) and the receiver operating characteristic area under the curve for predicting high-versus-low tumor content using binary histologic cutoffs (≥50%; ≥80% tumor).

RESULTS

Across relative CBV metrics, fractional tumor burden showed the highest correlations with tumor content (0%-100%) for normalized (r = 0.63, P < .001) and standardized (r = 0.66, P < .001) values. With binary cutoffs (ie, ≥50%; ≥80% tumor), predictive accuracies were similar for both standardized and normalized metrics and across relative CBV metrics. Median relative CBV achieved the highest area under the curve (normalized = 0.87, standardized = 0.86) for predicting ≥50% tumor, while fractional tumor burden achieved the highest area under the curve (normalized = 0.77, standardized = 0.80) for predicting ≥80% tumor.

CONCLUSIONS

Standardization of relative CBV achieves similar performance compared with normalized relative CBV and offers an important step toward workflow optimization and consensus methodology.

Standardized acquisition and post-processing of dynamic susceptibility contrast perfusion in patients with brain tumors, cerebrovascular disease and dementia: comparability of post-processing software

OBJECTIVE

MR-perfusion post-processing still lacks standardization. This study evaluates the results of perfusion analysis with two established software solutions in a large series of patients with different diseases when a highly standardized processing workflow is ensured.

METHODS

Multicenter data of 260 patients (80 with brain tumors, 124 with cerebrovascular disease and 56 with dementia examined with the same MR protocol) were analyzed. Raw data sets were processed with two software suites: Olea sphere and NordicICE. Group differences were analyzed with paired t-tests and one-way ANOVA.

RESULTS

Perfusion metrics were significantly different for all examined diseases in the unaffected brain for both software suites [ratio cortex/white matter left hemisphere: mean transit time (MTT) 0.991 vs 0.847, p < 0.05; relative cerebral bloodflow (rBF) 3.23 vs 4.418, p < 0.001; relative cerebral bloodvolume (rBVc) 2.813 vs 3.884, p < 0.001; right hemisphere: MTT 1.079 vs 0.854, p < 0.05; rBF 3.262 vs 4.378, p < 0.001; rBVc 2.762 vs 3.935, p < 0.001)]. Perfusion results were also significantly different in patients with stroke (ratio cortex/white matter affected hemisphere: MTT 1.058 vs 0.784; p < 0.001), dementia (ratio cortex/white matter left hemisphere: rBVc 1.152 vs 1.795, p < 0.001; right hemisphere: rBVc 1.396 vs 1.662, p < 0.05) and brain tumors (ratio cortex/whole tumor rBVc: 0.778 vs 0.919, p < 0.001 and ratio cortex/tumor hotspot rBVc: 0.529 vs 0.512, p < 0.05).

CONCLUSION

Despite a highly standardized workflow, parametric perfusion maps are depended on the chosen software. Radiologists should consider software related variances when using dynamic susceptibility contrast perfusion for clinical imaging and research.

ADVANCES IN KNOWLEDGE

This multicenter study compared perfusion parameters calculated by two commercial dynamic susceptibility contrast perfusion post-processing software solutions in different central nervous system disorders with a large sample size and a highly standardized processing workflow. Despite, parametric perfusion maps are depended on the chosen software which impacts clinical imaging and research.

Perfusion MRI-Based Fractional Tumor Burden Differentiates between Tumor and Treatment Effect in Recurrent Glioblastomas and Informs Clinical Decision-Making

BACKGROUND AND PURPOSE

Fractional tumor burden better correlates with histologic tumor volume fraction in treated glioblastoma than other perfusion metrics such as relative CBV. We defined fractional tumor burden classes with low and high blood volume to distinguish tumor from treatment effect and to determine whether fractional tumor burden can inform treatment-related decision-making.

