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The phase-3 INDIGO trial demonstrated that the isocitrate dehydrogenase ( IDH ) inhibitor vorasidenib significantly prolonged progression-free survival and delayed intervention in patients with CNS WHO grade 2 gliomas. However, conventional MRI showed limited response, with only 11% of patients having objective responses. Studies suggest that serial PET imaging with radiolabeled amino acids, such as O -(2-[ 18 F]-fluoroethyl)-L-tyrosine (FET) PET, may provide earlier and more informative assessments of treatment response than MRI. In an initial experience with FET PET, 3 out of 5 patients showed metabolic response to vorasidenib. This highlights FET PET's potential to guide decision-making, though further trials are needed to confirm outcome benefits., Competing Interests: N.G. received honoraria for lectures from Blue Earth Diagnostics, for advisory board participation from Telix Pharmaceuticals and Servier, and for consultancy services from Telix Pharmaceuticals. H.C.P. received travel support from Eli Lilly, MSD, Novartis, Pfizer, and Roche, and received lecture honoraria from Eli Lilly. N.L.A. has received honoraria for lectures, consultation or advisory board participation from Novartis, Advanced Accelerator Applications, Telix Pharmaceuticals, OncLive, MEDSIR, and Servier, and research funding from Novocure and Telix Pharmaceuticals. K.-J.L. received honoraria for consultancy services from Telix Pharmaceuticals. P.L. received honoraria for lectures from Blue Earth Diagnostics, and for advisory board participation from Servier. M.P. received honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dome, Tocagen, Adastra, Gan & Lee Pharmaceuticals, Janssen, Servier, Miltenyi, Böhringer Ingelheim, Telix, Medscape, and OncLive. All other authors reported no potential conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Background: In glioma patients, tumor growth and subsequent treatments are associated with various types of brain lesions. We hypothesized that cognitive functioning in these patients critically depends on the maintained structural connectivity of multiple brain networks., Methods: The study included 121 glioma patients (median age, 52 years; median Eastern Cooperative Oncology Group performance score 1; CNS-WHO Grade 3 or 4) after multimodal therapy. Cognitive performance was assessed by 10 tests in 5 cognitive domains at a median of 14 months after treatment initiation. Hybrid amino acid PET/MRI using the tracer O-(2-[ 18 F]fluoroethyl)-L-tyrosine, a network-based cortical parcellation, and advanced tractography were used to generate whole-brain fiber count-weighted connectivity matrices. The matrices were applied to a cross-validated machine-learning model to identify predictive fiber connections (edges), critical cortical regions (nodes), and the networks underlying cognitive performance., Results: Compared to healthy controls ( n  = 121), patients' cognitive scores were significantly lower in 9 cognitive tests. The models predicted the scores of 7/10 tests (median correlation coefficient, 0.47; range, 0.39-0.57) from 0.6% to 5.4% of the matrix entries; 84% of the predictive edges were between nodes of different networks. Critically involved cortical regions (≥10 adjacent edges) included predominantly left-sided nodes of the visual, somatomotor, dorsal/ventral attention, and default mode networks. Highly critical nodes (≥15 edges) included the default mode network's left temporal and bilateral posterior cingulate cortex., Conclusions: These results suggest that the cognitive performance of pretreated glioma patients is strongly related to structural connectivity between multiple brain networks and depends on the integrity of known network hubs also involved in other neurological disorders., Competing Interests: N.G. has received honoraria from Telix Pharmaceuticals and Blue Earth Diagnostics. The other authors report no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

In patients with meningioma, diagnosis and treatment planning are predominantly based on anatomical imaging using MRI or CT. Constraints of these imaging modalities include precise meningioma delineation-especially at the skull base, in the case of trans -osseus growth, and in tumors with complex geometry-and the differentiation of post-therapeutic reactive changes from meningioma relapse. Advanced metabolic imaging using PET may help to characterize specific metabolic and cellular features providing additional information beyond the information derived from anatomical imaging alone. Accordingly, the use of PET in meningioma patients is steadily increasing. This review summarizes recent advances in PET imaging helpful for improving the clinical management of patients with meningioma., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Background: Radiological differentiation of tumor progression (TPR) from treatment-related changes (TRC) in pretreated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O -(2-[ 18 F]-fluoroethyl)-l-tyrosine ( 18 F-FET) PET for the differentiation of TPR from TRC in patients with pretreated glioblastoma., Methods: Thirty-two patients with histomolecularly defined and pretreated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. Three-dimensional (3D) regions of interest were generated based on increased 18 F-FET uptake using a tumor-to-brain ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions of interest using co-registered 18 F-FET PET images, and advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions of interest. Diagnostic accuracy was analyzed by receiver operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression., Results: Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumor-to-brain ratio of 18 F-FET uptake and diffusion MRI kurtosis metrics., Conclusions: The combined use of 18 F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pretreated glioblastoma and warrants further investigation., (© The Author(s) 2021. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Radiomics allows the extraction of quantitative features from medical images such as CT, MRI, or PET, thereby providing additional, potentially relevant diagnostic information for clinical decision-making. Because the computation of these features is performed highly automated on medical images acquired during routine follow-up, radiomics offers this information at low cost. Further, the radiomics features can be used alone or combined with other clinical or histomolecular parameters to generate predictive or prognostic mathematical models. These models can then be applied for various important diagnostic indications in neuro-oncology, for example, to noninvasively predict relevant biomarkers in glioma patients, to differentiate between treatment-related changes and local brain tumor relapse, or to predict treatment response. In recent years, amino acid PET has become an important diagnostic tool in patients with brain tumors. Therefore, the number of studies in patients with brain tumors investigating the potential of PET radiomics or combined PET/MRI radiomics is steadily increasing. This review summarizes current research regarding feature-based PET as well as combined PET/MRI radiomics in neuro-oncology., (© The Author(s) 2021. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Over the past decades, a variety of PET tracers have been used for the evaluation of patients with brain tumors. For clinical routine, the most important clinical indications for PET imaging in patients with brain tumors are the identification of neoplastic tissue including the delineation of tumor extent for the further diagnostic and therapeutic management (ie, biopsy, resection, or radiotherapy planning), the assessment of response to a certain anticancer therapy including its (predictive) effect on the patients' outcome and the differentiation of treatment-related changes (eg, pseudoprogression and radiation necrosis) from tumor progression at follow-up. To serve medical professionals of all disciplines involved in the diagnosis and care of patients with brain tumors, this review summarizes the value of PET imaging for the latter-mentioned 3 clinically relevant indications in patients with glioma, meningioma, and brain metastases., (© The Author(s) 2019. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)