MRI and 18FET-PET Predict Survival Benefit from Bevacizumab Plus Radiotherapy in Patients with Isocitrate Dehydrogenase Wild-type Glioblastoma: Results from the Randomized ARTE Trial

Stem the blood flow: beneficial impact of bevacizumab on survival of subventricular zone glioblastoma patients

PURPOSE

Angiogenesis is a crucial step in tumorigenesis of glioblastoma (GBM). Bevacizumab, an anti-vascular endothelial growth factor drug, is approved for second-line therapy for GBM. Glioma stem cells, presumably the cell of origin of GBM, take an active role in angiogenesis. The subventricular zone (SVZ) is the brain's largest reservoir of neural stem cells, and GBM near this region (SVZ GBM) is associated with a poor prognosis. This study aims to evaluate the potential impact of second-line bevacizumab treatment on survival in patients with SVZ GBM.

METHODS

The electronic medical records of adult patients with newly diagnosed SVZ GDM under treated between 1/2011 and 12/2021 were retrospectively reviewed. Clinical, surgical, radiological, and outcome parameters were compared between patients treated with bevacizumab after first relapse to patients without such treatment.

RESULTS

The cohort included 67 patients. 45 (67.1%) were treated with bevacizumab after the first relapse while 22 (32.9%) were not. The only statistically significant difference between groups was the rate of re-surgery, which was higher in the non-bevacizumab group (40.9% vs. 15.6%; p = 0.023), indicating that the groups were quite homogenous. In general, bevacizumab as a second-line treatment did not affect OS in SVZ GBM cases. However, it significantly prolongs survival time from 1st relapse by an average of more than 4 months, including after adjustment to re-surgery variable (HR = 0.57, 95% CI 0.34-0.94, p = 0.028 and HR = 0.57, 95%CI = 0.34-0.97, PV = 0.038; respectively). Furthermore, when adjusting to time from diagnosis to 1st relapse, bevacizumab treatment was also associated with prolonged OS (HR = 0.58; p = 0.043). In a subgroup analysis, comparing patients treated with both re-surgery and bevacizumab to patients treated in any other way, patients with the combined treatment had the longest mean OS of the entire cohort (22.16 ± 7.81 m vs. 13.60 ± 6.86, p = 0.049; HR = 0.361 95%CI 0.108-1.209, p = 0.085).

CONCLUSIONS

The use of bevacizumab as a second-line therapy in SVZ GBM cases may positively affect survival after relapse, even when given as a monotherapy. Additionally, in certain yet-to-be-identified sub-populations, bevacizumab may even extend overall survival. Further research is required to accurately identify SVZ GBM patients who would benefit most from anti-angiogenic therapy.

PET imaging of gliomas: Status quo and quo vadis?

PET imaging, particularly using amino acid tracers, has become a valuable adjunct to anatomical MRI in the clinical management of patients with glioma. Collaborative international efforts have led to the development of clinical and technical guidelines for PET imaging in gliomas. The increasing readiness of statutory health insurance agencies, especially in European countries, to reimburse amino acid PET underscores its growing importance in clinical practice. Integrating artificial intelligence and radiomics in PET imaging of patients with glioma may significantly improve tumor detection, segmentation, and response assessment. Efforts are ongoing to facilitate the clinical translation of these techniques. Considerable progress in computer technology developments (eg quantum computers) may be helpful to accelerate these efforts. Next-generation PET scanners, such as long-axial field-of-view PET/CT scanners, have improved image quality and body coverage and therefore expanded the spectrum of indications for PET imaging in Neuro-Oncology (eg PET imaging of the whole spine). Encouraging results of clinical trials in patients with glioma have prompted the development of PET tracers directing therapeutically relevant targets (eg the mutant isocitrate dehydrogenase) for novel anticancer agents in gliomas to improve response assessment. In addition, the success of theranostics for the treatment of extracranial neoplasms such as neuroendocrine tumors and prostate cancer has currently prompted efforts to translate this approach to patients with glioma. These advancements highlight the evolving role of PET imaging in Neuro-Oncology, offering insights into tumor biology and treatment response, thereby informing personalized patient care. Nevertheless, these innovations warrant further validation in the near future.

