ObjectivesTo investigate if quantitative apparent diffusion coefficient (ADC) measurements can predict genetic subtypes of non-gadolinium-enhancing gliomas, comparing whole tumour against single slice analysis.MethodsVolumetric T2-derived masks of 44 gliomas were co-registered to ADC maps with ADC mean (ADCmean) calculated. For the slice analysis, two observers placed regions of interest in the largest tumour cross-section. The ratio (ADCratio) between ADCmean in the tumour and normal appearing white matter was calculated for both methods.ResultsIsocitrate dehydrogenase (IDH) wild-type gliomas showed the lowest ADC values throughout (p < 0.001). ADCmean in the IDH-mutant 1p19q intact group was significantly higher than in the IDH-mutant 1p19q co-deleted group (p < 0.01). A volumetric ADCmean threshold of 1201 × 10−6 mm2/s identified IDH wild-type with a sensitivity of 83% and a specificity of 86%; a volumetric ADCratio cut-off value of 1.65 provided a sensitivity of 80% and a specificity of 92% (area under the curve (AUC) 0.9–0.94). A slice ADCratio threshold for observer 1 (observer 2) of 1.76 (1.83) provided a sensitivity of 80% (86%), specificity of 91% (100%) and AUC of 0.95 (0.96). The intraclass correlation coefficient was excellent (0.98).ConclusionsADC measurements can support the distinction of glioma subtypes. Volumetric and two-dimensional measurements yielded similar results in this study.Key Points • Diffusion-weighted MRI aids the identification of non-gadolinium-enhancing malignant gliomas • ADC measurements may permit non-gadolinium-enhancing glioma molecular subtyping • IDH wild-type gliomas have lower ADC values than IDH-mutant tumours • Single cross-section and volumetric ADC measurements yielded comparable results in this study

With advances in treatments and survival of patients with glioblastoma (GBM), it has become apparent that conventional imaging sequences have significant limitations both in terms of assessing response to treatment and monitoring disease progression. Both 'pseudoprogression' after chemoradiation for newly diagnosed GBM and 'pseudoresponse' after anti-angiogenesis treatment for relapsed GBM are well-recognised radiological entities. This in turn has led to revision of response criteria away from the standard MacDonald criteria, which depend on the two-dimensional measurement of contrast-enhancing tumour, and which have been the primary measure of radiological response for over three decades. A working party of experts published RANO (Response Assessment in Neuro-oncology Working Group) criteria in 2010 which take into account signal change on T2/FLAIR sequences as well as the contrast-enhancing component of the tumour. These have recently been modified for immune therapies, which are associated with specific issues related to the timing of radiological response. There has been increasing interest in quantification and validation of physiological and metabolic parameters in GBM over the last 10 years utilising the wide range of advanced imaging techniques available on standard MRI platforms. Previously, MRI would provide structural information only on the anatomical location of the tumour and the presence or absence of a disrupted blood-brain barrier. Advanced MRI sequences include proton magnetic resonance spectroscopy (MRS), vascular imaging (perfusion/permeability) and diffusion imaging (diffusion weighted imaging/diffusion tensor imaging) and are now routinely available. They provide biologically relevant functional, haemodynamic, cellular, metabolic and cytoarchitectural information and are being evaluated in clinical trials to determine whether they offer superior biomarkers of early treatment response than conventional imaging, when correlated with hard survival endpoints. Multiparametric imaging, incorporating different combinations of these modalities, improves accuracy over single imaging modalities but has not been widely adopted due to the amount of post-processing analysis required, lack of clinical trial data, lack of radiology training and wide variations in threshold values. New techniques including diffusion kurtosis and radiomics will offer a higher level of quantification but will require validation in clinical trial settings. Given all these considerations, it is clear that there is an urgent need to incorporate advanced techniques into clinical trial design to avoid the problems of under or over assessment of treatment response.

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We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status.

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.

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Objective Quantitative MRI (qMRI) methods provide versatile neuroradiological applications and are a hot topic in research. The degree of their clinical implementation is however barely known. This survey was created to illuminate which and how qMRI techniques are currently applied across Europe. Methods In total, 4753 neuroradiologists from 27 countries received an online questionnaire. Demographic and professional data, experience with qMRI techniques in the brain and head and neck, usage, reasons for/against application, and knowledge of the QIBA and EIBALL initiatives were assessed. Results Two hundred seventy-two responders in 23 countries used the following techniques clinically (mean values in %): DWI (82.0%, n = 223), DSC (67.3%, n = 183), MRS (64.3%, n = 175), DCE (43.4%, n = 118), BOLD-fMRI (42.6%, n = 116), ASL (37.5%, n = 102), fat quantification (25.0%, n = 68), T2 mapping (16.9%, n = 46), T1 mapping (15.1%, n = 41), PET-MRI (11.8%, n = 32), IVIM (5.5%, n = 15), APT-CEST (4.8%, n = 13), and DKI (3.3%, n = 9). The most frequent usage indications for any qMRI technique were tissue differentiation (82.4%, n = 224) and oncological monitoring (72.8%, n = 198). Usage differed between countries, e.g. ASL: Germany (n = 13/63; 20.6%) vs. France (n = 31/40; 77.5%). Neuroradiologists endorsed the use of qMRI because of an improved diagnostic accuracy (89.3%, n = 243), but 50.0% (n = 136) are in need of better technology, 34.9% (n = 95) wish for more communication, and 31.3% need help with result interpretation/generation (n = 85). QIBA and EIBALL were not well known (12.5%, n = 34, and 11.0%, n = 30). Conclusions The clinical implementation of qMRI methods is highly variable. Beyond the aspect of readiness for clinical use, better availability of support and a wider dissemination of guidelines could catalyse a broader implementation. Key Points • Neuroradiologists endorse the use of qMRI techniques as they subjectively improve diagnostic accuracy. • Clinical implementation is highly variable between countries, techniques, and indications. • The use of advanced imaging could be promoted through an increase in technical support and training of both doctors and technicians.

Background Validation of the 2016 RANO MRI scorecard for leptomeningeal metastasis failed for multiple reasons. Accordingly, this joint EORTC Brain Tumor Group and RANO effort sought to prospectively validate a revised MRI scorecard for response assessment in leptomeningeal metastasis. Methods Coded paired cerebrospinal MRI of 20 patients with leptomeningeal metastases from solid cancers at baseline and follow-up after treatment and instructions for assessment were provided via the EORTC imaging platform. The Kappa coefficient was used to evaluate the inter-observer pairwise agreement. Results Thirty-five raters participated, including 9 neuroradiologists, 17 neurologists, 4 radiation oncologists, 3 neurosurgeons and 2 medical oncologists. Among single leptomeningeal metastases-related imaging findings at baseline, the best median concordance was noted for hydrocephalus (Kappa=0.63), and the worst median concordance for spinal linear enhancing disease (Kappa=0.46). The median concordance of raters for the overall response assessment was moderate (Kappa=0.44). Notably, the interobserver agreement for the presence of parenchymal brain metastases at baseline was fair (Kappa=0.29) and virtually absent for their response to treatment. 394 of 700 ratings (20 patients x 35 raters, 56%) were fully completed. In 308 of 394 fully completed ratings (78%), the overall response assessment perfectly matched the summary interpretation of the single ratings as proposed in the scorecard instructions. Conclusion This study confirms the principle utility of the new scorecard, but also indicates the need for training of MRI assessment with a dedicated reviewer panel in clinical trials. Electronic case report forms with “blocking options” may be required to enforce completeness and quality of scoring.