Diagnosis of brain tumors using dynamic contrast-enhanced perfusion imaging with a short acquisition time

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

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

Influence of scan duration on dynamic contrast -enhanced magnetic resonance imaging pharmacokinetic parameters for brain lesions

OBJECTIVE

Gadolinium-based contrast agent needs time to leak into the extravascular-extracellular space, leak back into the vascular space, and reach an equilibrium state. For this reason, acquisition times of <10 min may cause inaccurate estimation of pharmacokinetic parameters. Since no studies have been conducted on the influence of long scan times on DCE-MRI parameters in brain tumors, the aim of this study is to investigate the variation of DCE-MRI-derived kinetic parameters as a function of acquisition time, from 5 to 10 min in brain tumors.

MATERIALS AND METHODS

Fifty-two patients with histologically confirmed brain tumors were enrolled in this retrospective study, and examination at 3 T, DCE-MRI, with scan duration of 10 min, was used for retrospective generation of 6 sets of quantitative DCE-MRI maps (Ktrans, Ve and Kep) from 5 to 10 min. Features were extracted from the DCE-MRI maps in contrast enhancement (CE) volumes. Kruskal-Wallis with post-hoc correction and coefficient of variation (CoV) were used as statistical test to compare DCE-MRI maps obtained from 6 data sets.

SIGNIFICANCE

p < 0.05.

RESULTS

No differences in Ktrans features in CE volumes between different scan durations. Ve, Kep features in CE volumes were influenced by different data length. The highest number of significantly different Ve and Kep features in CE volumes were between 5 min and 10 min (p < 0.013), 5 min and 9 min (p < 0.044), 6 min and 10 min (p < 0.040). CoV of Kep was reduced from 5 min to 10 min, going from highly variable (CoV = 0.70) to mildly variable (CoV = 0.42).

CONCLUSION

Kep and Ve were time-dependent in brain tumors, so a longer scan time is needed to obtain reliable parameter values. Ktrans was found to be time-independent, as it remains the same in all 6 acquisition times and is the only reliable parameter with short acquisition times.

Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning

Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today's clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism ("oxygen metabolic radiomics") and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO₂) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO₂; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO₂ and mitoPO₂ values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.

Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data

Simple Summary

The pretreatment diagnosis of contrast-enhancing brain tumors is still challenging in clinical neuro-oncology due to their very similar appearance on conventional MRI. A precise initial characterization, however, is essential to initiate appropriate treatment management, which can substantially differ between brain tumor entities. To overcome the disadvantage of the low specificity of conventional MRI, several new neuroimaging methods have been developed and validated over the past decades. This increasing amount of diagnostic information makes a timely evaluation without computational support impossible in a clinical setting. Artificial intelligence methods such as machine learning offer new options to support clinicians. In this study, we combined nine common machine learning algorithms with a physiological MRI technique (we named this approach “radiophysiomics”) to investigate the effectiveness of the multiclass classification of contrast-enhancing brain tumors in a clinical setting. We were able to demonstrate that radiophysiomics could be helpful in the routine diagnostics of contrast-enhancing brain tumors, but further automation using deep neural networks is required.

Abstract

The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks.

The Extension of the LeiCNS-PK3.0 Model in Combination with the "Handshake" Approach to Understand Brain Tumor Pathophysiology

Micrometastatic brain tumor cells, which cause recurrence of malignant brain tumors, are often protected by the intact blood–brain barrier (BBB). Therefore, it is essential to deliver effective drugs across not only the disrupted blood-tumor barrier (BTB) but also the intact BBB to effectively treat malignant brain tumors. Our aim is to predict pharmacokinetic (PK) profiles in brain tumor regions with the disrupted BTB and the intact BBB to support the successful drug development for malignant brain tumors. LeiCNS-PK3.0, a comprehensive central nervous system (CNS) physiologically based pharmacokinetic (PBPK) model, was extended to incorporate brain tumor compartments. Most pathophysiological parameters of brain tumors were obtained from literature and two missing parameters of the BTB, paracellular pore size and expression level of active transporters, were estimated by fitting existing data, like a “handshake”. Simultaneous predictions were made for PK profiles in extracellular fluids (ECF) of brain tumors and normal-appearing brain and validated on existing data for six small molecule anticancer drugs. The LeiCNS-tumor model predicted ECF PK profiles in brain tumor as well as normal-appearing brain in rat brain tumor models and high-grade glioma patients within twofold error for most data points, in combination with estimated paracellular pore size of the BTB and active efflux clearance at the BTB. Our model demonstrated a potential to predict PK profiles of small molecule drugs in brain tumors, for which quantitative information on pathophysiological alterations is available, and contribute to the efficient and successful drug development for malignant brain tumors.