Contribution of [18F]FET PET in the Management of Gliomas, from Diagnosis to Follow-Up: A Review

Gliomas, the most common type of primary malignant brain tumors in adults, pose significant challenges in diagnosis and management due to their heterogeneity and potential aggressiveness. This review evaluates the utility of O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) positron emission tomography (PET), a promising imaging modality, to enhance the clinical management of gliomas. We reviewed 82 studies involving 4657 patients, focusing on the application of [18F]FET in several key areas: diagnosis, grading, identification of IDH status and presence of oligodendroglial component, guided resection or biopsy, detection of residual tumor, guided radiotherapy, detection of malignant transformation in low-grade glioma, differentiation of recurrence versus treatment-related changes and prognostic factors, and treatment response evaluation. Our findings confirm that [18F]FET helps delineate tumor tissue, improves diagnostic accuracy, and aids in therapeutic decision-making by providing crucial insights into tumor metabolism. This review underscores the need for standardized parameters and further multicentric studies to solidify the role of [18F]FET PET in routine clinical practice. By offering a comprehensive overview of current research and practical implications, this paper highlights the added value of [18F]FET PET in improving management of glioma patients from diagnosis to follow-up.

PET-based response assessment criteria for diffuse gliomas (PET RANO 1.0): a report of the RANO group

Response Assessment in Neuro-Oncology (RANO) response criteria have been established and were updated in 2023 for MRI-based response evaluation of diffuse gliomas in clinical trials. In addition, PET-based imaging with amino acid tracers is increasingly considered for disease monitoring in both clinical practice and clinical trials. So far, a standardised framework defining timepoints for baseline and follow-up investigations and response evaluation criteria for PET imaging of diffuse gliomas has not been established. Therefore, in this Policy Review, we propose a set of criteria for response assessment based on amino acid PET imaging in clinical trials enrolling participants with diffuse gliomas as defined in the 2021 WHO classification of tumours of the central nervous system. These proposed PET RANO criteria provide a conceptual framework that facilitates the structured implementation of PET imaging into clinical research and, ultimately, clinical routine. To this end, the PET RANO 1.0 criteria are intended to encourage specific investigations of amino acid PET imaging of gliomas.

Added prognostic value of DCE blood volume imaging in patients with suspected recurrent or residual glioblastoma-A hybrid [18F]FET PET/MRI study

Background

Magnetic resonance imaging (MRI) cerebral blood volume (CBV) measurements improve the diagnosis of recurrent gliomas. The study investigated the prognostic value of dynamic contrast-enhanced (DCE) CBV imaging in treated IDH wildtype glioblastoma when added to MRI or amino acid positron emission tomography (PET).

Methods

Hybrid [18F]FET PET/MRI with 2CXM (2-compartment exchange model) DCE from 86 adult patients with suspected recurrent or residual glioblastoma were retrospectively analyzed. High CBV tumor volume (VOLCBV), and contrast-enhancing (VOLCE) and [18F]FET active tumor (VOLFET) volumes were delineated. Absolute and fractional high CBV subvolumes within VOLCE and VOLFET were determined. Associations with overall survival (OS) were assessed by Cox analysis.

Results

Adjusted for methyltransferase gene status and steroid use all total tumor volumes were individually associated with shorter OS. Adding VOLCBV to VOLCE or VOLFET only the effect of VOLCBV was prognostic of OS (hazard ratio [HR] 1.327, P = .042 and 1.352, P = .011, respectively). High CBV subvolumes within both VOLCE and VOLFET were associated with shorter survival (HR 1.448, P = .042 and 1.416, P = .011, respectively), and the low CBV subvolumes with longer survival (HR 0.504, P = .002 and .365, P = .001, respectively). The fraction of VOLCE and VOLFET with high CBV was a strong predictor of OS with shorter median OS in upper versus lower tertiles (8.3 vs 14.5 months and 7.1 vs 15.6 months, respectively, both P < .001).

