Role of amino-acid PET in high-grade gliomas: limitations and perspectives

Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma

This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[18F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744-0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716-0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548-0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512-0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts.

A multi-label CNN model for the automatic detection and segmentation of gliomas using [18F]FET PET imaging

PURPOSE

The aim of this study was to develop a convolutional neural network (CNN) for the automatic detection and segmentation of gliomas using [18F]fluoroethyl-L-tyrosine ([18F]FET) PET.

METHODS

Ninety-three patients (84 in-house/7 external) who underwent a 20-40-min static [18F]FET PET scan were retrospectively included. Lesions and background regions were defined by two nuclear medicine physicians using the MIM software, such that delineations by one expert reader served as ground truth for training and testing the CNN model, while delineations by the second expert reader were used to evaluate inter-reader agreement. A multi-label CNN was developed to segment the lesion and background region while a single-label CNN was implemented for a lesion-only segmentation. Lesion detectability was evaluated by classifying [18F]FET PET scans as negative when no tumor was segmented and vice versa, while segmentation performance was assessed using the dice similarity coefficient (DSC) and segmented tumor volume. The quantitative accuracy was evaluated using the maximal and mean tumor to mean background uptake ratio (TBRmax/TBRmean). CNN models were trained and tested by a threefold cross-validation (CV) using the in-house data, while the external data was used for an independent evaluation to assess the generalizability of the two CNN models.

RESULTS

Based on the threefold CV, the multi-label CNN model achieved 88.9% sensitivity and 96.5% precision for discriminating between positive and negative [18F]FET PET scans compared to a 35.3% sensitivity and 83.1% precision obtained with the single-label CNN model. In addition, the multi-label CNN allowed an accurate estimation of the maximal/mean lesion and mean background uptake, resulting in an accurate TBRmax/TBRmean estimation compared to a semi-automatic approach. In terms of lesion segmentation, the multi-label CNN model (DSC = 74.6 ± 23.1%) demonstrated equal performance as the single-label CNN model (DSC = 73.7 ± 23.2%) with tumor volumes estimated by the single-label and multi-label model (22.9 ± 23.6 ml and 23.1 ± 24.3 ml, respectively) closely approximating the tumor volumes estimated by the expert reader (24.1 ± 24.4 ml). DSCs of both CNN models were in line with the DSCs by the second expert reader compared with the lesion segmentations by the first expert reader, while detection and segmentation performance of both CNN models as determined with the in-house data were confirmed by the independent evaluation using external data.

CONCLUSION

The proposed multi-label CNN model detected positive [18F]FET PET scans with high sensitivity and precision. Once detected, an accurate tumor segmentation and estimation of background activity was achieved resulting in an automatic and accurate TBRmax/TBRmean estimation, such that user interaction and potential inter-reader variability can be minimized.

Contribution of nuclear medicine to the diagnosis and management of primary brain tumours

Positron emission tomography (PET) is a powerful tool that can help physicians manage primary brain tumours at diagnosis and follow-up. In this context, PET imaging is used with three main types of radiotracers: ¹⁸F-FDG, amino acid radiotracers, and ⁶⁸Ga conjugated to somatostatin receptor ligands (SSTRs). At initial diagnosis, ¹⁸F-FDG helps to characterize primary central nervous system (PCNS) lymphomas and high-grade gliomas, amino acid radiotracers are indicated for gliomas, and SSTR PET ligands are indicated for meningiomas. Such radiotracers provide information on tumour grade or type, assist in directing biopsies and help with treatment planning. During follow-up, in the presence of symptoms and/or MRI modifications, the differential diagnosis between tumour recurrence and post-therapeutic changes, in particular radiation necrosis, may be challenging, and there is strong interest in using PET to evaluate therapeutic toxicity. PET may also contribute to identifying specific complications, such as postradiation therapy encephalopathy, encephalitis associated with PCNS lymphoma, and stroke-like migraine after radiation therapy (SMART) syndrome associated with glioma recurrence and temporal epilepsy, originally illustrated in this review. This review summarizes the main contribution of PET to the diagnosis, management, and follow-up of brain tumours, specifically gliomas, meningiomas, and primary central nervous system lymphomas.

F18-FET PET in pediatric brain tumors: integrative analysis of image derived parameters and clinico-pathological data

BACKGROUND

F18-FET PET has an established diagnostic role in adult brain gliomas. In this study we analyzed image derived static and dynamic parameters with available conventional MRI, histological, clinical and follow-up data in assessment of pediatric brain tumor patients at different stages of the disease.

