The most common type of primary brain tumor is malignant glioma. Despite intensive therapeutic efforts, the majority of these neoplasms remain incurable. Imaging techniques are important for initial tumor detection and comprise indispensable tools for monitoring treatment. Structural imaging using contrast-enhanced MRI is the method of choice for brain tumor surveillance, but its capacity to differentiate tumor from nonspecific tissue changes can be limited, particularly with posttreatment gliomas. Metabolic imaging using positron-emission-tomography (PET) can provide relevant additional information, which may allow for better assessment of tumor burden in ambiguous cases. Specific PET tracers have addressed numerous molecular targets in the last decades, but only a few have achieved relevance in routine clinical practice. At present, PET studies using radiolabeled amino acids appear to improve clinical decision-making as these tracers can offer better delineation of tumor extent as well as improved targeting of biopsies, surgical interventions, and radiation therapy. Amino acid PET imaging also appears useful for distinguishing glioma recurrence or progression from postradiation treatment effects, particularly radiation necrosis and pseudoprogression, and provides information on histological grading and patient prognosis. In the last decade, the tracers O-(2-[(18)F]fluoroethyl)-L-tyrosine (FET) and 3,4-dihydroxy-6-[(18)F]-fluoro-L-phenylalanine (FDOPA) have been increasingly used for these indications. This review article focuses on these tracers and summarizes their recent applications for patients with brain tumors. Current uses of tracers other than FET and FDOPA are also discussed, and the most frequent practical questions regarding PET brain tumor imaging are reviewed.

Routine diagnostics and treatment monitoring of brain tumors is usually based on contrast-enhanced MRI. However, the capacity of conventional MRI to differentiate tumor tissue from posttherapeutic effects following neurosurgical resection, chemoradiation, alkylating chemotherapy, radiosurgery, and/or immunotherapy may be limited. Metabolic imaging using PET can provide relevant additional information on tumor metabolism, which allows for more accurate diagnostics especially in clinically equivocal situations. This review article focuses predominantly on the amino acid PET tracers ¹¹C-methyl-l-methionine (MET), O-(2-[¹⁸F]fluoroethyl)-l-tyrosine (FET) and 3,4-dihydroxy-6-[¹⁸F]-fluoro-l-phenylalanine (FDOPA) and summarizes investigations regarding monitoring of brain tumor therapy.

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.

Many studies in patients with brain tumors evaluating innovative PET tracers have been published in recent years, and the initial results are promising. Here, the Response Assessment in Neuro-Oncology (RANO) PET working group provides an overview of the literature on novel investigational PET tracers for brain tumor patients. Furthermore, newer indications of more established PET tracers for the evaluation of glucose metabolism, amino acid transport, hypoxia, cell proliferation, and others are also discussed. Based on the preliminary findings, these novel investigational PET tracers should be further evaluated considering their promising potential. In particular, novel PET probes for imaging of translocator protein and somatostatin receptor overexpression as well as for immune system reactions appear to be of additional clinical value for tumor delineation and therapy monitoring. Progress in developing these radiotracers may contribute to improving brain tumor diagnostics and advancing clinical translational research.

Brain metastases (BM) from extracranial cancer are associated with significant morbidity and mortality. Effective local treatment options are stereotactic radiotherapy, including radiosurgery or fractionated external beam radiotherapy, and surgical resection. The use of systemic treatment for intracranial disease control also is improving. BM diagnosis, treatment planning, and follow-up is most often based on contrast-enhanced magnetic resonance imaging (MRI). However, anatomic imaging modalities including standard MRI have limitations in accurately characterizing posttherapeutic reactive changes and treatment response. Molecular imaging techniques such as positron emission tomography (PET) characterize specific metabolic and cellular features of metastases, potentially providing clinically relevant information supplementing anatomic MRI. Here, the Response Assessment in Neuro-Oncology working group provides recommendations for the use of PET imaging in the clinical management of patients with BM based on evidence from studies validated by histology and/or clinical outcome.