Conclusions

The high CBV tumor volume was a strong prognosticator of survival and allowed for the separation of high- and low-risk subvolumes underlining the heterogeneous physiological environment represented in the contrast-enhancing or metabolically active tumor volumes of treated glioblastoma.

18F-Fluoroethyl-L Tyrosine Positron Emission Tomography Radiomics in the Differentiation of Treatment-Related Changes from Disease Progression in Patients with Glioblastoma

The follow-up of glioma patients after therapeutic intervention remains a challenging topic, as therapy-related changes can emulate true progression in contrast-enhanced magnetic resonance imaging. 18F-fluoroethyl-tyrosine (18F-FET) is a radiopharmaceutical that accumulates in glioma cells due to an increased expression of L-amino acid transporters and, contrary to gadolinium, does not depend on blood-brain barrier disruption to reach tumoral cells. It has demonstrated a high diagnostic value in the differentiation of tumoral viability and pseudoprogression or any other therapy-related changes, especially when combining traditional visual analysis with modern radiomics. In this review, we aim to cover the potential role of 18F-FET positron emission tomography in everyday clinical practice when applied to the follow-up of patients after the first therapeutical intervention, early response evaluation, and the differential diagnosis between therapy-related changes and progression.

Clinical Value of Hybrid PET/MR Imaging: Brain Imaging Using PET/MR Imaging

Hybrid PET/MR imaging offers a unique opportunity to acquire MR imaging and PET information during a single imaging session. PET/MR imaging has numerous advantages, including enhanced diagnostic accuracy, improved disease characterization, and better treatment planning and monitoring. It enables the immediate integration of anatomic, functional, and metabolic imaging information, allowing for personalized characterization and monitoring of neurologic diseases. This review presents recent advances in PET/MR imaging and highlights advantages in clinical practice for neuro-oncology, epilepsy, and neurodegenerative disorders. PET/MR imaging provides valuable information about brain tumor metabolism, perfusion, and anatomic features, aiding in accurate delineation, treatment response assessment, and prognostication.

Amino Acid Tracer PET MRI in Glioma Management: What a Neuroradiologist Needs to Know

PET with amino acid tracers provides additional insight beyond MR imaging into the biology of gliomas that can be used for initial diagnosis, delineation of tumor margins, planning of surgical and radiation therapy, assessment of residual tumor, and evaluation of posttreatment response. Hybrid PET MR imaging allows the simultaneous acquisition of various PET and MR imaging parameters in a single investigation with reduced scanning time and improved anatomic localization. This review aimed to provide neuroradiologists with a concise overview of the various amino acid tracers and a practical understanding of the clinical applications of amino acid PET MR imaging in glioma management. Future perspectives in newer advances, novel radiotracers, radiomics, and cost-effectiveness are also outlined.

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather than harming all rapidly dividing cells as in cytotoxic chemotherapy. The Response Evaluation Criteria in Solid Tumor (RECIST) system has been used to assess tumor response to therapy via changes in the size of target lesions as measured by calipers, conventional anatomically based imaging modalities such as computed tomography (CT), and magnetic resonance imaging (MRI), and other imaging methods. However, RECIST is sometimes inaccurate in assessing the efficacy of targeted therapy drugs because of the poor correlation between tumor size and treatment-induced tumor necrosis or shrinkage. This approach might also result in delayed identification of response when the therapy does confer a reduction in tumor size. Innovative molecular imaging techniques have rapidly gained importance in the dawning era of targeted therapy as they can visualize, characterize, and quantify biological processes at the cellular, subcellular, or even molecular level rather than at the anatomical level. This review summarizes different targeted cell signaling pathways, various molecular imaging techniques, and developed probes. Moreover, the application of molecular imaging for evaluating treatment response and related clinical outcome is also systematically outlined. In the future, more attention should be paid to promoting the clinical translation of molecular imaging in evaluating the sensitivity to targeted therapy with biocompatible probes. In particular, multimodal imaging technologies incorporating advanced artificial intelligence should be developed to comprehensively and accurately assess cancer-targeted therapy, in addition to RECIST-based methods.