MATERIALS AND METHODS

Forty-seven patients with high-grade gliomas (primarily glioblastoma) with recurrent contrast-enhancing lesions on DSC-MR imaging were retrospectively evaluated after surgical sampling. Histopathologic examination defined treatment effect versus tumor. Normalized relative CBV thresholds of 1.0 and 1.75 were used to define low, intermediate, and high fractional tumor burden classes in each histopathologically defined group. Performance was assessed with an area under the receiver operating characteristic curve. Consensus agreement among physician raters reporting hypothetic changes in treatment-related decisions based on fractional tumor burden was compared with actual real-time treatment decisions.

RESULTS

Mean lower fractional tumor burden, high fractional tumor burden, and relative CBV of the contrast-enhancing volume were significantly different between treatment effect and tumor (P = .002, P < .001, and P < .001), with tumor having significantly higher fractional tumor burden and relative CBV and lower fractional tumor burden. No significance was found with intermediate fractional tumor burden. Performance of the area under the receiver operating characteristic curve was the following: high fractional tumor burden, 0.85; low fractional tumor burden, 0.7; and relative CBV, 0.81. In comparing treatment decisions, there were disagreements in 7% of tumor and 44% of treatment effect cases; in the latter, all disagreements were in cases with scattered atypical cells.

CONCLUSIONS

High fractional tumor burden and low fractional tumor burden define fractions of the contrast-enhancing lesion volume with high and low blood volume, respectively, and can differentiate treatment effect from tumor in recurrent glioblastomas. Fractional tumor burden maps can also help to inform clinical decision-making.

Longitudinal, leakage corrected and uncorrected rCBV during the first-line treatment of glioblastoma: a prospective study

PURPOSE

Dynamic susceptibility contrast (DSC) MR-perfusion is becoming a standard of care for the monitoring of glioblastoma. Yet, technical standards are lacking and measurements without leakage correction are still common. Also, data on leakage corrected measurements during stable disease is scarce. In this study we hypothesized that basic leakage correction would significantly enhance data quality during stable disease and improve progress detection. We furthermore investigated whether longitudinal data could increase diagnostic performance.

METHODS

Patients with histologically proven glioblastoma undergoing first-line therapy were prospectively recruited. We conducted DSC perfusion measurements without prebolus administration in 6-week intervals from the end of radiotherapy until progression. Maximum relative cerebral volume values (rCBV_max) with and without leakage correction were calculated using Philips IntelliSpace®.

RESULTS

We recruited 16 patients and conducted 82 MRI scans with a mean follow up of 7.2 month. During stable disease, corrected rCBV_max was significantly more stable than uncorrected rCBV_max. Detection of progression with a rCBV_max cutoff was better for corrected (specificity 86%) than for uncorrected rCBV_max (specificity 41%). Interestingly, the increase of corrected rCBV_max upon progression also had a good diagnostic performance with a combination of both cutoffs delivering the best result (sensitivity/specificity 89%/93%).

CONCLUSION

Corrected rCBV_max supports the imaging finding of a stable disease and large increases during longitudinal observation support the diagnosis of tumor progression. rCBV values without prebolus or leakage correction are not reliable to monitor glioblastomas. Further studies to investigate the value of longitudinal rCBV dynamics for the differentiation of real tumor progression from pseudoprogression are warranted.

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

BACKGROUND AND PURPOSE

MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data.

MATERIALS AND METHODS

We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models.

RESULTS

Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%).

CONCLUSIONS

Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Wavelet-based reconstruction of dynamic susceptibility MR-perfusion: a new method to visualize hypervascular brain tumors

OBJECTIVES

Parameter maps based on wavelet-transform post-processing of dynamic perfusion data offer an innovative way of visualizing blood vessels in a fully automated, user-independent way. The aims of this study were (i) a proof of concept regarding wavelet-based analysis of dynamic susceptibility contrast (DSC) MRI data and (ii) to demonstrate advantages of wavelet-based measures compared to standard cerebral blood volume (CBV) maps in patients with the initial diagnosis of glioblastoma (GBM).

