Diagnostic accuracy of dynamic contrast-enhanced perfusion MRI in stratifying gliomas: A systematic review and meta-analysis

Background T1‐weighted dynamic contrast‐enhanced (DCE) perfusion magnetic resonance imaging (MRI) has been broadly utilized in the evaluation of brain tumors. We aimed at assessing the diagnostic accuracy of DCE‐MRI in discriminating between low‐grade gliomas (LGGs) and high‐grade gliomas (HGGs), between tumor recurrence and treatment‐related changes, and between primary central nervous system lymphomas (PCNSLs) and HGGs. Methods We performed this study based on the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis of Diagnostic Test Accuracy Studies criteria. We systematically surveyed studies evaluating the diagnostic accuracy of DCE‐MRI for the aforementioned entities. Meta‐analysis was conducted with the use of a random effects model. Results Twenty‐seven studies were included after screening of 2945 possible entries. We categorized the eligible studies into three groups: those utilizing DCE‐MRI to differentiate between HGGs and LGGs (14 studies, 546 patients), between recurrence and treatment‐related changes (9 studies, 298 patients) and between PCNSLs and HGGs (5 studies, 224 patients). The pooled sensitivity, specificity, and area under the curve for differentiating HGGs from LGGs were 0.93, 0.90, and 0.96, for differentiating tumor relapse from treatment‐related changes were 0.88, 0.86, and 0.89, and for differentiating PCNSLs from HGGs were 0.78, 0.81, and 0.86, respectively. Conclusions Dynamic contrast‐enhanced‐Magnetic resonance imaging is a promising noninvasive imaging method that has moderate or high accuracy in stratifying gliomas. DCE‐MRI shows high diagnostic accuracy in discriminating between HGGs and their low‐grade counterparts, and moderate diagnostic accuracy in discriminating recurrent lesions and treatment‐related changes as well as PCNSLs and HGGs

Pre- and Post-Treatment Imaging of Primary Central Nervous System Tumors in the Molecular and Genetic Era

Recent advances in the molecular and genetic characterization of central nervous system (CNS) tumors have ushered in a new era of tumor classification, diagnosis, and prognostic assessment. In this emerging and rapidly evolving molecular genetic era, imaging plays a critical role in the preoperative diagnosis and surgical planning, molecular marker prediction, targeted treatment planning, and post-therapy assessment of CNS tumors. This review provides an overview of the current imaging methods relevant to the molecular genetic classification of CNS tumors. Specifically, we focused on 1) the correlates between imaging features and specific molecular genetic markers and 2) the post-therapy imaging used for therapeutic assessment.ope

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

Treatment evaluation of patients with glioblastomas is important to aid in clinical decisions. Conventional MRI with contrast is currently the standard method, but unable to differentiate tumor progression from treatment-related effects. Pseudoprogression appears as new enhancement, and thus mimics tumor progression on conventional MRI. Contrarily, a decrease in enhancement or edema on conventional MRI during antiangiogenic treatment can be due to pseudoresponse and is not necessarily reflective of a favorable outcome. Neovascularization is a hallmark of tumor progression but not for posttherapeutic effects. Perfusion-weighted MRI provides a plethora of additional parameters that can help to identify this neovascularization. This review shows that perfusion MRI aids to identify tumor progression, pseudoprogression, and pseudoresponse. The review provides an overview of the most applicable perfusion MRI methods and their limitations. Finally, future developments and remaining challenges of perfusion MRI in treatment evaluation in neuro-oncology are discussed. Level of Evidence: 3. Technical Efficacy: Stage 4. J. Magn. Reson. Imaging 2019;49:11–22

Quantifying effects of radiotherapy-induced microvascular injury; review of established and emerging brain MRI techniques

Microvascular changes are increasingly recognised not only as primary drivers of radiotherapy treatment response in brain tumours, but also as an important contributor to short- and long-term (cognitive) side effects arising from irradiation of otherwise healthy brain tissue. As overall survival of patients with brain tumours is increasing, monitoring long-term sequels of radiotherapy-induced microvascular changes in the context of their potential predictive power for outcome, such as cognitive disability, has become increasingly relevant. Ideally, radiotherapy-induced significant microvascular changes in otherwise healthy brain tissue should be identified as early as possible to facilitate adaptive radiotherapy and to proactively start treatment to minimise the influence on these side-effects on the final outcome. Although MRI is already known to be able to detect significant long-term radiotherapy induced microvascular effects, more recently advanced MR imaging biomarkers reflecting microvascular integrity and function have been reported and might provide a more accurate and earlier detection of microvascular changes. However, the use and validation of both established and new techniques in the context of monitoring early and late radiotherapy-induced microvascular changes in both target-tissue and healthy tissue currently are minimal at best. This review aims to summarise the performance and limitations of existing methods and future opportunities for detection and quantification of radiotherapy-induced microvascular changes, as well as the relation of these findings with key clinical parameters. (C) 2019 Elsevier B.V. All rights reserved