Imaging of Brain Tumors

OBJECTIVE

This article focuses on neuroimaging as an essential tool for diagnosing brain tumors and monitoring response to treatment.

LATEST DEVELOPMENTS

Neuroimaging is useful at all stages of brain tumor care. Technologic advances have improved the clinical diagnostic capability of neuroimaging as a vital complement to history, examination, and pathologic assessment. Presurgical evaluations are enriched by novel imaging techniques, through improved differential diagnosis and better surgical planning using functional MRI (fMRI) and diffusion tensor imaging. The common clinical challenge of differentiating tumor progression from treatment-related inflammatory change is aided by novel uses of perfusion imaging, susceptibility-weighted imaging (SWI), spectroscopy, and new positron emission tomography (PET) tracers.

ESSENTIAL POINTS

Using the most up-to-date imaging techniques will facilitate high-quality clinical practice in the care of patients with brain tumors.

18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy

Background

Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy.

Methods

A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results.

Results

First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression.

Conclusions

18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction.

Multispectral fluorine-19 MRI enables longitudinal and noninvasive monitoring of tumor-associated macrophages

High-grade gliomas, the most common and aggressive primary brain tumors, are characterized by a complex tumor microenvironment (TME). Among the immune cells infiltrating the glioma TME, tumor-associated microglia and macrophages (TAMs) constitute the major compartment. In patients with gliomas, increased TAM abundance is associated with more aggressive disease. Alterations in TAM phenotypes and functions have been reported in preclinical models of multiple cancers during tumor development and after therapeutic interventions, including radiotherapy and molecular targeted therapies. These findings indicate that it is crucial to evaluate TAM abundance and dynamics over time. Current techniques to quantify TAMs in patients rely mainly on histological staining of tumor biopsies. Although informative, these techniques require an invasive procedure to harvest the tissue sample and typically only result in a snapshot of a small region at a single point in time. Fluorine isotope 19 MRI (¹⁹F MRI) represents a powerful means to noninvasively and longitudinally monitor myeloid cells in pathological conditions by intravenously injecting perfluorocarbon-containing nanoparticles (PFC-NP). In this study, we demonstrated the feasibility and power of ¹⁹F MRI in preclinical models of gliomagenesis, breast-to-brain metastasis, and breast cancer and showed that the major cellular source of ¹⁹F signal consists of TAMs. Moreover, multispectral ¹⁹F MRI with two different PFC-NP allowed us to identify spatially and temporally distinct TAM niches in radiotherapy-recurrent murine gliomas. Together, we have imaged TAMs noninvasively and longitudinally with integrated cellular, spatial, and temporal resolution, thus revealing important biological insights into the critical functions of TAMs, including in disease recurrence.

Radiotherapy-drug combinations in the treatment of glioblastoma: a brief review

Glioblastoma (GBM) accounts for over 50% of gliomas and carries the worst prognosis of all solid tumors. Owing to the limited local control afforded by surgery alone, efficacious adjuvant treatments such as radiotherapy (RT) and chemotherapy are fundamental in achieving durable disease control. The best clinical outcomes are achieved with tri-modality treatment consisting of surgery, RT and systemic therapy. While RT-chemotherapy combination regimens are well established in oncology, this approach was largely unsuccessful in GBM until the introduction of temozolomide. The success of this combination has stimulated the search for other candidate drugs for concomitant use with RT in GBM. This review seeks to collate the current evidence for these agents and synthesize possible future directions for the field.

