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In response to major changes in diagnostic algorithms and the publication of mature results from various large clinical trials, the European Association of Neuro-Oncology (EANO) recognized the need to provide updated guidelines for the diagnosis and management of adult patients with diffuse gliomas. Through these evidence-based guidelines, a task force of EANO provides recommendations for the diagnosis, treatment and follow-up of adult patients with diffuse gliomas. The diagnostic component is based on the 2016 update of the WHO Classification of Tumors of the Central Nervous System and the subsequent recommendations of the Consortium to Inform Molecular and Practical Approaches to CNS Tumour Taxonomy — Not Officially WHO (cIMPACT-NOW). With regard to therapy, we formulated recommendations based on the results from the latest practice-changing clinical trials and also provide guidance for neuropathological and neuroradiological assessment. In these guidelines, we define the role of the major treatment modalities of surgery, radiotherapy and systemic pharmacotherapy, covering current advances and cognizant that unnecessary interventions and expenses should be avoided. This document is intended to be a source of reference for professionals involved in the management of adult patients with diffuse gliomas, for patients and caregivers, and for health-care providers.Herein, the European Association of Neuro-Oncology (EANO) provides recommendations for the diagnosis, treatment and follow-up of adult patients with diffuse gliomas. These evidence-based guidelines incorporate major changes in diagnostic algorithms based on the 2016 update of the WHO Classification of Tumors of the Central Nervous System as well as on evidence from recent large clinical trials.

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as 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 imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.MRI is an important tool in neuro-oncology but is still predominantly used in a qualitative manner. In this Review, Marion Smits discusses the development of MRI biomarkers for use in neuro-oncology and highlights the clinical potential of quantitative image analysis techniques.

BackgroundMolecular characterization plays a crucial role in glioma classification which impacts treatment strategy and patient outcome. Dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) perfusion imaging have been suggested as methods to help characterize glioma in a non-invasive fashion. This study set out to review and meta-analyze the evidence on the accuracy of DSC and/or DCE perfusion MRI in predicting IDH genotype and 1p/19q integrity status.MethodsAfter systematic literature search on Medline, EMBASE, Web of Science and the Cochrane Library, a qualitative meta-synthesis and quantitative meta-analysis were conducted. Meta-analysis was carried out on aggregated AUC data for different perfusion metrics.ResultsOf 680 papers, twelve were included for the qualitative meta-synthesis, totaling 1384 patients. It was observed that CBV, ktrans, Ve and Vp values were, in general, significantly higher in IDH wildtype compared to IDH mutated glioma. Meta-analysis comprising of five papers (totaling 316 patients) showed that the AUC of CBV, ktrans, Ve and Vp were 0.85 (95%-CI 0.75–0.93), 0.81 (95%-CI 0.74–0.89), 0.84 (95%-CI 0.71–0.97) and 0.76 (95%-CI 0.61–0.90), respectively. No conclusive data on the prediction of 1p/19q integrity was available from these studies.ConclusionsFuture research should aim to predict 1p/19q integrity based on perfusion MRI data. Additionally, correlations with other clinically relevant outcomes should be further investigated, including patient stratification for treatment and overall survival.

Bevacizumab is frequently used in the treatment of recurrent WHO grade II and III glioma, but without supporting evidence from randomised trials. Therefore, we assessed the use of bevacizumab in patients with first recurrence of grade II or III glioma who did not have 1p/19q co-deletion. The TAVAREC trial was a randomised, open-label phase 2 trial done at 32 centres across Europe in patients with locally diagnosed grade II or III glioma without 1p/19q co-deletion, with a first and contrast-enhancing recurrence after initial radiotherapy or chemotherapy, or both. Previous chemotherapy must have been stopped at least 6 months before enrolment and radiotherapy must have been stopped at least 3 months before enrolment. Random group assignment was done electronically through the European Organisation for Research and Treatment of Cancer web-based system, stratified by a minimisation procedure using institution, initial histology (WHO grade II vs III), WHO performance status (0 or 1 vs 2), and previous treatment (radiotherapy, chemotherapy, or both). Patients were assigned to receive either temozolomide (150–200 mg/m2, orally) monotherapy on days 1–5 every 4 weeks for a maximum of 12 cycles, or the same temozolomide regimen in combination with bevacizumab (10 mg/kg, intravenously) every 2 weeks until progression. The primary endpoint was overall survival at 12 months in the per-protocol population. Safety analyses were done in all patients who started their allocated treatment. The study is registered at EudraCT (2009–017422–39) and ClinicalTrials.gov (NCT01164189), and is complete. Between Feb 8, 2011, and July 31, 2015, 155 patients were enrolled and randomly assigned to receive either monotherapy (n=77) or combination therapy (n=78). Overall survival in the per-protocol population at 12 months was achieved by 44 (61% [80% CI 53–69]) of 72 patients in the temozolomide group and 38 (55% [47–69]) of 69 in the combination group. The most frequent toxicity was haematological: 17 (23%) of 75 patients in the monotherapy group and 25 (33%) of 76 in the combination group developed grade 3 or 4 haematological toxicity. Other than haematological toxicities, the most common adverse events were nervous system disorders (59 [79%] of 75 patients in the monotherapy group vs 65 [86%] of 76 in the combination group), fatigue (53 [70%] vs 61 [80%]), and nausea (39 [52%] vs 43 [56%]). Infections were more frequently reported in the combination group (29 [38%] of 76 patients) than in the monotherapy group (17 [23%] of 75). One treatment-related death was reported in the combination group (infection after intratumoral haemorrhage during a treatment-related grade 4 thrombocytopenia). We found no evidence of improved overall survival with bevacizumab and temozolomide combination treatment versus temozolomide monotherapy. The findings from this study provide no support for further phase 3 studies on the role of bevacizumab in this disease. Roche Pharmaceuticals.

