Enhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [18F]FET PET, and TSPO PET.

PURPOSE: According to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively. METHODS: Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied. RESULTS: TBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97). CONCLUSION: The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.

Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping.

BACKGROUND AND PURPOSE: Tractography of the corticospinal tract is paramount to presurgical planning and guidance of intraoperative resection in patients with motor-eloquent gliomas. It is well-known that DTI-based tractography as the most frequently used technique has relevant shortcomings, particularly for resolving complex fiber architecture. The purpose of this study was to evaluate multilevel fiber tractography combined with functional motor cortex mapping in comparison with conventional deterministic tractography algorithms. MATERIALS AND METHODS: Thirty-one patients (mean age, 61.5 [SD, 12.2] years) with motor-eloquent high-grade gliomas underwent MR imaging with DWI (TR/TE = 5000/78 ms, voxel size = 2 × 2 × 2 mm3, 1 volume at b = 0 s/mm2, 32 volumes at b = 1000 s/mm2). DTI, constrained spherical deconvolution, and multilevel fiber tractography-based reconstruction of the corticospinal tract within the tumor-affected hemispheres were performed. The functional motor cortex was enclosed by navigated transcranial magnetic stimulation motor mapping before tumor resection and used for seeding. A range of angular deviation and fractional anisotropy thresholds (for DTI) was tested. RESULTS: For all investigated thresholds, multilevel fiber tractography achieved the highest mean coverage of the motor maps (eg, angular threshold = 60°; multilevel/constrained spherical deconvolution/DTI, 25% anisotropy threshold = 71.8%, 22.6%, and 11.7%) and the most extensive corticospinal tract reconstructions (eg, angular threshold = 60°; multilevel/constrained spherical deconvolution/DTI, 25% anisotropy threshold = 26,485 mm3, 6308 mm3, and 4270 mm3). CONCLUSIONS: Multilevel fiber tractography may improve the coverage of the motor cortex by corticospinal tract fibers compared with conventional deterministic algorithms. Thus, it could provide a more detailed and complete visualization of corticospinal tract architecture, particularly by visualizing fiber trajectories with acute angles that might be of high relevance in patients with gliomas and distorted anatomy.

Methylation subgroup and molecular heterogeneity is a hallmark of glioblastoma: implications for biopsy targeting, classification and therapy.

BACKGROUND: Intratumoral heterogeneity at the cellular and molecular level is a hallmark of glioblastoma (GB) that contributes to treatment resistance and poor clinical outcome. Little is known regarding epigenetic heterogeneity and intratumoral phylogeny and their implication for molecular classification and targeted therapies. PATIENTS AND METHODS: Multiple tissue biopsies (238 in total) were sampled from 56 newly-diagnosed, treatment-naive GB patients from a prospective in-house cohort and publicly available data and profiled for DNA methylation using the Illumina MethylationEPIC array. Methylation-based classification using the glioma classifier developed by Ceccarelli et al. and estimation of the MGMT promoter methylation status via the MGMT-STP27 model were carried out. In addition, copy number variations (CNVs) and phylogeny were analyzed. RESULTS: Almost half of the patients (22/56, 39%) harbored tumors composed of heterogeneous methylation subtypes. We found two predominant subtype combinations: classic-/mesenchymal-like, and mesenchymal-/pilocytic astrocytoma-like. Nine patients (16%) had tumors composed of subvolumes with and without MGMT promoter methylation, whereas 20 patients (36%) were homogeneously methylated, and 27 patients (48%) were homogeneously unmethylated. CNV analysis revealed high variations in many genes, including CDKN2A/B, EGFR, and PTEN. Phylogenetic analysis correspondingly showed a general pattern of CDKN2A/B loss and gain of EGFR, PDGFRA, and CDK4 during early stages of tumor development. CONCLUSIONS: (Epi)genetic intratumoral heterogeneity is a hallmark of GB, both at DNA methylation and CNV level. This intratumoral heterogeneity is of utmost importance for molecular classification as well as for defining therapeutic targets in this disease, as single biopsies might underestimate the true molecular diversity in a tumor.

Differential Effects of Fingolimod and Natalizumab on Magnetic Resonance Imaging Measures in Relapsing-Remitting Multiple Sclerosis.