Diagnostic accuracy of positron emission tomography tracers for the differentiation of tumor progression from treatment-related changes in high-grade glioma:a systematic review and meta-analysis

Background: Post-treatment high-grade gliomas are usually monitored with contrast-enhanced MRI, but its diagnostic accuracy is limited as it cannot adequately distinguish between true tumor progression and treatment-related changes. According to recent response assessment in neuro-oncology (RANO) recommendations PET overcomes this limitation. However, it is currently unknown which tracer yields the best results. Therefore, a systematic review and meta-analysis were performed to compare the diagnostic accuracy of the different PET tracers in differentiating tumor progression from treatment-related changes in high-grade glioma patients. Methods: Pubmed, Web of Science and Embase were searched systematically. Study selection, data extraction and quality assessment were performed independently by two authors. Meta-analysis was performed using a bivariate random effects model when ≥ 5 studies were included. Results: 39 studies (11 tracers) were included in the systematic review. 18F-FDG (12 studies, 171 lesions) showed a pooled sensitivity and specificity of 84% (95%CI 72-92) and 84% (69-93), respectively. 18F-FET (7 studies, 172 lesions) demonstrated a sensitivity of 90% (81-95) and specificity of 85% (71-93). 11C-MET (8 studies, 151 lesions) sensitivity was 93% (80-98) and specificity was 82% (68-91). The number of included studies for the other tracers were too low to combine, but sensitivity and specificity ranged between 93-100% and 0-100% for 18F-FLT, 85-100% and 72-100% for 18F-FDOPA and 100% and 70-88% for 11C-CHO, respectively. Conclusion:18F-FET and 11C-MET, both amino-acid tracers, showed a comparable higher sensitivity than 18F-FDG in the differentiation between tumor progression and treatment-related changes in high-grade glioma patients. The evidence for other tracers is limited, thus 18F-FET and 11C-MET are preferred when available. Our results support the incorporation of amino-acid PET tracers for the treatment evaluation of high-grade gliomas

MR imaging biomarkers in neuro-oncology

The central role of magnetic resonance imaging (MRI) in neuro-oncology is undisputed, to diagnose and monitor disease activity, provide treatment decision support and guide focused treatments, and to determine response to treatment both in clinical practice and in clinical trials. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily applied on the basis of qualitative, subjective interpretation of macrostructural features rather than quantitatively and with taking pathophysiological features into account. The field of quantitative imaging and imaging biomarker development is however maturing. The European Imaging Biomarker ALLiance (EIBALL) and Quantitative Imaging Biomarker Alliance (QIBA) are important drivers setting standards for development, validation and implementation, and promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological parameters that further drive the discovery, validation, and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques convert medical images into objective high-dimensional data to define radiomic signatures of disease states. This review addresses the definition and current state of MRI biomarkers, as well as quantitative image analysis techniques with clinical potential for neuro-oncology

Diagnostic accuracy of magnetic resonance imaging techniques for treatment response evaluation in patients with high-grade glioma, a systematic review and meta-analysis

Treatment response assessment in high-grade gliomas uses contrast enhanced T1-weighted MRI, but is unreliable. Novel advanced MRI techniques have been studied, but the accuracy is not well known. Therefore, we performed a systematic meta-analysis to assess the diagnostic accuracy of anatomical and advanced MRI for treatment response in high-grade gliomas. Databases were searched systematically. Study selection and data extraction were done by two authors independently. Meta-analysis was performed using a bivariate random effects model when ae5 studies were included. Anatomical MRI (five studies, 166 patients) showed a pooled sensitivity and specificity of 68% (95%CI 51-81) and 77% (45-93), respectively. Pooled apparent diffusion coefficients (seven studies, 204 patients) demonstrated a sensitivity of 71% (60-80) and specificity of 87% (77-93). DSC-perfusion (18 studies, 708 patients) sensitivity was 87% (82-91) with a specificity of 86% (77-91). DCE-perfusion (five studies, 207 patients) sensitivity was 92% (73-98) and specificity was 85% (76-92). The sensitivity of spectroscopy (nine studies, 203 patients) was 91% (79-97) and specificity was 95% (65-99). Advanced techniques showed higher diagnostic accuracy than anatomical MRI, the highest for spectroscopy, supporting the use in treatment response assessment in high-grade gliomas. aEuro cent Treatment response assessment in high-grade gliomas with anatomical MRI is unreliable aEuro cent Novel advanced MRI techniques have been studied, but diagnostic accuracy is unknown aEuro cent Meta-analysis demonstrates that advanced MRI showed higher diagnostic accuracy than anatomical MRI aEuro cent Highest diagnostic accuracy for spectroscopy and perfusion MRI aEuro cent Supports the incorporation of advanced MRI in high-grade glioma treatment response assessment