Advanced Imaging Techniques for Newly Diagnosed and Recurrent Gliomas

Management of gliomas following initial diagnosis requires thoughtful presurgical planning followed by regular imaging to monitor treatment response and survey for new tumor growth. Traditional MR imaging modalities such as T1 post-contrast and T2-weighted sequences have long been a staple of tumor diagnosis, surgical planning, and post-treatment surveillance. While these sequences remain integral in the management of gliomas, advances in imaging techniques have allowed for a more detailed characterization of tumor characteristics. Advanced MR sequences such as perfusion, diffusion, and susceptibility weighted imaging, as well as PET scans have emerged as valuable tools to inform clinical decision making and provide a non-invasive way to help distinguish between tumor recurrence and pseudoprogression. Furthermore, these advances in imaging have extended to the operating room and assist in making surgical resections safer. Nevertheless, surgery, chemotherapy, and radiation treatment continue to make the interpretation of MR changes difficult for glioma patients. As analytics and machine learning techniques improve, radiomics offers the potential to be more quantitative and personalized in the interpretation of imaging data for gliomas. In this review, we describe the role of these newer imaging modalities during the different stages of management for patients with gliomas, focusing on the pre-operative, post-operative, and surveillance periods. Finally, we discuss radiomics as a means of promoting personalized patient care in the future.

Pretreatment ADC Histogram Analysis as a Prognostic Imaging Biomarker for Patients with Recurrent Glioblastoma Treated with Bevacizumab: A Systematic Review and Meta-analysis

BACKGROUND

The mean ADC value of the lower Gaussian curve (ADC_L) derived from the bi-Gaussian curve-fitting histogram analysis has been reported as a predictive/prognostic imaging biomarker in patients with recurrent glioblastoma treated with bevacizumab; however, its systematic summary has been lacking.

PURPOSE

We applied a systematic review and meta-analysis to investigate the predictive/prognostic performance of ADC_L in patients with recurrent glioblastoma treated with bevacizumab.

DATA SOURCES

We performed a literature search using PubMed, Scopus, and EMBASE.

STUDY SELECTION

A total of 1344 abstracts were screened, of which 83 articles were considered potentially relevant. Data were finally extracted from 6 studies including 578 patients.

DATA ANALYSIS

Forest plots were generated to illustrate the hazard ratios of overall survival and progression-free survival. The heterogeneity across the studies was assessed using the Cochrane Q test and I² values.

DATA SYNTHESIS

The pooled hazard ratios for overall survival and progression-free survival in patients with an ADC_L lower than the cutoff values were 1.89 (95% CI, 1.53-2.31) and 1.98 (95% CI, 1.54-2.55) with low heterogeneity among the studies. Subgroup analysis of the bevacizumab-free cohort showed a pooled hazard ratio for overall survival of 1.20 (95% CI, 1.08-1.34) with low heterogeneity.

LIMITATIONS

The conclusions are limited by the difference in the definition of recurrence among the included studies.

CONCLUSIONS

This systematic review with meta-analysis supports the prognostic value of ADC_L in patients with recurrent glioblastoma treated with bevacizumab, with a low ADC_L demonstrating decreased overall survival and progression-free survival. On the other hand, the predictive role of ADC_L for bevacizumab treatment was not confirmed.

Dynamic susceptibility contrast and diffusion-weighted MRI in posterior fossa pilocytic astrocytoma and medulloblastoma

BACKGROUND AND PURPOSE

The utility of perfusion MRI in distinguishing between pilocytic astrocytoma (PA) and medulloblastoma (MB) is unclear. This study aimed to evaluate the diagnostic and prognostic performance of dynamic susceptibility contrast (DSC)-MRI parameters and apparent diffusion coefficient (ADC) values between PA and MB.

METHODS

Between January 2012 and August 2021, 49 (median, 7 years [range, 1-28 years]; 28 females) and 35 (median, 8 years [1-24 years]; 12 females) patients with pathologically confirmed PA and MB, respectively, were included. The normalized relative cerebral blood volume and flow (nrCBV and nrCBF) and mean and minimal normalized ADC (nADCmean and nADCmin) values were calculated using volume-of-interest analyses. Diagnostic performance and Pearson's correlation with progression-free survival were also evaluated.

