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.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Arterial spin labeling (ASL) is a non-invasive MRI technique to measure cerebral blood flow (CBF). This review provides a practical guide and overview of the clinical applications of ASL of the brain, as well its potential pitfalls. The technical and physiological background is also addressed. At present, main areas of interest are cerebrovascular disease, dementia and neuro-oncology. In cerebrovascular disease, ASL is of particular interest owing to its quantitative nature and its capability to determine cerebral arterial territories. In acute stroke, the source of the collateral blood supply in the penumbra may be visualised. In chronic cerebrovascular disease, the extent and severity of compromised cerebral perfusion can be visualised, which may be used to guide therapeutic or preventative intervention. ASL has potential for the detection and follow-up of arteriovenous malformations. In the workup of dementia patients, ASL is proposed as a diagnostic alternative to PET. It can easily be added to the routinely performed structural MRI examination. In patients with established Alzheimer’s disease and frontotemporal dementia, hypoperfusion patterns are seen that are similar to hypometabolism patterns seen with PET. Studies on ASL in brain tumour imaging indicate a high correlation between areas of increased CBF as measured with ASL and increased cerebral blood volume as measured with dynamic susceptibility contrast-enhanced perfusion imaging. Major advantages of ASL for brain tumour imaging are the fact that CBF measurements are not influenced by breakdown of the blood–brain barrier, as well as its quantitative nature, facilitating multicentre and longitudinal studies.

This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high‐grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day‐to‐day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9–30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow‐up. The combination of MRS and diffusion‐weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer‐aided diagnostic technology, and meticulous evaluation with clinical and pathological data.Level of Evidence: 3Technical Efficacy: Stage 2J. Magn. Reson. Imaging 2018;48:571–589.

Objectives At a European Society of Neuroradiology (ESNR) Annual Meeting 2015 workshop, commonalities in practice, current controversies and technical hurdles in glioma MRI were discussed. We aimed to formulate guidance on MRI of glioma and determine its feasibility, by seeking information on glioma imaging practices from the European Neuroradiology community. Methods Invitations to a structured survey were emailed to ESNR members (n=1,662) and associates (n=6,400), European national radiologists’ societies and distributed via social media. Results Responses were received from 220 institutions (59% academic). Conventional imaging protocols generally include T2w, T2-FLAIR, DWI, and pre- and post-contrast T1w. Perfusion MRI is used widely (85.5%), while spectroscopy seems reserved for specific indications. Reasons for omitting advanced imaging modalities include lack of facility/software, time constraints and no requests. Early postoperative MRI is routinely carried out by 74% within 24–72 h, but only 17% report a percent measure of resection. For follow-up, most sites (60%) issue qualitative reports, while 27% report an assessment according to the RANO criteria. A minority of sites use a reporting template (23%). Conclusion Clinical best practice recommendations for glioma imaging assessment are proposed and the current role of advanced MRI modalities in routine use is addressed. Key Points • We recommend the EORTC-NBTS protocol as the clinical standard glioma protocol. • Perfusion MRI is recommended for diagnosis and follow-up of glioma. • Use of advanced imaging could be promoted with increased education activities. • Most response assessment is currently performed qualitatively. • Reporting templates are not widely used, and could facilitate standardisation.