Characterising Peritumoural Progression of Glioblastoma using Multimodal MRI

Glioblastoma is a highly malignant tumor which mostly recurs locally around the resected contrast enhancement. However, it is difficult to identify tumor invasiveness pre-surgically, especially in non-enhancing areas. Thus, the aim of this thesis was to utilize multimodal MR technique to identify and characterize the peritumoral progression zone that eventually leads to tumor progression. Patients with newly diagnosed cerebral glioblastoma were included consecutively from our cohort between 2010 and2014. The presurgical MRI sequences included volumetric T1-weighted with contrast, FLAIR, T2-weighted, diffusion-weighted imaging, diffusion tensor and perfusion MR imaging. Postsurgical and follow-up MRI included structural and ADC images. Image deformation, caused by disease nature and surgical procedure, renders routine coregistration methods inadequate for MRIs comparison between different time points. Therefore, a two-staged non-linear semi-automatic coregistration method was developed from the modification of the linear FLIRT and non-linear FNIRT functions in FMRIB’s Software Library (FSL). Utilising the above mentioned coregistration method, a volumetric study was conducted to analyse the extent of resection based on different MR techniques, including T1 weighted with contrast, FLAIR and DTI measures of isotropy (DTI-p) and anisotropy (DTI-q). The results showed that patients can have a better clinical outcome with a larger resection of the abnormal DTI q areas. Further study of the imaging characteristics of abnormal peritumoural DTI-q areas, using MRS and DCS-MRI, showed a higher Choline/NAA ratio (p = 0.035), especially higher Choline (p = 0.022), in these areas when compared to normal DTI-q areas. This was indicative of tumour activity in the peritumoural abnormal DTI-q areas. The peritumoural progression areas were found to have distinct imaging characteristics. In these progression areas, compared to non-progression areas within a 10 mm border around the contrast enhancing lesion, there was higher signal intensity in FLAIR (p = 0.02), and T1C (p < 0.001), and there were lower intensity in ADC (p = 0.029) and DTI-p (p < 0.001). Further applying radiomics features showed that 35 first order features and 77 second order features were significantly different between progression and non-progression areas. By using supervised convolutional neural network, there was an overall accuracy of 92.4% in the training set (n = 37) and 78.5% in the validation set (n=14). In summary, multimodal MR imaging, particularly diffusion tensor imaging, can demonstrate distinct characteristics in areas of potential progression on preoperative MRI, which can be considered potential targets for treatment. Further application of radiomics and machine learning can be potentially useful when identifying the tumor invasive margin before the surgery.Chung Gung Medical Foundatio

Characterising Heterogeneity of Glioblastoma using Multi-parametric Magnetic Resonance Imaging

A better understanding of tumour heterogeneity is central for accurate diagnosis, targeted therapy and personalised treatment of glioblastoma patients. This thesis aims to investigate whether pre-operative multi-parametric magnetic resonance imaging (MRI) can provide a useful tool for evaluating inter-tumoural and intra-tumoural heterogeneity of glioblastoma. For this purpose, we explored: 1) the utilities of habitat imaging in combining multi-parametric MRI for identifying invasive sub-regions (I & II); 2) the significance of integrating multi-parametric MRI, and extracting modality inter-dependence for patient stratification (III & IV); 3) the value of advanced physiological MRI and radiomics approach in predicting epigenetic phenotypes (V). The following observations were made: I. Using a joint histogram analysis method, habitats with different diffusivity patterns were identified. A non-enhancing sub-region with decreased isotropic diffusion and increased anisotropic diffusion was associated with progression-free survival (PFS, hazard ratio [HR] = 1.08, P < 0.001) and overall survival (OS, HR = 1.36, P < 0.001) in multivariate models. II. Using a thresholding method, two low perfusion compartments were identified, which displayed hypoxic and pro-inflammatory microenvironment. Higher lactate in the low perfusion compartment with restricted diffusion was associated with a worse survival (PFS: HR = 2.995, P = 0.047; OS: HR = 4.974, P = 0.005). III. Using an unsupervised multi-view feature selection and late integration method, two patient subgroups were identified, which demonstrated distinct OS (P = 0.007) and PFS (P < 0.001). Features selected by this approach showed significantly incremental prognostic value for 12-month OS (P = 0.049) and PFS (P = 0.022) than clinical factors. IV. Using a method of unsupervised clustering via copula transform and discrete feature extraction, three patient subgroups were identified. The subtype demonstrating high inter-dependency of diffusion and perfusion displayed higher lactate than the other two subtypes (P = 0.016 and P = 0.044, respectively). Both subtypes of low and high inter-dependency showed worse PFS compared to the intermediate subtype (P = 0.046 and P = 0.009, respectively). V. Using a radiomics approach, advanced physiological images showed better performance than structural images for predicting O6-methylguanine-DNA methyltransferase (MGMT) methylation status. For predicting 12-month PFS, the model of radiomic features and clinical factors outperformed the model of MGMT methylation and clinical factors (P = 0.010). In summary, pre-operative multi-parametric MRI shows potential for the non-invasive evaluation of glioblastoma heterogeneity, which could provide crucial information for patient care.The Cambridge Trust and China Scholarship Council ; Clare College; the British Neuro-Oncology Society; the EG Fearnsides Trust; the International Society for Magnetic Resonance in Medicin