RESULTS

The MB group showed a significantly higher nrCBV and nrCBF (nrCBV: 1.69 [0.93-4.23] vs. 0.95 [range, 0.37-2.28], p = .0032; nrCBF: 1.62 [0.93-3.16] vs. 1.07 [0.46-2.26], p = .0084) and significantly lower nADCmean and nADCmin (nADCmean: 0.97 [0.70-1.68] vs. 2.21 [1.44-2.80], p < .001; nADCmin: 0.50 [0.19-0.89] vs. 1.42 [0.89-2.20], p < .001) than the PA group. All parameters exhibited good diagnostic ability (accuracy >0.80) with nADCmin achieving the highest score (accuracy = 1). A moderate correlation was found between nADCmean and progression-free survival for MB (r = 0.44, p = .0084).

CONCLUSIONS

DSC-MRI parameters and ADC values were useful for distinguishing between PA and MB. A lower ADC indicated an unfavorable MB prognosis, but the DSC-MRI parameters did not correlate with progression-free survival in either group.

Leveraging machine learning predictive biomarkers to augment the statistical power of clinical trials with baseline magnetic resonance imaging

A key factor in designing randomized clinical trials is the sample size required to achieve a particular level of power to detect the benefit of a treatment. Sample size calculations depend upon the expected benefits of a treatment (effect size), the accuracy of measurement of the primary outcome, and the level of power specified by the investigators. In this study, we show that radiomic models, which leverage complex brain MRI patterns and machine learning, can be utilized in clinical trials with protocols that incorporate baseline MR imaging to significantly increase statistical power to detect treatment effects. Akin to the historical control paradigm, we propose to utilize a radiomic prediction model to generate a pseudo-control sample for each individual in the trial of interest. Because the variability of expected outcome across patients can mask our ability to detect treatment effects, we can increase the power to detect a treatment effect in a clinical trial by reducing that variability through using radiomic predictors as surrogates. We illustrate this method with simulations based on data from two cohorts in different neurologic diseases, Alzheimer's disease and glioblastoma multiforme. We present sample size requirements across a range of effect sizes using conventional analysis and models that include a radiomic predictor. For our Alzheimer's disease cohort, at an effect size of 0.35, total sample size requirements for 80% power declined from 246 to 212 for the endpoint cognitive decline. For our glioblastoma multiforme cohort, at an effect size of 1.65 with the endpoint survival time, total sample size requirements declined from 128 to 74. This methodology can decrease the required sample sizes by as much as 50%, depending on the strength of the radiomic predictor. The power of this method grows with increased accuracy of radiomic prediction, and furthermore, this method is most helpful when treatment effect sizes are small. Neuroimaging biomarkers are a powerful and increasingly common suite of tools that are, in many cases, highly predictive of disease outcomes. Here, we explore the possibility of using MRI-based radiomic biomarkers for the purpose of improving statistical power in clinical trials in the contexts of brain cancer and prodromal Alzheimer's disease. These methods can be applied to a broad range of neurologic diseases using a broad range of predictors of outcome to make clinical trials more efficient.

Advanced Imaging Techniques for Radiotherapy Planning of Gliomas

The accuracy of target delineation in radiation treatment (RT) planning of cerebral gliomas is crucial to achieve high tumor control, while minimizing treatment-related toxicity. Conventional magnetic resonance imaging (MRI), including contrast-enhanced T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, represents the current standard imaging modality for target volume delineation of gliomas. However, conventional sequences have limited capability to discriminate treatment-related changes from viable tumors, owing to the low specificity of increased blood-brain barrier permeability and peritumoral edema. Advanced physiology-based MRI techniques, such as MR spectroscopy, diffusion MRI and perfusion MRI, have been developed for the biological characterization of gliomas and may circumvent these limitations, providing additional metabolic, structural, and hemodynamic information for treatment planning and monitoring. Radionuclide imaging techniques, such as positron emission tomography (PET) with amino acid radiopharmaceuticals, are also increasingly used in the workup of primary brain tumors, and their integration in RT planning is being evaluated in specialized centers. This review focuses on the basic principles and clinical results of advanced MRI and PET imaging techniques that have promise as a complement to RT planning of gliomas.