METHODS

Forty-four pediatric patients with median age 7 years, diagnosed with brain tumors and underwent forty-seven 18F-FET PET scans either initially (20 scans) or post-therapy (27 scans) were enrolled. Standardized analysis of summed FET PET images early from 10-20 min and late from 30-40 min post-injection were used for static (mean and maximum tumor to brain ratio [TBR] and biological tumor volume [BTV]) parameters evaluation as well as the time activity curve [TAC].

RESULTS

Nineteen out of 20 initially assessed patients had pathologically and/or clinico-radiologically proven neoplastic lesions and one patient had pathologically proven abscess. Receiver operator curve (ROC) marked early TBR max 2.95, early TBR mean 1.76, late TBR max 2.5 and late TBR mean 1.74 as discriminator points with diagnostic accuracy reaching 90% when TBR max was combined with dynamic parameters. Significant association was found between initial FET scans, early and late BTV and event free survival (EFS) (P value=0.042 and 0.005 respectively). In post-therapy assessment, the diagnostic accuracy of conventional MRI was 81.48% when used alone and 96.30% when combined with F18-FET PET scan findings. A cutoff point of 3.2 cm³ for late BTV, in post-therapy scans, was successfully marked as a predictor for therapy response (P value 0.042) and was significantly associated with EFS (P value 0.002). In FET-avid / MRI non-enhancing lesions, early TBR max was able to detect highly malignant processes (high-grade tumors in initial scans and residue/recurrence in post-therapy scans) with 80% sensitivity and 100% specificity when cutoff value of 2.25 was used (P value=0.024). In patients with FET-avid brainstem lesions, whether enhancing or non-enhancing in MRI scans, 81.8% were associated with high risk diagnoses and 68.2% of them were associated with poor therapy outcome. The degree of FET uptake matched tumor-grading, but did not show significant association with OS or EFS (P value>0.05).

CONCLUSIONS

F18-FET PET seems to be an evolving pediatric neuro-imaging technique with valuable diagnostic and prognostic information at initial and post-therapy evaluation.

Hotspot on 18F-FET PET/CT to Predict Aggressive Tumor Areas for Radiotherapy Dose Escalation Guiding in High-Grade Glioma

The standard therapy strategy for high-grade glioma (HGG) is based on the maximal surgery followed by radio-chemotherapy (RT-CT) with insufficient control of the disease. Recurrences are mainly localized in the radiation field, suggesting an interest in radiotherapy dose escalation to better control the disease locally. We aimed to identify a similarity between the areas of high uptake on O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) positron emission tomography/computed tomography (PET) before RT-CT, the residual tumor on post-therapy NADIR magnetic resonance imaging (MRI) and the area of recurrence on MRI. This is an ancillary study from the IMAGG prospective trial assessing the interest of FET PET imaging in RT target volume definition of HGG. We included patients with diagnoses of HGG obtained by biopsy or tumor resection. These patients underwent FET PET and brain MRIs, both after diagnosis and before RT-CT. The follow-up consisted of sequential brain MRIs performed every 3 months until recurrence. Tumor delineation on the initial MRI 1 (GTV 1), post-RT-CT NADIR MRI 2 (GTV 2), and progression MRI 3 (GTV 3) were performed semi-automatically and manually adjusted by a neuroradiologist specialist in neuro-oncology. GTV 2 and GTV 3 were then co-registered on FET PET data. Tumor volumes on FET PET (MTV) were delineated using a tumor to background ratio (TBR) ≥ 1.6 and different % SUVmax PET thresholds. Spatial similarity between different volumes was performed using the dice (DICE), Jaccard (JSC), and overlap fraction (OV) indices and compared together in the biopsy or partial surgery group (G1) and the total or subtotal surgery group (G2). Another overlap index (OV') was calculated to determine the threshold with the highest probability of being included in the residual volume after RT-CT on MRI 2 and in MRI 3 (called "hotspot"). A total of 23 patients were included, of whom 22% (n = 5) did not have a NADIR MRI 2 due to a disease progression diagnosed on the first post-RT-CT MRI evaluation. Among the 18 patients who underwent a NADIR MRI 2, the average residual tumor was approximately 71.6% of the GTV 1. A total of 22% of patients (5/23) showed an increase in GTV 2 without diagnosis of true progression by the multidisciplinary team (MDT). Spatial similarity between MTV and GTV 2 and between MTV and GTV 3 were higher using a TBR ≥ 1.6 threshold. These indices were significantly better in the G1 group than the G2 group. In the FET hotspot analysis, the best similarity (good agreement) with GTV 2 was found in the G1 group using a 90% SUVmax delineation method and showed a trend of statistical difference with those (poor agreement) in the G2 group (OV' = 0.67 vs. 0.38, respectively, p = 0.068); whereas the best similarity (good agreement) with GTV 3 was found in the G1 group using a 80% SUVmax delineation method and was significantly higher than those (poor agreement) in the G2 group (OV'= 0.72 vs. 0.35, respectively, p = 0.014). These results showed modest spatial similarity indices between MTV, GTV 2, and GTV 3 of HGG. Nevertheless, the results were significantly improved in patients who underwent only biopsy or partial surgery. TBR ≥ 1.6 and 80-90% SUVmax FET delineation methods showing a good agreement in the hotspot concept for targeting standard dose and radiation boost. These findings need to be tested in a larger randomized prospective study.