Fingolimod and natalizumab are approved disease-modifying drugs in relapsing-remitting multiple sclerosis (RRMS). The two drugs have different modes of action and may therefore influence different aspects of MS-related tissue damage. In this retrospective cohort study, we longitudinally compared patients treated with fingolimod and patients treated with natalizumab by measures based on structural magnetic resonance imaging (MRI). We included patients with RRMS given that two standardized MRI scans under the same drug were available with an interval of at least 6 months both from therapy start to baseline scan and from baseline scan to follow-up scan. After matching for age, baseline and follow-up scans from 93 patients (fingolimod, 48; natalizumab, 45) were investigated. Mean follow-up time was 1.9 years. We determined the number of new white matter lesions as well as thalamic, cortical, and whole-brain atrophy. After scaling for time of the interscan interval, measures were analyzed by group comparisons and, to account for demographic and clinical characteristics, by multiple regression models and a binary logistic regression model. Compared to natalizumab, fingolimod treatment went along with more new white matter lesions (median [interquartile range, IQR] 0.0 [0.0; 0.7] vs. 0.0 [0.0; 0.0] /year; p < 0.01) whereas whole-brain atrophy was lower (median [IQR] 0.2 [0.0; 0.5] vs. 0.5 [0.2; 1.0] %/year; p = 0.01). These significant differences were confirmed by multiple regression models and the binary logistic regression model. In conclusion, our observation is compatible with stronger neuroprotective properties of fingolimod compared to natalizumab.

Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression.

PURPOSE: To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. MATERIAL AND METHODS: At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier's performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. RESULTS: In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77-0.93 (sensitivity 0.91, 95% CI 0.81-0.97; specificity 0.71, 95% CI 0.44-0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72-0.9), 0.81 for [18F]-FET-PET (95% CI 0.7-0.89), and 0.81 for expert consensus (95% CI 0.7-0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. CONCLUSION: Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

Imaging glioma biology: spatial comparison of amino acid PET, amide proton transfer, and perfusion-weighted MRI in newly diagnosed gliomas.

PURPOSE: Imaging glioma biology holds great promise to unravel the complex nature of these tumors. Besides well-established imaging techniques such O-(2-[18F]fluoroethyl)-L-tyrosine (FET)-PET and dynamic susceptibility contrast (DSC) perfusion imaging, amide proton transfer-weighted (APTw) imaging has emerged as a promising novel MR technique. In this study, we aimed to better understand the relation between these imaging biomarkers and how well they capture cellularity and vascularity in newly diagnosed gliomas. METHODS: Preoperative MRI and FET-PET data of 46 patients (31 glioblastoma and 15 lower-grade glioma) were segmented into contrast-enhancing and FLAIR-hyperintense areas. Using established cutoffs, we calculated hot-spot volumes (HSV) and their spatial overlap. We further investigated APTw and CBV values in FET-HSV. In a subset of 10 glioblastoma patients, we compared cellularity and vascularization in 34 stereotactically targeted biopsies with imaging. RESULTS: In glioblastomas, the largest HSV was found for APTw, followed by PET and CBV (p < 0.05). In lower-grade gliomas, APTw-HSV was clearly lower than in glioblastomas. The spatial overlap of HSV was highest between APTw and FET in both tumor entities and regions. APTw correlated significantly with cellularity, similar to FET, while the association with vascularity was more pronounced in CBV and FET. CONCLUSIONS: We found a relevant spatial overlap in glioblastomas between hotspots of APTw and FET both in contrast-enhancing and FLAIR-hyperintense tumor. As suggested by earlier studies, APTw was lower in lower-grade gliomas compared with glioblastomas. APTw meaningfully contributes to biological imaging of gliomas.

Discrimination of Different Brain Metastases and Primary CNS Lymphomas Using Morphologic Criteria and Diffusion Tensor Imaging.