METHODS

Consecutive 3-T DSC MRI datasets of 46 subjects with GBM (mean age 63.0 ± 13.1 years, 28 m) were retrospectively included in this feasibility study. Vessel-specific wavelet magnetic resonance perfusion (wavelet-MRP) maps were calculated using the wavelet transform (Paul wavelet, order 1) of each voxel time course. Five different aspects of image quality and tumor delineation were each qualitatively rated on a 5-point Likert scale. Quantitative analysis included image contrast and contrast-to-noise ratio.

RESULTS

Vessel-specific wavelet-MRP maps could be calculated within a mean time of 2:27 min. Wavelet-MRP achieved higher scores compared to CBV in all qualitative ratings: tumor depiction (4.02 vs. 2.33), contrast enhancement (3.93 vs. 2.23), central necrosis (3.86 vs. 2.40), morphologic correlation (3.87 vs. 2.24), and overall impression (4.00 vs. 2.41); all p < .001. Quantitative image analysis showed a better image contrast and higher contrast-to-noise ratios for wavelet-MRP compared to conventional perfusion maps (all p < .001).

CONCLUSIONS

wavelet-MRP is a fast and fully automated post-processing technique that yields reproducible perfusion maps with a clearer vascular depiction of GBM compared to standard CBV maps.

KEY POINTS

• Wavelet-MRP offers high-contrast perfusion maps with a clear delineation of focal perfusion alterations. • Both image contrast and visual image quality were beneficial for wavelet-MRP compared to standard perfusion maps like CBV. • Wavelet-MRP can be automatically calculated from existing dynamic susceptibility contrast (DSC) perfusion data.

MMP-2 expression and correlation with pathology and MRI of glioma

The expression of matrix metalloproteinase-2 (MMP-2) in brain glioma and its correlation with patients' clinicopathological characteristics and magnetic resonance imaging (MRI) features were investigated. A total of 104 patients with brain glioma admitted and treated in the First Affiliated Hospital of Anhui Medical University from June 2010 to September 2014 were randomly enrolled. MRI examination was performed before operation. Immunohistochemistry (IHC) was used to detect the expression levels of MMP-2 in brain glioma tissues and paired normal brain tissues after operation and to analyze the associations of MMP-2 expression with the clinicopathological characteristics of brain glioma and survival time of patients. The relationship between MMP-2 expression and preoperative MRI features of glioma was analyzed. The positive rate of MMP-2 expression in brain glioma was 73.08% (76/104), while that in paired normal brain tissues was only 12.5% (13/104), obviously lower than that in brain glioma tissues (P<0.05). The MMP-2 expression in the body of glioma was not related to the patients' sex, age, tumor location and pathological type (P>0.05), but there was a significant correlation with the tumor diameter and pathological grade of the patients (P<0.05). Analysis by Cox model suggested that tumor diameter, pathological grade and MMP-2 were independent prognostic factors for glioma (P<0.05). The overall survival (OS) of patients in the positive MMP-2 expression group was 16.4 months, while the OS in the negative MMP-2 expression group was 20.16 months, and the difference between the two groups was statistically significant (P<0.05). The positive expression of MMP-2 in glioma was closely related to the uniformity of MRI signal for tumor, tumor diameter, severity of peritumoral edema, degree of enhancement and pathological grade of tumor (P<0.05). MMP-2 is highly expressed in brain glioma, and it is a negative factor for prognosis. Therefore, the MRI manifestations of glioma can reflect to some extent the intensity of MMP-2 expression.

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

BACKGROUND AND PURPOSE

Standard assessment criteria for brain tumors that only include anatomic imaging continue to be insufficient. While numerous studies have demonstrated the value of DSC-MR imaging perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MR imaging metrics across sites and platforms. This study addresses this limitation with a comparison of multisite/multiplatform analyses of shared DSC-MR imaging datasets of patients with brain tumors.