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges

Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.

Multi-tracer and multiparametric PET imaging to detect the IDH mutation in glioma: a preclinical translational in vitro, in vivo, and ex vivo study

Background

This translational study explores multi-tracer PET imaging for the non-invasive detection of the IDH1 mutation which is a positive prognostic factor in glioma.

Methods

U87 human high-grade glioma (HGG) isogenic cell lines with or without the IDH1 mutation (CRISP/Cas9 method) were stereotactically grafted into rat brains, and examined, in vitro, in vivo and ex vivo. PET imaging sessions, with radiotracers specific for glycolytic metabolism ([¹⁸F]FDG), amino acid metabolism ([¹⁸F]FDopa), and inflammation ([¹⁸F]DPA-714), were performed sequentially during 3–4 days. The in vitro radiotracer uptake was expressed as percent per million cells. For each radiotracer examined in vivo, static analyses included the maximal and mean tumor-to-background ratio (TBR_max and TBR_mean) and metabolic tumor volume (MTV). Dynamic analyses included the distribution volume ratio (DVR) and the relative residence time (RRT) extracted from a reference Logan model. Ex vivo analyses consisted of immunological analyses.

Results

In vitro, IDH1+ cells (i.e. cells expressing the IDH1 mutation) showed lower levels of [¹⁸F]DPA-714 uptake compared to IDH1- cells (p < 0.01). These results were confirmed in vivo with lower [¹⁸F]DPA-714 uptake in IDH+ tumors (3.90 versus 5.52 for TBR_max, p = 0.03). Different values of [¹⁸F]DPA-714 and [¹⁸F] FDopa RRT (respectively 11.07 versus 22.33 and 2.69 versus − 1.81 for IDH+ and IDH- tumors, p < 0.02) were also observed between the two types of tumors. RRT [¹⁸F]DPA-714 provided the best diagnostic performance to discriminate between the two cell lines (AUC of 100%, p < 0.01). Immuno-histological analyses revealed lower expression of Iba-1 and TSPO antibodies in IDH1+ tumors.

Conclusions

[18F]DPA-714 and [18F] FDopa both correlate with the presence of the IDH1 mutation in HGG. These radiotracers are therefore good candidates for translational studies investigating their clinical applications in patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00454-6.

PET Imaging in Neuro-Oncology: An Update and Overview of a Rapidly Growing Area

Simple Summary

Positron emission tomography (PET) is a functional imaging technique which plays an increasingly important role in the management of brain tumors. Owing different radiotracers, PET allows to image different metabolic aspects of the brain tumors. This review outlines currently available PET radiotracers and their respective indications in neuro-oncology. It specifically focuses on the investigation of gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in the production of PET radiotracers, image analyses and translational applications to peptide radionuclide receptor therapy, which allow to treat brain tumors with radiotracers, are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain magnetic resonance imaging (MRI).

Abstract

PET plays an increasingly important role in the management of brain tumors. This review outlines currently available PET radiotracers and their respective indications. It specifically focuses on ¹⁸F-FDG, amino acid and somatostatin receptor radiotracers, for imaging gliomas, meningiomas, primary central nervous system lymphomas as well as brain metastases. Recent advances in radiopharmaceuticals, image analyses and translational applications to therapy are also discussed. The objective of this review is to provide a comprehensive overview of PET imaging’s potential in neuro-oncology as an adjunct to brain MRI for all medical professionals implicated in brain tumor diagnosis and care.