Purpose: Brain metastases are a common complication of cancer and occur in about 15 - 40 % of patients with malignancies. The aim of this retrospective study was to differentiate between metastases from different primary tumors/CNS lymphyomas using morphologic criteria, fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Materials and Methods: Morphologic criteria such as hemorrhage, cysts, pattern of contrast enhancement and location were reported in 200 consecutive patients with brain metastases/primary CNS lymphomas. FA and ADC values were measured in regions of interest (ROIs) placed in the contrast-enhancing tumor part, the necrosis and the non-enhancing peritumoral region (NEPTR). Differences between histopathological subtypes of metastases were analyzed using non-parametric tests, decision trees and hierarchical clustering analysis. Results: Significant differences were found in morphologic criteria such as hemorrhage or pattern of contrast enhancement. In diffusion measurements, significant differences between the different tumor entities were only found in ADC analyzed in the contrast-enhancing tumor part. Among single tumor entities, primary CNS lymphomas showed significantly lower median ADC values in the contrast-enhancing tumor part (ADClymphoma 0.92 [0.83 - 1.07] vs. ADCno_lymphoma 1.35 [1.10 - 1.64] P = 0.001). Further differentiation between types of metastases was not possible using FA and ADC. Conclusion: There were morphologic differences among the main subtypes of brain metastases/CNS lymphomas. However, due to a high variability of common types of metastases and low specificity, prospective differentiation remained challenging. DTI including FA and ADC was not a reliable tool for differentiation between different histopathological subtypes of brain metastases except for CNS lymphomas showing lower ADC values. Biopsy, surgery and staging remain essential for diagnosis. Key Points: • Histopathological subtypes of brain metastases/CNS lymphomas show different morphologic features on MRI• Primary CNS lymphomas show significantly reduced ADC values• DTI is not a reliable tool for differentiation between brain metastases Citation Format: • Bette S, Wiestler B, Delbridge C et al. Discrimination of Different Brain Metastases and Primary CNS Lymphomas Using Morphologic Criteria and Diffusion Tensor Imaging. Fortschr Röntgenstr 2016; 188: 1134 - 1143.

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

BACKGROUND AND PURPOSE: Dynamic contrast-enhanced MR imaging can provide in vivo assessment of the microvasculature in intracranial tumors. The aim of the present study was to evaluate the diagnostic performance of dynamic contrast-enhanced MR imaging derived vascular permeability parameters, including the volume transfer constant, the volume of extravascular extracellular space, and the flux rate constant between the extravascular extracellular space and plasma, for the differentiation of primary CNS lymphoma and glioblastoma. MATERIALS AND METHODS: Sixty glioblastomas and 11 primary central nervous system lymphomas were included. Pretreatment T1-weighted dynamic contrast-enhanced MR imaging with a 3D T1-weighted spoiled gradient-echo sequence was performed on a 3T MR imaging scanner. Perfusion parameters (volume transfer constant, the volume of extravascular extracellular space, and the flux rate constant) were measured on the basis of the Tofts-Kernmode model. The Mann-Whitney U test and receiver operating characteristic analysis were used to compare those parameters between primary central nervous system lymphoma and glioblastoma. Histopathologic correlation of dynamic contrast-enhanced MR imaging findings was performed by using reticulin staining and CD31 immunohistochemistry. RESULTS: Median volume transfer constant and flux rate constant values were significantly higher in primary central nervous system lymphoma (0.145 ± 0.057 and 0.396 ± 0.088) than in glioblastoma (0.064 ± 0.021 and 0.230 ± 0.058) (P < .001, respectively). Median volume of extravascular extracellular space values did not differ significantly between primary central nervous system lymphoma (0.434 ± 0.165) and glioblastoma (0.319 ± 0.107). On receiver operating characteristic analysis, volume transfer constant had the best discriminative value for differentiating primary central nervous system lymphoma and glioblastoma (threshold, 0.093; sensitivity, 90.9%; specificity, 95.0%). Histopathologic evaluation revealed intact vascular integrity in glioblastoma despite endothelial proliferation, whereas primary central nervous system lymphoma demonstrated destroyed vessel architecture, thereby promoting vascular disintegrity. CONCLUSIONS: Primary central nervous system lymphoma demonstrated significantly higher volume transfer constant and flux rate constant values compared with glioblastoma, implying a higher vascular permeability in primary central nervous system lymphoma. These findings confirm initial observations from perfusion CT and dynamic contrast-enhanced MR imaging studies, correlating with underlying histopathologic features, and may be useful in distinguishing primary central nervous system lymphoma from glioblastoma.

Neuroradiological response criteria for high-grade gliomas.

The recently introduced new response criteria of the response assessment in neuro-oncology (RANO) working group and its clinical implications are the topic of this article. Establishing this working group as a work-in-progress platform and its first report, the RANO criteria represent an important step forward in the accurate assessment of response to therapy in patients with malignant gliomas not only in clinical trials but also in daily practice. Anti-angiogenic therapy and other new treatment modalities have increased the incidence and awareness of novel imaging phenomena, such as pseudoprogression and pseudoresponse not only within clinical trials. The new RANO criteria also take clinical parameters, such as steroid medication and neurological symptoms into account. Neuroradiologists and neuro-oncologists need to be aware of and experienced in applying these new criteria to correctly assess the response to treatment in patients with malignant gliomas. Further research is needed to study new imaging techniques, such as perfusion and diffusion-weighted imaging and to investigate and incorporate these for routine tumor response criteria.