MATERIALS AND METHODS

DSC-MR imaging data were collected after a preload and during a bolus injection of gadolinium contrast agent using a gradient recalled-echo-EPI sequence (TE/TR = 30/1200 ms; flip angle = 72°). Forty-nine low-grade (n = 13) and high-grade (n = 36) glioma datasets were uploaded to The Cancer Imaging Archive. Datasets included a predetermined arterial input function, enhancing tumor ROIs, and ROIs necessary to create normalized relative CBV and CBF maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement among sites was assessed with the Lin concordance correlation coefficient. Distinction of low- from high-grade tumors was evaluated with the Wilcoxon rank sum test followed by receiver operating characteristic analysis to identify the optimal thresholds based on sensitivity and specificity.

RESULTS

For normalized relative CBV and normalized CBF, 93% and 94% of entries showed good or excellent cross-site agreement (0.8 ≤ Lin concordance correlation coefficient ≤ 1.0). All metrics could distinguish low- from high-grade tumors. Optimum thresholds were determined for pooled data (normalized relative CBV = 1.4, sensitivity/specificity = 90%:77%; normalized CBF = 1.58, sensitivity/specificity = 86%:77%).

CONCLUSIONS

By means of DSC-MR imaging data obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing low- from high-grade tumors.

Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload

BACKGROUND AND PURPOSE

Several leakage correction algorithms for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI)-based cerebral blood volume (CBV) measurement have been proposed, and combination with a preload of contrast agent is generally recommended. A single bolus application scheme would largely simplify and facilitate standardized clinical applications, while reducing contrast agent (CA) dose. The aim of this study was, therefore, to investigate whether appropriate leakage correction redundantizes prebolus application by comparing normalized DSC-based CBV (nCBV) measures of two consecutive CA boli.

MATERIALS AND METHODS

Twenty-seven patients with suspected glioblastoma (WHO-grade-IV) underwent DSC-MRI during two consecutive boli of Gd-based CA. Four variants of two post-processing leakage correction techniques were compared with respect to nCBV in contrast enhancing tumor tissue. First, a reference curve approach with first pass and full integration of corrected ΔR2*(t), and second, a deconvolution-based approach using singular value decomposition (SVD) with a standard noise-dependent cutoff or Tikhonov regularization.

RESULTS

Compared to respective uncorrected values, all leakage correction techniques increased nCBV for data acquired without prebolus, while there was no consistent trend for data acquired with prebolus. The best agreement between corrected nCBV values in contrast enhancing tumor, obtained in the same patients without and with prebolus, respectively, was obtained for the reference curve-based correction approach with either first pass or full integration.

CONCLUSION

The reference curve-based leakage correction approach with integration-based nCBV calculation yielded a high accordance between nCBV values without and with prebolus, respectively. Thus, it appears possible to obtain valid nCBV in glioblastoma with a single CA injection.

Pseudoprogression of brain tumors

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high-grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day-to-day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9-30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow-up. The combination of MRS and diffusion-weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer-aided diagnostic technology, and meticulous evaluation with clinical and pathological data.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI

In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function, integrity, and architecture. Even after two decades of use, these modalities continue to evolve as their biophysical and kinetic basis is better understood, with improvements in pulse sequences and accelerated imaging techniques and through application of more robust and automated data analysis strategies. Here, we systematically review each of these elements, with a focus on how their integration improves kinetic parameter accuracy and the development of new hemodynamic biomarkers that provide sub-voxel sensitivity (e.g., capillary transit time and flow heterogeneity). Regarding contrast mechanisms, we discuss the dipole-dipole interactions and susceptibility effects that give rise to simultaneous T₁, T₂ and T₂^∗ relaxation effects, including their quantification, influence on pulse sequence parameter optimization, and use in methods such as vessel size and vessel architectural imaging. The application of technologic advancements, such as parallel imaging, simultaneous multi-slice, undersampled k-space acquisitions, and sliding window strategies, enables improved spatial and/or temporal resolution of DSC and DCE acquisitions. Such acceleration techniques have also enabled the implementation of, clinically feasible, simultaneous multi-echo spin- and gradient echo acquisitions, providing more comprehensive and quantitative interrogation of T₁, T₂ and T₂^∗ changes. Characterizing these relaxation rate changes through different post-processing options allows for the quantification of hemodynamics and vascular permeability. The application of different biophysical models provides insight into traditional hemodynamic parameters (e.g., cerebral blood volume) and more advanced parameters (e.g., capillary transit time heterogeneity). We provide insight into the appropriate selection of biophysical models and the necessary post-processing steps to ensure reliable measurements while minimizing potential sources of error. We show representative examples of advanced DSC- and DCE-MRI methods applied to pathologic conditions affecting the cerebral microcirculation, including brain tumors, stroke, aging, and multiple sclerosis. The maturation and standardization of conventional DSC- and DCE-MRI techniques has enabled their increased integration into clinical practice and use in clinical trials, which has, in turn, spurred renewed interest in their technological and biophysical development, paving the way towards a more comprehensive assessment of cerebral hemodynamics.

Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice

Objectives

At a European Society of Neuroradiology (ESNR) Annual Meeting 2015 workshop, commonalities in practice, current controversies and technical hurdles in glioma MRI were discussed. We aimed to formulate guidance on MRI of glioma and determine its feasibility, by seeking information on glioma imaging practices from the European Neuroradiology community.

Methods

Invitations to a structured survey were emailed to ESNR members (n=1,662) and associates (n=6,400), European national radiologists’ societies and distributed via social media.

Results

Responses were received from 220 institutions (59% academic). Conventional imaging protocols generally include T2w, T2-FLAIR, DWI, and pre- and post-contrast T1w. Perfusion MRI is used widely (85.5%), while spectroscopy seems reserved for specific indications. Reasons for omitting advanced imaging modalities include lack of facility/software, time constraints and no requests. Early postoperative MRI is routinely carried out by 74% within 24–72 h, but only 17% report a percent measure of resection. For follow-up, most sites (60%) issue qualitative reports, while 27% report an assessment according to the RANO criteria. A minority of sites use a reporting template (23%).

Conclusion

Clinical best practice recommendations for glioma imaging assessment are proposed and the current role of advanced MRI modalities in routine use is addressed.

Key Points

• We recommend the EORTC-NBTS protocol as the clinical standard glioma protocol.

• Perfusion MRI is recommended for diagnosis and follow-up of glioma.

• Use of advanced imaging could be promoted with increased education activities.

• Most response assessment is currently performed qualitatively.

• Reporting templates are not widely used, and could facilitate standardisation.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5314-5) contains supplementary material, which is available to authorized users.

Imaging in neuro-oncology

Imaging plays several key roles in managing brain tumors, including diagnosis, prognosis, and treatment response assessment. Ongoing challenges remain as new therapies emerge and there are urgent needs to find accurate and clinically feasible methods to noninvasively evaluate brain tumors before and after treatment. This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize several key areas of their applications, including improving the accuracy of diagnosis and addressing the challenging clinical problems such as evaluation of pseudoprogression and anti-angiogenic therapy, and rising challenges of imaging with immunotherapy.

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.

Relative cerebral blood volume is a potential biomarker in late delayed radiation-induced brain injury

PURPOSE

To assess whether relative cerebral blood volume (rCBV) can provide information to reliably evaluate the stages of late delayed radiation-induced brain injury.