Blood-Brain Barrier Permeability in Patients With Reversible Cerebral Vasoconstriction Syndrome Assessed With Dynamic Contrast-Enhanced MRI

BACKGROUND AND OBJECTIVES

Blood-brain barrier (BBB) disruption has been proposed to be important in the pathogenesis of reversible cerebral vasoconstriction syndrome (RCVS), but not all patients present an identifiable macroscopic BBB disruption; that is, visible contrast leakage on contrast-enhanced T2 fluid-attenuated inversion recovery imaging. This study aimed to evaluate microscopic BBB permeability and its dynamic change in patients with RCVS.

METHODS

This prospective cohort implemented 3T dynamic contrast-enhanced MRI. We measured microscopic BBB permeability by determining the whole-brain and white matter hyperintensity (WMH) K^trans values and evaluated the correlation of whole-brain K^trans permeability with clinical and vascular measures in transcranial color-coded sonography.

RESULTS

In total, 176 patients (363 scans) were analyzed and separated into acute (≦30 days) and remission (≧90 days) groups based on the onset-to-examination time. Whole-brain K^trans values were similar between patients with and without macroscopic BBB disruption in either acute or remission stage. The whole-brain K^trans was significantly decreased (p < 0.001) from acute to remission stages. The WMH K^trans was significantly higher than mirror references and decreased from acute to remission stages (p < 0.001). Whole-brain K^trans correlated with mean pulsatility index (r _s = 0.5, p = 0.029), mean resistance index (r _s = 0.662, p = 0.002), and distal-to-proximal ratio of resistance index (r _s = 0.801, p < 0.001) of M1 segment of middle cerebral arteries at around 10-15 days after onset. The time-trend curve of whole-brain K^trans depicted dynamic changes during disease course, similar to temporal trends of vasoconstrictions and WMH.

DISCUSSION

Patients with RCVS presented increased microscopic brain permeability during acute stage, even without discernible macroscopic BBB disruption. The dynamic changes in BBB permeability may be related to impaired cerebral microvascular compliance and WMH formation.

Contrast Pediatric Brain Perfusion: Dynamic Susceptibility Contrast and Dynamic Contrast-Enhanced MR Imaging

Magnetic resonance (MR) perfusion is a robust imaging technique that assesses the passage of blood through the cerebral vascular network using a variety of techniques. The applications of MR perfusion have been expanded and is well suited to investigate cerebrovascular diseases and cerebral neoplastic processes in pediatric patients. Assessment of brain perfusion can augment the information obtained on conventional MR imaging and provides additional information on the biological and physiologic features of pediatric brain tumors. Similarly, MR perfusion can help guide the management of a variety of pediatric cerebrovascular diseases, including acute ischemic stroke and Moyamoya syndrome.

Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI

The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.

How tissue T1-variability influences DCE-MRI perfusion parameters estimation of recurrent high-grade glioma after surgery followed by radiochemotherapy

BACKGROUND

Quantification of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) kinetic parameters (KPs) requires a determination of native tissue T1. Two approaches are adopted: (i) tissue T1-maps are acquired; and (ii) an a priori T1 value (fT1) is fixed for all patients (fT1-approach). Although it is more attractive, the fT1-approach might bias the results of KP calculations due to tissue T1 variability.

PURPOSE

To quantify the tissue T1 variability of recurrent high-grade glioma (HGG) and the error in KP estimation when the fT1-approach is adopted.

MATERIAL AND METHODS

We reviewed the postoperative MRI scans of 28 patients with recurrent HGG after radiochemotherapy. MRI study included T1-maps from multiple-dynamic multiple-echo imaging, DCE-MRI, and contrast enhanced T1-weighted images. KPs were calculated using T1-map and fT1-approach.

RESULTS

The tissue T1 variability of recurrent HGG was relevant. The absolute error in KP estimation, as a function of the deviation of fT1 from the true value, was 8% every 100 ms. The difference between the KPs obtained with fT1-approach from fT1 values of 1300, 1390, and 1500 ms and their reference values were mostly within the 95% confidence interval (± 1.96 standard deviation). Conversely, using fT1 values of 900, 1200, 1600, and 1900 ms causes a significant error in KP estimation (P<0.05).

CONCLUSION

Recurrent HGG is characterized by a substantial T1 variability. Although the fT1-approach does not account for this variability, it results in a minor effect on the KP estimations provided the fT1 value is in the range of 1300-1500 ms.

Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI

OBJECTIVE

This study aimed to evaluate whether arterial input functions (AIFs) obtained from dynamic susceptibility contrast (DSC)-MRI (AIF_DSC) improve the reliability and diagnostic accuracy of dynamic contrast-enhanced (DCE)-derived pharmacokinetic (PK) parameters for differentiating glioblastoma from primary CNS lymphoma (PCNSL) compared with AIFs derived from DCE-MRI (AIF_DCE).