What Does PET Imaging Bring to Neuro-Oncology in 2022? A Review

Simple Summary

Positron emission tomography (PET) imaging is increasingly used to supplement MRI in the management of patient with brain tumors. In this article, we provide a review of the current place and perspectives of PET imaging for the diagnosis and follow-up of from primary brain tumors such as gliomas, meningiomas and central nervous system lymphomas, as well as brain metastases. Different PET radiotracers targeting different biological processes are used to accurately depict these brain tumors and provide unique metabolic and biologic information. Radiolabeled amino acids such as [¹⁸F]FDOPA or [¹⁸F]FET are used for imaging of gliomas while both [¹⁸F]FDG and amino acids can be used for brain metastases. Meningiomas can be seen with a high contrast using radiolabeled ligands of somatostatin receptors, which they usually carry. Unconventional tracers that allow the study of other biological processes such as cell proliferation, hypoxia, or neo-angiogenesis are currently being studied for brain tumors imaging.

Abstract

PET imaging is being increasingly used to supplement MRI in the clinical management of brain tumors. The main radiotracers implemented in clinical practice include [¹⁸F]FDG, radiolabeled amino acids ([¹¹C]MET, [¹⁸F]FDOPA, [¹⁸F]FET) and [⁶⁸Ga]Ga-DOTA-SSTR, targeting glucose metabolism, L-amino-acid transport and somatostatin receptors expression, respectively. This review aims at addressing the current place and perspectives of brain PET imaging for patients who suffer from primary or secondary brain tumors, at diagnosis and during follow-up. A special focus is given to the following: radiolabeled amino acids PET imaging for tumor characterization and follow-up in gliomas; the role of amino acid PET and [¹⁸F]FDG PET for detecting brain metastases recurrence; [⁶⁸Ga]Ga-DOTA-SSTR PET for guiding treatment in meningioma and particularly before targeted radiotherapy.

Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes

This study evaluates the relevance of ¹⁸F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic ¹⁸F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features-radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both-in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBR_mean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values.

Dynamic 11C-Methionine PET-CT: Prognostic Factors for Disease Progression and Survival in Patients with Suspected Glioma Recurrence

Simple Summary

Recurrence after initial treatments is an expected event in glioma patients, particularly for high-grade glioma, with a median progression-free survival of 8–11 weeks. The prognostic evaluation of disease is a crucial step in the planning of therapeutic strategies, in both the primary and recurrence stages of disease. The aim of our retrospective study was to assess the prognostic value of ¹¹C-methionine PET-CT dynamic and semiquantitative parameters in patients with suspected glioma recurrence at MR, in terms of progression-free survival and overall survival. In a population of sixty-seven consecutive patients, both static and kinetic analyses provided parameters (i.e., tumour-to-background ratio and SUVmax associated with time-to-peak, respectively) able to predict both progression-free and overall survival in the whole population and in the high-grade glioma subgroup of patients. Dynamic ¹¹C-methionine PET-CT can be a useful diagnostic tool, in patients with suspicion of glioma recurrence, able to produce significant prognostic indices.

Abstract

Purpose: The prognostic evaluation of glioma recurrence patients is important in the therapeutic management. We investigated the prognostic value of ¹¹C-methionine PET-CT (MET-PET) dynamic and semiquantitative parameters in patients with suspected glioma recurrence. Methods: Sixty-seven consecutive patients who underwent MET-PET for suspected glioma recurrence at MR were retrospectively included. Twenty-one patients underwent static MET-PET; 46/67 underwent dynamic MET-PET. In all patients, SUVmax, SUVmean and tumour-to-background ratio (T/B) were calculated. From dynamic acquisition, the shape and slope of time-activity curves, time-to-peak and its SUVmax (SUVmax_TTP) were extrapolated. The prognostic value of PET parameters on progression-free (PFS) and overall survival (OS) was evaluated using Kaplan–Meier survival estimates and Cox regression. Results: The overall median follow-up was 19 months from MET-PET. Recurrence patients (38/67) had higher SUVmax (p = 0.001), SUVmean (p = 0.002) and T/B (p < 0.001); deceased patients (16/67) showed higher SUVmax (p = 0.03), SUVmean (p = 0.03) and T/B (p = 0.006). All static parameters were associated with PFS (all p < 0.001); T/B was associated with OS (p = 0.031). Regarding kinetic analyses, recurrence (27/46) and deceased (14/46) patients had higher SUVmax_TTP (p = 0.02, p = 0.01, respectively). SUVmax_TTP was the only dynamic parameter associated with PFS (p = 0.02) and OS (p = 0.006). At univariate analysis, SUVmax, SUVmean, T/B and SUVmax_TTP were predictive for PFS (all p < 0.05); SUVmax_TTP was predictive for OS (p = 0.02). At multivariate analysis, SUVmax_TTP remained significant for PFS (p = 0.03). Conclusion: Semiquantitative parameters and SUVmax_TTP were associated with clinical outcomes in patients with suspected glioma recurrence. Dynamic PET-CT acquisition, with static and kinetic parameters, can be a valuable non-invasive prognostic marker, identifying patients with worse prognosis who require personalised therapy.