MATERIALS AND METHODS

Forty patients diagnosed with late delayed radiation-induced brain injury were enrolled. The patients were examined using a 1.5T magnetic resonance imaging (MRI) system equipped with an 8-channel head coil. An echo planar imaging (EPI) sequence was used in perfusion-weighted imaging (PWI). The location of 1H-MR spectroscopy scanning was acquired by a point-resolved spectroscopy sequence. Lesions of the temporal lobe were divided into one of two groups according to rCBV value: rCBV<1 (low rCBV [group 1; n = 45]); and rCBV>1 (elevated rCBV [group 2; n = 14]). PWI and MRS parameters, as well as morphological lesion types, in these two groups were compared. Morphological severity was assessed independently and agreed on by two imaging specialists (J.L. and H.X.S., with 16 and 24 years' experience, respectively). If necessary, a third imaging professor (Z.M.H.) with 30 years' experience resolved disagreement(s). Standards for evaluating morphological lesion types were based on previously published criteria. After testing the skewness of data, the Mann-Whitney U-test or Student's t-test was used, as appropriate.

RESULTS

rCBV, relative cerebral blood flow (rCBF), and relative mean transit time (rMTT) in group 2 (n = 14) were significantly higher than in group 1 (n = 45) (rCBV: 1.21 ± 0.38 vs. 0.72 ± 0.32, respectively; P < 0.001; rCBF: 1.13 ± 0.02 vs. 0.74 ± 0.04, respectively; P < 0.001; rMTT: 1.10 ± 0.26 vs. 0.96 ± 0.20, P < 0.001). The levels of choline-containing compounds (CHO) / creatine (Cr) and CHO/N-acetylaspartate (NAA) in group 1 were significantly greater than in group 2 (CHO/Cr: 1.89 ± 1.83 vs. 1.22 ± 1.31, respectively; P = 0.016; CHO/NAA: 1.85 ± 3.50 vs. 1.17 ± 0.75, respectively; P = 0.022). More severe morphological lesions were present in lesions with low rCBV compared with elevated rCBV (overall severity: 7.00 ± 4.25 vs. 5.00 ± 5.13, respectively; P = 0.029).

CONCLUSION

Elevated rCBV accompanied by a more conservative metabolic pattern and milder lesion(s) may represent a less advanced stage in the development of late delayed radiation-induced brain injury.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1112-1118.

Progress in neuro-imaging of brain tumors

PURPOSE OF REVIEW

Magnetic resonance imaging (MRI) is routinely employed in the diagnosis and clinical management of brain tumors. This review provides an overview of the advancements in the field of MRI, with a particular focus on the quantitative assessment by advanced physiological magnetic resonance techniques in light of the new molecular classification of brain tumor.

RECENT FINDINGS

Understanding how molecular phenotypes of brain tumors are reflected in noninvasive imaging is the goal of radiogenomics, which aims at determining the association between imaging features and molecular markers in neuro-oncology. Advanced MRI techniques such as diffusion magnetic resonance imaging and perfusion-weighted imaging add important structural, hemodynamic, and physiological information for tumor diagnosis and classification, as well as to stratify tumor response. Magnetic resonance spectroscopy is able to depict with unprecedented accuracy metabolic biomarkers, which are relevant for molecular subtyping. Ultra-high-field imaging enhances anatomical detail and enables to explore new horizon in tumor imaging.

SUMMARY

The noninvasive MRI-based assessment of tumor malignancy and molecular status may offer the opportunity to predict prognosis and to select patients who may be candidates for individualized targeted therapies, providing more sensitive tools for their follow-up.

Discrimination between Glioma Grades II and III Using Dynamic Susceptibility Perfusion MRI: A Meta-Analysis

BACKGROUND

DSC perfusion has been evaluated in the discrimination between low-grade and high-grade glioma but the diagnostic potential to discriminate beween glioma grades II and III remains unclear.

PURPOSE

Our aim was to evaluate the diagnostic accuracy of relative maximal CBV from DSC perfusion MR imaging to discriminate glioma grades II and III.

DATA SOURCES

A systematic literature search was performed in PubMed/MEDLINE, Embase, Web of Science, and ClinicalTrials.gov.