METHODS

This retrospective study included 172 patients with glioblastoma (n = 147) and PCNSL (n = 25). All patients had undergone preoperative DSC- and DCE-MRI. The volume transfer constant (K^trans), volume of the vascular plasma space (v_p), and volume of the extravascular extracellular space (v_e) were acquired using AIF_DSC and AIF_DCE. The relative cerebral blood volume (rCBV) was obtained from DSC-MRI. Intraclass correlation coefficients (ICC) and ROC curves were used to assess the reliability and diagnostic accuracy of individual parameters.

RESULTS

The mean K^trans, v_p, and v_e values revealed better ICCs with AIF_DSC than with AIF_DCE (K^trans, 0.911 vs 0.355; v_p, 0.766 vs 0.503; v_e, 0.758 vs 0.657, respectively). For differentiating all glioblastomas from PCNSL, the mean rCBV (AUC = 0.856) was more accurate than the AIF_DSC-driven mean K^trans, which had the largest AUC (0.711) among the DCE-derived parameters (p = 0.02). However, for glioblastomas with low rCBV (≤ 75th percentile of PCNSL; n = 30), the AIF_DSC-driven mean K^trans and v_p were more accurate than rCBV (AUC: K^trans, 0.807 vs rCBV, 0.515, p = 0.004; v_p, 0.715 vs rCBV, p = 0.045).

CONCLUSION

DCE-derived PK parameters using the AIF_DSC showed improved reliability and diagnostic accuracy for differentiating glioblastoma with low rCBV from PCNSL.

KEY POINTS

• An accurate differential diagnosis of glioblastoma and PCNSL is crucial because of different therapeutic strategies. • In contrast to the rCBV from DSC-MRI, another perfusion imaging technique, the DCE parameters for the differential diagnosis have been limited because of the low reliability of AIFs from DCE-MRI. • When we analyzed DCE-MRI data using AIFs from DSC-MRI (AIF_DSC), AIF_DSC-driven DCE parameters showed improved reliability and better diagnostic accuracy than rCBV for differentiating glioblastoma with low rCBV from PCNSL.

Current landscape and future perspectives in preclinical MR and PET imaging of brain metastasis

Brain metastasis (BM) is a major cause of cancer patient morbidity. Clinical magnetic resonance imaging (MRI) and positron emission tomography (PET) represent important resources to assess tumor progression and treatment responses. In preclinical research, anatomical MRI and to some extent functional MRI have frequently been used to assess tumor progression. In contrast, PET has only to a limited extent been used in animal BM research. A considerable culprit is that results from most preclinical studies have shown little impact on the implementation of new treatment strategies in the clinic. This emphasizes the need for the development of robust, high-quality preclinical imaging strategies with potential for clinical translation. This review focuses on advanced preclinical MRI and PET imaging methods for BM, describing their applications in the context of what has been done in the clinic. The strengths and shortcomings of each technology are presented, and recommendations for future directions in the development of the individual imaging modalities are suggested. Finally, we highlight recent developments in quantitative MRI and PET, the use of radiomics and multimodal imaging, and the need for a standardization of imaging technologies and protocols between preclinical centers.

Diagnostic Value of Diffusion-weighted Magnetic Resonance (MR) Imaging, MR Perfusion, and MR Spectroscopy in Addition to Conventional MR Imaging in Intracranial Space-occupying Lesions

PURPOSE

Our aim was to determine the diagnostic performance of the combined usage of diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS) and perfusion MR (MRP) imaging for the differential diagnosis of benign and malignant intracranial lesions.

MATERIALS AND METHODS

A total of 30 patients with intracranial lesions who were prospectively evaluated with contrast-enhanced magnetic resonance imaging (MRI), DWI, MRS, and MRP were included in this study. The lesions were classified as benign and malignant according to the radiologic findings. All imaging data were compared with the histopathologic results and follow-up of the patients. We used the Pearson chi-square test and Fischer's exact t-test for statistical analysis.

RESULTS

For the differentiation of benign and malignant brain lesions, CBV and choline/creatine (Cho/Cr) ratio at short echo time (TE) had the highest sensitivity (87%-88%), Cho/N-acetyl aspartate (NAA) at short TE had the highest specificity (86%). DWI predicted 77% sensitivity, 75% specificity; MRP presented 91% sensitivity, 88% specificity; MRS yielded 77% sensitivity, 63% specificity. The combination of either DWI and MRS, MRP and MRS or DWI+MRS+MRP revealed 100% sensitivity, 100% specificity.

CONCLUSION

For the differentiation of benign and malignant brain lesions, the combination of DWI, MRS, and MRP predicted 100% sensitivity. Invasive procedures like transcranial biopsy were not required via the usage of these advanced MRI techniques.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PURPOSE

Glioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis.

METHOD

In a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods.

RESULTS

All five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (S_COR), contrary to SWI images which showed different values of two parameters (angular second moment-S_ASM and contrast-S_CON). Only three fractal features (binary box dimension-D_bin, normalized box dimension-D_norm and lacunarity-λ) measured on T2W and D_norm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (S_IDM) on T2W and S_IDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images.