The advent of immunotherapy using immune checkpoint inhibitors (ICIs) and targeted therapy (TT) has dramatically improved the prognosis of various cancer types. However, following ICI therapy or TT-either alone (especially ICI) or in combination with radiotherapy-imaging findings on anatomical contrast-enhanced MRI can be unpredictable and highly variable, and are often difficult to interpret regarding treatment response and outcome. This review aims at summarizing the imaging challenges related to TT and ICI monotherapy as well as combined with radiotherapy in patients with brain metastases, and to give an overview on advanced imaging techniques which potentially overcome some of these imaging challenges. Currently, major evidence suggests that imaging parameters especially derived from amino acid PET, perfusion-/diffusion-weighted MRI, or MR spectroscopy may provide valuable additional information for the differentiation of treatment-induced changes from brain metastases recurrence and the evaluation of treatment response.

This guideline provides recommendations for the use of PET imaging in gliomas. The review examines established clinical benefit in glioma patients of PET using glucose ((18)F-FDG) and amino acid tracers ((11)C-MET, (18)F-FET, and (18)F-FDOPA). An increasing number of studies have been published on PET imaging in the setting of diagnosis, biopsy, and resection as well radiotherapy planning, treatment monitoring, and response assessment. Recommendations are based on evidence generated from studies which validated PET findings by histology or clinical course. This guideline emphasizes the clinical value of PET imaging with superiority of amino acid PET over glucose PET and provides a framework for the use of PET to assist in the management of patients with gliomas.

Background

To assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria.

Methods

A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling.

Results

The CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02).

Conclusions

AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).

Meningiomas are the most common primary intracranial tumors in adults and are increasing in incidence due to the aging population and increased access to neuroimaging. While most exhibit nonmalignant behavior, a subset of meningiomas are biologically aggressive and are associated with treatment resistance, resulting in significant neurologic morbidity and even mortality. In recent years, meaningful advances in our understanding of the biology of these tumors have led to the incorporation of molecular biomarkers into their grading and prognostication. However, unlike other central nervous system (CNS) tumors, a unified molecular taxonomy for meningiomas has not yet been established and remains an overarching goal of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy-Not Official World Health Organization (cIMPACT-NOW) working group. Additionally, clinical equipoise still remains on how specific meningioma cases and patient populations should be optimally managed. To address these existing gaps, members of the International Consortium on Meningiomas including field-leading experts, have prepared this comprehensive consensus narrative review directed toward clinicians, researchers, and patients. Included in this manuscript are detailed overviews of proposed molecular classifications, novel biomarkers, contemporary treatment strategies, trials on systemic therapies, health-related quality-of-life studies, and management strategies for unique meningioma patient populations. In each section, we discuss the current state of knowledge as well as ongoing clinical and research challenges to road map future directions for further investigation.

Surgical resection represents the standard of care for people with newly diagnosed diffuse gliomas, and the neuropathological and molecular profile of the resected tissue guides clinical management and forms the basis for research. The Response Assessment in Neuro-Oncology (RANO) consortium is an international, multidisciplinary effort that aims to standardise research practice in neuro-oncology. These recommendations represent a multidisciplinary consensus from the four RANO groups: RANO resect, RANO recurrent glioblastoma, RANO radiotherapy, and RANO/PET for a standardised workflow to achieve a representative tumour evaluation in a disease characterised by intratumoural heterogeneity, including recommendations on which tumour regions should be surgically sampled, how to define those regions on the basis of preoperative imaging, and the optimal sample volume. Practical recommendations for tissue sampling are given for people with low-grade and high-grade gliomas, as well as for people with newly diagnosed and recurrent disease. Sampling of liquid biopsies is also addressed. A standardised workflow for subsequent handling of the resected tissue is proposed to avoid information loss due to decreasing tissue quality or insufficient clinical information. The recommendations offer a framework for prospective biobanking studies.