STUDY SELECTION

Eligible studies reported on patients evaluated with relative maximal CBV derived from DSC with a confirmed neuropathologic diagnosis of glioma World Health Organization grades II and III. Studies reporting on mean or individual patient data were considered for inclusion.

DATA ANALYSIS

Data were analyzed by using inverse variance with the random-effects model and receiver operating characteristic curves describing optimal cutoffs and areas under the curve. Bivariate diagnostic random-effects meta-analysis was used to calculate diagnostic accuracy.

DATA SYNTHESIS

Twenty-eight studies evaluating 727 individuals were included in the meta-analysis. Individual data were available from 10 studies comprising 190 individuals. The mean difference of relative maximal CBV between glioma grades II and III (n = 727) was 1.76 (95% CI, 1.27-2.24; P < .001). Individual patient data (n = 190) had an area under the curve of 0.77 for discriminating glioma grades II and III at an optimal cutoff of 2.02. When we analyzed astrocytomas separately, the area under the curve increased to 0.86 but decreased to 0.61 when we analyzed oligodendrogliomas.

LIMITATIONS

A substantial heterogeneity was found among included studies.

CONCLUSIONS

Glioma grade III had higher relative maximal CBV compared with glioma grade II. A high diagnostic accuracy was found for all patients and astrocytomas; however, the diagnostic accuracy was substantially reduced when discriminating oligodendroglioma grades II and III.

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.

Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation

Relative cerebral blood volume (rCBV) is a magnetic resonance imaging biomarker that is used to differentiate progression from pseudoprogression in patients with glioblastoma multiforme, the most common primary brain tumor. However, calculated rCBV depends considerably on the software used. Automating all steps required for rCBV calculation is important, as user interaction can lead to increased variability and possible inaccuracies in clinical decision-making. Here, we present an automated tool for computing rCBV from dynamic susceptibility contrast-magnetic resonance imaging that includes leakage correction. The entrance and exit bolus time points are automatically calculated using wavelet-based detection. The proposed tool is compared with 3 Food and Drug Administration-approved software packages, 1 automatic and 2 requiring user interaction, on a data set of 43 patients. We also evaluate manual and automated white matter (WM) selection for normalization of the cerebral blood volume maps. Our system showed good agreement with 2 of the 3 software packages. The intraclass correlation coefficient for all comparisons between the same software operated by different people was >0.880, except for FuncTool when operated by user 1 versus user 2. Little variability in agreement between software tools was observed when using different WM selection techniques. Our algorithm for automatic rCBV calculation with leakage correction and automated WM selection agrees well with 2 out of the 3 FDA-approved software packages.

Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective

Purpose

Magnetic resonance imaging (MRI) plays a key role in neurooncology, i.e., for diagnosis, treatment evaluation and detection of recurrence. However, standard MRI cannot always separate malignant tissue from other pathologies or treatment-induced changes. Advanced MRI techniques such as diffusion-weighted imaging, perfusion imaging and spectroscopy show promising results in discriminating malignant from benign lesions. Further, supplemental imaging with amino acid positron emission tomography (PET) has been shown to increase accuracy significantly and is used routinely at an increasing number of sites. Several centers are now implementing hybrid PET/MRI systems allowing for multiparametric imaging, combining conventional MRI with advanced MRI and amino acid PET imaging. Neurooncology is an obvious focus area for PET/MR imaging.

Methods

Based on the literature and our experience from more than 300 PET/MRI examinations of brain tumors with ¹⁸F-fluoro-ethyl-tyrosine, the clinical use of PET/MRI in adult and pediatric neurooncology is critically reviewed.

Results

Although the results are increasingly promising, the added value and range of indications for multiparametric imaging with PET/MRI are yet to be established. Robust solutions to overcome the number of issues when using a PET/MRI scanner are being developed, which is promising for a more routine use in the future.