Theranostics integrate molecular imaging and targeted radionuclide therapy for personalised cancer therapy. Theranostic treatments have shown meaningful efficacy in randomised clinical trials and are approved for clinical use in prostate cancer and neuroendocrine tumours. Brain tumours represent an unmet clinical need and theranostics might offer effective treatment options, although specific issues need to be considered for clinical development. In this Policy Review, we discuss opportunities and challenges of developing targeted radionuclide therapies for the treatment of brain tumours including glioma, meningioma, and brain metastasis. The rational choice of molecular treatment targets is highlighted, including the potential relevance of different types of targeted radionuclide therapeutics, and the role of the blood–brain barrier and blood–tumour barrier. Furthermore, we discuss considerations for effective clinical trial design and conduct, as well as logistical and regulatory challenges for implementation of radionuclide therapies into neuro-oncological practice. Rational development will foster successful translation of the theranostic concept to brain tumours.

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N ​= ​9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen’s kappa, κ=0.72−0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: ε=1%−5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N ​= ​58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice. •We present a direct CNN-based method for white matter tract segmentation from DTI.•The method accurately segmented 25 tracts from a large population dataset.•It showed higher reproducibility than compared tractography-based methodology.•The method generalized well to clinical-quality, dementia, and cross-scanner data.•It enables fast, easy, and reliable analysis of brain microstructure in aging and disease.

This Policy Review provides recommendations for the use of PET imaging in patients with gliomas and represents a joint effort of the Response Assessment in Neuro-Oncology (RANO) working group for PET and the European Association for Neuro-Oncology. The initial guideline was published in 2016, and summarised the previously established clinical benefit of PET with radiolabelled glucose and amino acid tracers in patients with gliomas. Since then, numerous additional studies have been published on this topic, focusing on differential diagnosis, prediction of molecular information, and prognostication. Further studies evaluated PET for biopsy guidance and delineation of glioma extent for local therapy planning, including resection and radiotherapy. In patients undergoing treatment, PET was studied for the assessment of response to local and systemic treatments and PET-based standardised response criteria (PET RANO 1.0) were proposed. In this Policy Review, the updated recommendations are based on evidence generated from studies that validated PET findings by histomolecular findings or clinical course. This guideline further underscores the previously reported clinical value of PET imaging and the superiority of amino acid PET over glucose PET, providing a framework for the use of PET in the management of patients with gliomas. The guideline also underscores the scarcity of class 1 evidence showing that incorporating PET imaging into clinical workflows improves patient outcomes, highlighting priority areas for future clinical studies designed to address this gap.

Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed.

Oncological neurosurgery relies heavily on making continuous, intra-operative tumor-brain delineations based on image-guidance. Limitations of currently available imaging techniques call for the development of real-time image-guided resection tools, which allow for reliable functional and anatomical information in an intra-operative setting. Functional ultrasound (fUS), is a new mobile neuro-imaging tool with unprecedented spatiotemporal resolution, which allows for the detection of small changes in blood dynamics that reflect changes in metabolic activity of activated neurons through neurovascular coupling. We have applied fUS during conventional awake brain surgery to determine its clinical potential for both intra-operative functional and vascular brain mapping, with the ultimate aim of achieving maximum safe tumor resection. During awake brain surgery, fUS was used to image tumor vasculature and task-evoked brain activation with electrocortical stimulation mapping (ESM) as a gold standard. For functional imaging, patients were presented with motor, language or visual tasks, while the probe was placed over (ESM-defined) functional brain areas. For tumor vascular imaging, tumor tissue (pre-resection) and tumor resection cavity (post-resection) were imaged by moving the hand-held probe along a continuous trajectory over the regions of interest. A total of 10 patients were included, with predominantly intra-parenchymal frontal and temporal lobe tumors of both low and higher histopathological grades. fUS was able to detect (ESM-defined) functional areas deep inside the brain for a range of functional tasks including language processing. Brain tissue could be imaged at a spatial and temporal resolution of 300 μm and 1.5-2.0 ms respectively, revealing real-time tumor-specific, and healthy vascular characteristics. The current study presents the potential of applying fUS during awake brain surgery. We illustrate the relevance of fUS for awake brain surgery based on its ability to capture both task-evoked functional cortical responses as well as differences in vascular characteristics between tumor and healthy tissue. As current neurosurgical practice is still pre-dominantly leaning on inherently limited pre-operative imaging techniques for tumor resection-guidance, fUS enters the scene as a promising alternative that is both anatomically and physiologically informative.

Objectives To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls. Methods This retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions. Results Combining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p  = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information. Conclusions ASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level. Key points • Multiparametric MRI is promising for computer-aided diagnosis of early-onset AD and FTD. • Diagnosis is driven by different brain regions when using different MRI methods. • Combining structural MRI, ASL, and DTI may improve differential diagnosis of dementia.