CONCLUSIONS

Computational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.

Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging

Background

Differentiating glioblastoma, brain metastasis, and central nervous system lymphoma (CNSL) on conventional magnetic resonance imaging (MRI) can present a diagnostic dilemma due to the potential for overlapping imaging features. We investigate whether machine learning evaluation of multimodal MRI can reliably differentiate these entities.

Methods

Preoperative brain MRI including diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion in patients with glioblastoma, lymphoma, or metastasis were retrospectively reviewed. Perfusion maps (rCBV, rCBF), permeability maps (K-trans, Kep, Vp, Ve), ADC, T1C+ and T2/FLAIR images were coregistered and two separate volumes of interest (VOIs) were obtained from the enhancing tumor and non-enhancing T2 hyperintense (NET2) regions. The tumor volumes obtained from these VOIs were utilized for supervised training of support vector classifier (SVC) and multilayer perceptron (MLP) models. Validation of the trained models was performed on unlabeled cases using the leave-one-subject-out method. Head-to-head and multiclass models were created. Accuracies of the multiclass models were compared against two human interpreters reviewing conventional and diffusion-weighted MR images.

Results

Twenty-six patients enrolled with histopathologically-proven glioblastoma (n=9), metastasis (n=9), and CNS lymphoma (n=8) were included. The trained multiclass ML models discriminated the three pathologic classes with a maximum accuracy of 69.2% accuracy (18 out of 26; kappa 0.540, P=0.01) using an MLP trained with the VpNET2 tumor volumes. Human readers achieved 65.4% (17 out of 26) and 80.8% (21 out of 26) accuracies, respectively. Using the MLP VpNET2 model as a computer-aided diagnosis (CADx) for cases in which the human reviewers disagreed with each other on the diagnosis resulted in correct diagnoses in 5 (19.2%) additional cases.

Conclusions

Our trained multiclass MLP using VpNET2 can differentiate glioblastoma, brain metastasis, and CNS lymphoma with modest diagnostic accuracy and provides approximately 19% increase in diagnostic yield when added to routine human interpretation.

Comparison of T1 mapping and fixed T1 method for dynamic contrast-enhanced MRI perfusion in brain gliomas

OBJECTIVES

To compare dynamic contrast-enhanced MRI (DCE-MRI) data obtained using different prebolus T1 values in glioma grading and molecular profiling.

METHODS

We retrospectively reviewed 83 cases of gliomas: 46 lower-grade gliomas (LGG; grades II and III) and 37 high-grade gliomas (HGG; grade IV). DCE-MRI maps of plasma volume fraction (Vp), extravascular-extracellular volume fraction (Ve), and tracer transfer constant from plasma to tissue (K^trans) were obtained using a fixed T1 value of 1400 ms and a measured T1 obtained with variable flip angle (VFA). Tumour segmentations were performed and first-order histogram parameters were extracted from volumes of interest (VOIs) after co-registration with the perfusion maps. The two methods were compared using Wilcoxon matched-pairs signed-rank test and Bland-Altman analysis. Diagnostic accuracy was obtained and compared using ROC curve analysis and DeLong's test.

RESULTS

Perfusion parameters obtained with the fixed T1 value were significantly higher than those obtained with the VFA. As regards diagnostic accuracy, there were no significant differences between the two methods both for glioma grading and molecular classification, except for few parameters of both methods.

CONCLUSIONS

DCE-MRI data obtained with different prebolus T1 are not comparable and the definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI.

KEY POINTS

• DCE-MRI data obtained with different prebolus T1 are significantly different, thus not comparable. • The definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI for glioma grading. • The use of a fixed T1 value represents a valid alternative to T1 mapping for DCE-MRI analysis.

Comparison of transport of chemotherapeutic drugs in voxelized heterogeneous model of human brain tumor

Systemic administration of chemotherapeutic drugs is widely used in the treatment of cancer. However, a good understanding of drug transport barriers that influence the treatment efficacy is still lacking. In this study, a voxelized numerical model based on dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) and computational fluid dynamics (CFD) is employed to study the transport and efficacy of three different chemotherapeutic drugs, namely methotrexate, doxorubicin and cisplatin in human brain tumors. DCE-MRI data provides realistic heterogeneous vasculature of the tumor, the permeability of tissue to contrast agent, interstitial volume fraction (porosity) of the tissue and patient-specific arterial input function (AIF). The permeability of tissue to aforementioned drugs is determined by correlating it with the permeability of tissue to the contrast agent. The model is employed to simulate drug concentration in the tissue and compare the effect of heterogeneous vasculature on the distribution of the drugs in the tumor. The drug accumulation is observed to be higher in high permeability areas initially, and in higher porosity areas at later times. Furthermore, it is observed that methotrexate remains in the interstitial space of the tumor in higher concentration for a longer duration as compared to other two drugs, facilitating more tumor cell killing.