Diagnostic Performance of PET and Perfusion-Weighted Imaging in Differentiating Tumor Recurrence or Progression from Radiation Necrosis in Posttreatment Gliomas: A Review of Literature

Tumor resection followed by chemoradiation remains the current criterion standard treatment for high-grade gliomas. Regardless of aggressive treatment, tumor recurrence and radiation necrosis are 2 different outcomes. Differentiation of tumor recurrence from radiation necrosis remains a critical problem in these patients because of considerable overlap in clinical and imaging presentations. Contrast-enhanced MR imaging is the universal imaging technique for diagnosis, treatment evaluation, and detection of recurrence of high-grade gliomas. PWI and PET with novel radiotracers have an evolving role for monitoring treatment response in high-grade gliomas. In the literature, there is no clear consensus on the superiority of either technique or their complementary information. This review aims to elucidate the diagnostic performance of individual and combined use of functional (PWI) and metabolic (PET) imaging modalities to distinguish recurrence from posttreatment changes in gliomas.

Integration of dynamic parameters in the analysis of 18F-FDopa PET imaging improves the prediction of molecular features of gliomas

PURPOSE

¹⁸F-FDopa PET imaging of gliomas is routinely interpreted with standardized uptake value (SUV)-derived indices. This study aimed to determine the added value of dynamic ¹⁸F-FDopa PET parameters for predicting the molecular features of newly diagnosed gliomas.

METHODS

We retrospectively included 58 patients having undergone an ¹⁸F-FDopa PET for establishing the initial diagnosis of gliomas, whose molecular features were additionally characterized according to the WHO 2016 classification. Dynamic parameters, involving time-to-peak (TTP) values and curve slopes, were tested for the prediction of glioma types in addition to current static parameters, i.e., tumor-to-normal brain or tumor-to-striatum SUV ratios and metabolic tumor volume (MTV).

RESULTS

There were 21 IDH mutant without 1p/19q co-deletion (IDH+/1p19q-) gliomas, 16 IDH mutants with 1p/19q co-deletion (IDH+/1p19q+) gliomas, and 21 IDH wildtype (IDH-) gliomas. Dynamic parameters enabled differentiating the gliomas according to these molecular features, whereas static parameters did not. In particular, a longer TTP was the single best independent predictor for identifying (1) IDH mutation status (area under the curve (AUC) of 0.789, global accuracy of 74% for the criterion of a TTP ≥ 5.4 min) and (2) 1p/19q co-deletion status (AUC of 0.679, global accuracy of 69% for the criterion of a TTP ≥ 6.9 min). Moreover, the TTP from IDH- gliomas was significantly shorter than those from both IDH+/1p19q- and IDH+/1p19q+ (p ≤ 0.007).

CONCLUSION

Prediction of the molecular features of newly diagnosed gliomas with ¹⁸F-FDopa PET and especially of the presence or not of an IDH mutation, may be obtained with dynamic but not with current static uptake parameters.

Glioma response assessment: Classic pitfalls, novel confounders, and emerging imaging tools

Neuroimaging plays a pivotal role in the care of patients with infiltrating gliomas, in whom imaging changes are often the first indications of tumor response or progression. Unfortunately, evaluation of glioma response is often not straightforward, even for experienced radiologists. Post-surgical or radiation-related changes may mimic the appearance of disease progression, while medications such as corticosteroids and antiangiogenic agents may mimic tumor response without truly arresting tumor growth or improving patient survival. Immunotherapy response can result in inflammatory changes which manifest as progressively increasing tumor enhancement and edema over months. Many of these pitfalls can be minimized or avoided altogether by the use of modern brain tumor response criteria, while others will require new imaging tools before they can be fully addressed. Advanced MRI methods and novel positron emission tomography (PET) agents are proving important for this purpose, and their role will undoubtedly continue to grow in the future.