Etude de l'effet radio-sensibilisant de nanotubes de carbone dans le modèle de glioblastome canin spontané, en vue d'un développement chez l'Homme

Les gliomes représentent les tumeurs primaires les plus fréquentes du système nerveux central avec le pronostic le plus mauvais, malgré une prise en charge précoce associée à un traitement agressif et multimodal. De nouvelles thérapies sont donc à l'étude afin d'améliorer la médiane de survie des patients. Parmi ces pistes de recherche, l'optimisation de la radiothérapie (RT) du glioblastome (GBM) est un enjeu majeur. Dans ce contexte, l'utilisation de nanotubes de carbone (NTC) est prometteuse : en plus d'être potentiellement radio-sensibilisants, les NTC contenant du gadolinium (Gd) peuvent aider à mieux délimiter le volume cible tumoral en Imagerie par Résonance Magnétique (IRM). Contrairement au modèle d'étude actuel qu'est la souris avec un certain nombre de limites, le chien est un modèle particulièrement adapté car il peut spontanément développer un GBM d'une taille suffisante pour utiliser le même matériel de RT et d'imagerie qu'en médecine humaine, facilitant ainsi la transposition directe des méthodes. Cette étude vise donc à valider la pertinence de modèles cellulaires de GBM canins en radio-oncologie comparée en caractérisant 5 lignées cellulaires de gliome canin et à évaluer la capacité des NTC à radio-sensibiliser des lignées de GBM canins. L'étude de ces 5 lignées cellulaires canines a montré de nombreuses analogies entre le chien et l'homme. La morphologie des cellules est identique, de même que le temps de doublement, le test de clonalité et le caryotype. L'étude immunohistochimique des protéines de surface, sur les lignées cellulaires et après injection stéréotaxique sur des souris révèle aussi une similarité étroite. Les cellules gliales canines et humaines ont un profil de radiosensibilité similaire. La pénétration des NTC au sein des cellules tumorales a pu être mis en évidence à l'aide de la microscopie bi-photonique. Des études complémentaires sont nécessaires pour démontrer un effet radiosensibilisant des NTC. L'excitation laser des NTC lors de l'observation au microscope bi-photonique a montré un effet photothermique intéressant qui pourrait être approfondi au cours d'études ultérieures. Le modèle canin et les nanotubes de carbone ont fourni des résultats intéressants, qui en font des éléments d'intérêt dans l'étude des gliomes.Despite several aggressive and multimodal treatments, gliomas represent the most frequent brain tumor with the worst prognostic associated. In order to enhance median survival, researchers are looking for optimization of radiotherapy (RT) for glioblastoma (GBM). In this context, the use of carbon nanotubes (CNTs) is promising: in addition to being potentially radio-sensitizing, CNTs containing gadolinium (Gd) can help to better define the tumor target volume in Magnetic Resonance Imaging (MRI). Unlike the current study model of the mouse with its number of limitations, dog is a particularly suitable model because it can spontaneously develop a GBM of sufficient size to use the same RT and imaging equipment as in human medicine, facilitating thus the direct transposition of methods. This study therefore aims to validate the relevance of cell models of canine GBM in comparative radiation oncology by characterizing 5 cell lines of canine glioma and to assess the capacity of CNTs to radio-sensitize canine GBM lines. Study of these 5 canine cell lines shows numerous analogies between dogs and humans. Cell morphology is identical, such as doubling time, clonality test and karyotype. Immunohistochemical study of surface proteins, directly on cell lines and after stereotaxic injection in mice also reveals close similarity. Radiosensitivity profile of glial cells between human and dog is alike. CNT penetration into tumor cells is demonstrated using two-photon microscopy. Therefore, further studies are needed to demonstrate a radiosensitizing effect of CNTs. Laser excitation of CNTs with bi-photon microscope showed an interesting photothermal effect which could be further explored in further studies. The canine model and carbon nanotubes have provided interesting results which are elements of interest in the study of gliomas