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.

Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis

OBJECTIVES

Differentiation of glioma from brain metastasis is clinically crucial because it affects the clinical outcome of patients and alters patient management. Here, we present a systematic review and meta-analysis of the currently available data on perfusion magnetic resonance imaging (MRI) for differentiating glioma from brain metastasis, assessing MRI protocols and parameters.

METHODS

A computerised search of Ovid-MEDLINE and EMBASE databases was performed up to 3 October 2017, to find studies on the diagnostic performance of perfusion MRI for differentiating glioma from brain metastasis. Pooled summary estimates of sensitivity and specificity were obtained using hierarchical logistic regression modelling. We conducted meta-regression and subgroup analyses to explain the effects of the study heterogeneity.

RESULTS

Eighteen studies with 900 patients were included. The pooled sensitivity and specificity were 90% (95% CI, 84-94%) and 91% (95% CI, 84-95%), respectively. The area under the hierarchical summary receiver operating characteristic curve was 0.96 (95% CI, 0.94-0.98). The meta-regression showed that the percentage of glioma in the study population and the study design were significant factors affecting study heterogeneity. In a subgroup analysis including patients with glioblastoma only, the pooled sensitivity was 92% (95% CI, 84-97%) and the pooled specificity was 94% (95% CI, 85-98%).

CONCLUSIONS

Although various perfusion MRI techniques were used, the current evidence supports the use of perfusion MRI to differentiate glioma from brain metastasis. In particular, perfusion MRI showed excellent diagnostic performance for differentiating glioblastoma from brain metastasis.

KEY POINTS

• Perfusion MRI shows high diagnostic performance for differentiating glioma from brain metastasis. • The pooled sensitivity was 90% and pooled specificity was 91%. • Peritumoral rCBV derived from DSC is a relatively well-validated.

The role of cerebral blood flow gradient in peritumoral edema for differentiation of glioblastomas from solitary metastatic lesions

OBJECTIVE

Differentiation of glioblastomas from solitary brain metastases using conventional MRI remains an important unsolved problem. In this study, we introduced the conception of the cerebral blood flow (CBF) gradient in peritumoral edema-the difference in CBF values from the proximity of the enhancing tumor to the normal-appearing white matter, and investigated the contribution of perfusion metrics on the discrimination of glioblastoma from a metastatic lesion.

MATERIALS AND METHODS

Fifty-two consecutive patients with glioblastoma or a solitary metastatic lesion underwent three-dimensional arterial spin labeling (3D-ASL) before surgical resection. The CBF values were measured in the peritumoral edema (near: G1; Intermediate: G2; Far: G3). The CBF gradient was calculated as the subtractions CBFG1 -CBFG3, CBFG1 - CBFG2 and CBFG2 - CBFG3. A receiver operating characteristic (ROC) curve analysis was used to seek for the best cutoff value permitting discrimination between these two tumors.

RESULTS

The absolute/related CBF values and the CBF gradient in the peritumoral regions of glioblastomas were significantly higher than those in metastases(P < 0.038). ROC curve analysis reveals, a cutoff value of 1.92 ml/100g for the CBF gradient of CBFG1 -CBFG3 generated the best combination of sensitivity (92.86%) and specificity (100.00%) for distinguishing between a glioblastoma and metastasis.

CONCLUSION

The CBF gradient in peritumoral edema appears to be a more promising ASL perfusion metrics in differentiating high grade glioma from a solitary metastasis.

Numerical Study of Transport of Anticancer Drugs in Heterogeneous Vasculature of Human Brain Tumors Using Dynamic Contrast Enhanced-Magnetic Resonance Imaging

Systemic administration of drugs in tumors is a challenging task due to unorganized microvasculature and nonuniform extravasation. There is an imperative need to understand the transport behavior of drugs when administered intravenously. In this study, a transport model is developed to understand the therapeutic efficacy of a free drug and liposome-encapsulated drugs (LED), in heterogeneous vasculature of human brain tumors. Dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data is employed to model the heterogeneity in tumor vasculature that is directly mapped onto the computational fluid dynamics (CFD) model. Results indicate that heterogeneous vasculature leads to preferential accumulation of drugs at the tumor position. Higher drug accumulation was found at location of higher interstitial volume, thereby facilitating more tumor cell killing at those areas. Liposome-released drug (LRD) remains inside the tumor for longer time as compared to free drug, which together with higher concentration enhances therapeutic efficacy. The interstitial as well as intracellular concentration of LRD is found to be 2–20 fold higher as compared to free drug, which are in line with experimental data reported in literature. Close agreement between the predicted and experimental data demonstrates the potential of the developed model in modeling the transport of LED and free drugs in heterogeneous vasculature of human tumors.