Conclusions

In a clinical setting, a PET/MRI scan may increase accuracy in discriminating recurrence from treatment changes, although sequential same-day imaging on separate systems will often constitute a reliable and cost-effective alternative. Pediatric patients who require general anesthesia will benefit the most from simultaneous PET and MR imaging.

Dynamic Susceptibility Contrast MR Imaging in Glioma: Review of Current Clinical Practice

Dynamic susceptibility contrast (DSC) MR imaging, a perfusion-weighted MR imaging technique typically used in neuro-oncologic applications for estimating the relative cerebral blood volume within brain tumors, has demonstrated much potential for determining prognosis, predicting therapeutic response, and assessing early treatment response of gliomas. This review highlights recent developments using DSC-MR imaging and emphasizes the need for technical standardization and validation in prospective studies in order for this technique to become incorporated into standard-of-care imaging for patients with brain tumors.

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.

MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis

BACKGROUND

Distinction between tumor and treatment related changes is crucial for clinical management of patients with high-grade gliomas. Our purpose was to evaluate whether dynamic susceptibility contrast-enhanced (DSC) and dynamic contrast enhanced (DCE) perfusion-weighted imaging (PWI) metrics can effectively differentiate between recurrent tumor and posttreatment changes within the enhancing signal abnormality on conventional MRI.

METHODS

A comprehensive literature search was performed for studies evaluating PWI-based differentiation of recurrent tumor and posttreatment changes in patients with high-grade gliomas (World Health Organization grades III and IV). Only studies published in the "temozolomide era" beginning in 2005 were included. Summary estimates of diagnostic accuracy were obtained by using a random-effects model.

RESULTS

Of 1581 abstracts screened, 28 articles were included. The pooled sensitivities and specificities of each study's best performing parameter were 90% and 88% (95% CI: 0.85-0.94; 0.83-0.92) and 89% and 85% (95% CI: 0.78-0.96; 0.77-0.91) for DSC and DCE, respectively. The pooled sensitivities and specificities for detecting tumor recurrence using the 2 most commonly evaluated parameters, mean relative cerebral blood volume (rCBV) (threshold range, 0.9-2.15) and maximum rCBV (threshold range, 1.49-3.1), were 88% and 88% (95% CI: 0.81-0.94; 0.78-0.95) and 93% and 76% (95% CI: 0.86-0.98; 0.66-0.85), respectively.

CONCLUSIONS

PWI-derived thresholds separating viable tumor from treatment changes demonstrate relatively good accuracy in individual studies. However, because of significant variability in optimal reported thresholds and other limitations in the existing body of literature, further investigation and standardization is needed before implementing any particular quantitative PWI strategy across institutions.

Technical Pitfalls of Signal Truncation in Perfusion MRI of Glioblastoma

Dynamic susceptibility contrast (DSC) perfusion-weighted imaging (PWI) is widely used in clinical settings for the radiological diagnosis of brain tumor. The signal change in brain tissue in gradient echo-based DSC PWI is much higher than in spin echo-based DSC PWI. Due to its exquisite sensitivity, gradient echo-based sequence is the preferred method for imaging of all tumors except those near the base of the skull. However, high sensitivity also comes with a dynamic range problem. It is not unusual for blood volume to increase in gene-mediated cytotoxic immunotherapy-treated glioblastoma patients. The increase of fractional blood volume sometimes saturates the MRI signal during first-pass contrast bolus arrival and presents signal truncation artifacts of various degrees in the tumor when a significant amount of blood exists in the image pixels. It presents a hidden challenge in PWI, as this signal floor can be either close to noise level or just above and can go no lower. This signal truncation in the signal intensity time course is a significant issue that deserves attention in DSC PWI. In this paper, we demonstrate that relative cerebral blood volume and relative cerebral blood flow (rCBF) are underestimated due to signal truncation in DSC perfusion, in glioblastoma patients. We propose the use of second-pass tissue residue function in rCBF calculation using least-absolute-deviation deconvolution to avoid the underestimation problem.