Multiparametric Analysis of Permeability and ADC Histogram Metrics for Classification of Pediatric Brain Tumors by Tumor Grade

BACKGROUND AND PURPOSE

Accurate tumor grading is essential for treatment planning of pediatric brain tumors. We hypothesized that multiparametric analyses of a combination of permeability metrics and ADC histogram metrics would differentiate high- and low-grade tumors with high accuracy.

MATERIALS AND METHODS

DTI and dynamic contrast-enhanced MR imaging using T1-mapping with flip angles of 2°, 5°, 10°, and 15°, followed by a 0.1-mmol/kg body weight gadolinium-based bolus was performed on all patients in addition to standard MR imaging. Permeability data were processed and transfer constant from the blood plasma into the extracellular extravascular space, rate constant from the extracellular extravascular space back into blood plasma, extravascular extracellular volume fraction, and fractional blood plasma volume were calculated from 3D tumor volumes. Apparent diffusion coefficient histogram metrics were calculated for 3 separate tumor volumes derived from T2-FLAIR sequences, T1 contrast-enhanced sequences, and permeability maps, respectively.

RESULTS

Results from 41 patients (0.3-16.76 years of age; mean, 6.22 years) with newly diagnosed contrast-enhancing brain tumors (16 low-grade; 25 high-grade) were included in the institutional review board-approved retrospective analysis. Wilcoxon tests showed a higher transfer constant from blood plasma into extracellular extravascular space and rate constant from extracellular extravascular space back into blood plasma, and lower extracellular extravascular volume fraction (P < .001) in high-grade tumors. The mean ADCs of FLAIR and enhancing tumor volumes were significantly lower in high-grade tumors (P < .001). ROC analysis showed that a combination of extravascular volume fraction and mean ADC of FLAIR volume differentiated high- and low-grade tumors with high accuracy (area under receiver operating characteristic curve = 0.918).

CONCLUSIONS

ADC histogram metrics combined with permeability metrics differentiate low- and high-grade pediatric brain tumors with high accuracy.

Aberrant glioblastoma neovascularization patterns and their correlation with DCE-MRI-derived parameters following temozolomide and bevacizumab treatment

Glioblastoma (GBM) is a highly angiogenic malignancy, and its abundant, aberrant neovascularization is closely related to the proliferation and invasion of tumor cells. However, anti-angiogenesis combined with standard radio-/chemo-therapy produces little improvement in treatment outcomes. Determining the reason for treatment failure is pivotal for GBM treatment. Here, histopathological analysis and dynamic contrast-enhanced MRI (DCE-MRI) were used to explore the effects of temozolomide (TMZ) and bevacizumab (BEV) on GBM neovascularization patterns in an orthotopic U87MG mouse model at 1, 3 and 6 days after treatment. We found that the amount of vascular mimicry (VM) significantly increased 6 days after BEV treatment. TMZ inhibited neovascularization at an early stage, but the microvessel density (MVD) and transfer coefficient (Ktrans) derived from DCE-MRI increased 6 days after treatment. TMZ and BEV combination therapy slightly prolonged the inhibitory effect on tumor microvessels. Sprouting angiogenesis was positively correlated with Ktrans in all treatment groups. The increase in VM after BEV administration and the increase in MVD and Ktrans after TMZ administration may be responsible for treatment resistance. Ktrans holds great potential as an imaging biomarker for indicating the variation in sprouting angiogenesis during drug treatment for GBM.

Usefulness of enhancement-perfusion mismatch in differentiation of CNS lymphomas from other enhancing malignant tumors of the brain

Background

Surgical planning and treatment options for primary or secondary central nervous system lymphomas (PCNSL or SCNSL) are different from other enhancing malignant lesions such as glioblastoma multiforme (GBM), anaplastic gliomas and metastases; so, it is critical to distinguish them preoperatively. We hypothesized that enhancement-perfusion (E-P) mismatch on dynamic susceptibility weighted magnetic resonance (DSC-MR) perfusion imaging which corresponds to low mean relative cerebral blood volume (mean rCBV) in an enhancing portion of the tumor should allow differentiation of CNS lymphomas from other enhancing malignant lesions.

Methods

We retrospectively reviewed pre-treatment MRI exams, including DSC-MR perfusion images of 15 lymphoma patients. As a control group, pre-treatment DSC-MR perfusion images of biopsy proven 18 GBMs (group II), 13 metastases (group III), and 10 anaplastic enhancing gliomas (group IV) patients were also reviewed. Region of interests (ROIs) were placed around the most enhancing part of tumor on contrast-enhanced T1WI axial images and images were transferred onto co-registered DSC perfusion maps to obtain CBV in all 4 groups. The mean and maximum relative CBV values were obtained. Statistical analysis was performed on SPSS software and significance of the results between the groups was done with Mann-Whitney test, whereas optimal thresholds for tumor differentiation were done by receiver operating characteristic (ROC) analysis.

Results

The enhancing component of CNS lymphomas were found to have significantly lower mean rCBV compared to enhancing component of GBM (1.2 versus 4.3; P<0.001), metastasis (1.2 versus 2.7; P<0.001), and anaplastic enhancing gliomas (1.2 versus 2.4; P<0.001). Maximum rCBV of enhancing component of lymphoma were significantly lower than GBM (3.1 versus 6.5; P<0.001) and metastasis (3.1 versus 4.9; P<0.013), and not significantly lower than anaplastic enhancing gliomas (3.9 versus 4.2; P<0.08). On the basis of ROC analysis, mean rCBV provided the best threshold [area under the curve (AUC) =0.92] and had better accuracy in differentiating malignant lesions.

Conclusions

E-P mismatch in DSC perfusion MR, i.e., low mean rCBV in an enhancing portion of the tumor is strongly suggestive of lymphoma and should allow differentiation of CNS lymphoma from other enhancing malignant lesions.

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

Purpose:

We evaluated the diagnostic performance of histogram analysis of data from a combination of dynamic susceptibility contrast (DSC)-MRI and dynamic contrast-enhanced (DCE)-MRI for quantitative differentiation between central nervous system lymphoma (CNSL) and high-grade glioma (HGG), with the aim of identifying useful perfusion parameters as objective radiological markers for differentiating between them.

Methods:

Eight lesions with CNSLs and 15 with HGGs who underwent MRI examination, including DCE and DSC-MRI, were enrolled in our retrospective study. DSC-MRI provides a corrected cerebral blood volume (cCBV), and DCE-MRI provides a volume transfer coefficient (K^trans) for transfer from plasma to the extravascular extracellular space. K^trans and cCBV were measured from a round region-of-interest in the slice of maximum size on the contrast-enhanced lesion. The differences in t values between CNSL and HGG for determining the most appropriate percentile of K^trans and cCBV were investigated. The differences in K^trans, cCBV, and K^trans/cCBV between CNSL and HGG were investigated using histogram analysis. Receiver operating characteristic (ROC) analysis of K^trans, cCBV, and K^trans/cCBV ratio was performed.

Results:

The 30th percentile (C30) in K^trans and 80th percentile (C80) in cCBV were the most appropriate percentiles for distinguishing between CNSL and HGG from the differences in t values. CNSL showed significantly lower C80 cCBV, significantly higher C30 K^trans, and significantly higher C30 K^trans/C80 cCBV than those of HGG. In ROC analysis, C30 K^trans/C80 cCBV had the best discriminative value for differentiating between CNSL and HGG as compared to C30 K^trans or C80 cCBV.

Conclusion:

The combination of K^trans by DCE-MRI and cCBV by DSC-MRI was found to reveal the characteristics of vascularity and permeability of a lesion more precisely than either K^trans or cCBV alone. Histogram analysis of these vascular microenvironments enabled quantitative differentiation between CNSL and HGG.

Validation of Blood Volume Fraction Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Porcine Skeletal Muscle

The purpose of this study was to assess the accuracy of fractional blood volume (v_b) estimates in low-perfused and low-vascularized tissue using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The results of different MRI methods were compared with histology to evaluate the accuracy of these methods under clinical conditions. v_b was estimated by DCE-MRI using a 3D gradient echo sequence with k-space undersampling in five muscle groups in the hind leg of 9 female pigs. Two gadolinium-based contrast agents (CA) were used: a rapidly extravasating, extracellular, gadolinium-based, low-molecular-weight contrast agent (LMCA, gadoterate meglumine) and an extracellular, gadolinium-based, albumin-binding, slowly extravasating blood pool contrast agent (BPCA, gadofosveset trisodium). LMCA data were evaluated using the extended Tofts model (ETM) and the two-compartment exchange model (2CXM). The images acquired with administration of the BPCA were used to evaluate the accuracy of v_b estimation with a bolus deconvolution technique (BD) and a method we call equilibrium MRI (EqMRI). The latter calculates the ratio of the magnitude of the relaxation rate change in the tissue curve at an approximate equilibrium state to the height of the same area of the arterial input function (AIF). Immunohistochemical staining with isolectin was used to label endothelium. A light microscope was used to estimate the fractional vascular area by relating the vascular region to the total tissue region (immunohistochemical vessel staining, IHVS). In addition, the percentage fraction of vascular volume was determined by multiplying the microvascular density (MVD) with the average estimated capillary lumen, π(d2)2, where d = 8μm is the assumed capillary diameter (microvascular density estimation, MVDE). Except for ETM values, highly significant correlations were found between most of the MRI methods investigated. In the cranial thigh, for example, the v_b medians (interquartile range, IQRs) of IHVS, MVDE, BD, EqMRI, 2CXM and ETM were v_b = 0.7(0.3)%, 1.1(0.4)%, 1.1(0.4)%, 1.4(0.3)%, 1.2(1.8)% and 0.1(0.2)%, respectively. Variances, expressed by the difference between third and first quartiles (IQR) were highest for the 2CXM for all muscle groups. High correlations between the values in four muscle groups—medial, cranial, lateral thigh and lower leg - estimated with MRI and histology were found between BD and EqMRI, MVDE and 2CXM and IHVS and ETM. Except for the ETM, no significant differences between the v_b medians of all MRI methods were revealed with the Wilcoxon rank sum test. The same holds for all muscle regions using the 2CXM and MVDE. Except for cranial thigh muscle, no significant difference was found between EqMRI and MVDE. And except for the cranial thigh and the lower leg muscle, there was also no significant difference between the v_b medians of BD and MVDE. Overall, there was good v_b agreement between histology and the BPCA MRI methods and the 2CXM LMCA approach with the exception of the ETM method. Although LMCA models have the advantage of providing excellent curve fits and can in principle determine more physiological parameters than BPCA methods, they yield more inaccurate results.

Diagnostic Accuracy of T1-Weighted Dynamic Contrast-Enhanced-MRI and DWI-ADC for Differentiation of Glioblastoma and Primary CNS Lymphoma

BACKGROUND AND PURPOSE

Glioblastoma and primary CNS lymphoma dictate different neurosurgical strategies; it is critical to distinguish them preoperatively. However, current imaging modalities do not effectively differentiate them. We aimed to examine the use of DWI and T1-weighted dynamic contrast-enhanced-MR imaging as potential discriminative tools.

MATERIALS AND METHODS

We retrospectively reviewed 18 patients with primary CNS lymphoma and 36 matched patients with glioblastoma with pretreatment DWI and dynamic contrast-enhanced-MR imaging. VOIs were drawn around the tumor on contrast-enhanced T1WI and FLAIR images; these images were transferred onto coregistered ADC maps to obtain the ADC and onto dynamic contrast-enhanced perfusion maps to obtain the plasma volume and permeability transfer constant. Histogram analysis was performed to determine the mean and relative ADC_mean and relative 90th percentile values for plasma volume and the permeability transfer constant. Nonparametric tests were used to assess differences, and receiver operating characteristic analysis was performed for optimal threshold calculations.

RESULTS

The enhancing component of primary CNS lymphoma was found to have significantly lower ADC_mean (1.1 × 10^-3 versus 1.4 × 10^-3; P < .001) and relative ADC_mean (1.5 versus 1.9; P < .001) and relative 90th percentile values for plasma volume (3.7 versus 5.0; P < .05) than the enhancing component of glioblastoma, but not significantly different relative 90th percentile values for the permeability transfer constant (5.4 versus 4.4; P = .83). The nonenhancing portions of glioblastoma and primary CNS lymphoma did not differ in these parameters. On the basis of receiver operating characteristic analysis, mean ADC provided the best threshold (area under the curve = 0.83) to distinguish primary CNS lymphoma from glioblastoma, which was not improved with normalized ADC or the addition of perfusion parameters.

CONCLUSIONS

ADC was superior to dynamic contrast-enhanced-MR imaging perfusion, alone or in combination, in differentiating primary CNS lymphoma from glioblastoma.

Automated Processing of Dynamic Contrast-Enhanced MRI: Correlation of Advanced Pharmacokinetic Metrics with Tumor Grade in Pediatric Brain Tumors

BACKGROUND AND PURPOSE

Pharmacokinetic parameters from dynamic contrast-enhanced MR imaging have proved useful for differentiating brain tumor grades in adults. In this study, we retrospectively reviewed dynamic contrast-enhanced perfusion data from children with newly diagnosed brain tumors and analyzed the pharmacokinetic parameters correlating with tumor grade.

MATERIALS AND METHODS

Dynamic contrast-enhanced MR imaging data from 38 patients were analyzed by using commercially available software. Subjects were categorized into 2 groups based on pathologic analyses consisting of low-grade (World Health Organization I and II) and high-grade (World Health Organization III and IV) tumors. Pharmacokinetic parameters were compared between the 2 groups by using linear regression models. For parameters that were statistically distinct between the 2 groups, sensitivity and specificity were also estimated.

RESULTS

Eighteen tumors were classified as low-grade, and 20, as high-grade. Transfer constant from the blood plasma into the extracellular extravascular space (K^trans), rate constant from extracellular extravascular space back into blood plasma (K_ep), and extracellular extravascular volume fraction (V_e) were all significantly correlated with tumor grade; high-grade tumors showed higher K^trans, higher K_ep, and lower V_e. Although all 3 parameters had high specificity (range, 82%-100%), K_ep had the highest specificity for both grades. Optimal sensitivity was achieved for V_e, with a combined sensitivity of 76% (compared with 71% for K^trans and K_ep).

CONCLUSIONS

Pharmacokinetic parameters derived from dynamic contrast-enhanced MR imaging can effectively discriminate low- and high-grade pediatric brain tumors.