Clinical applications and prospects of PET imaging in patients with IDH-mutant gliomas.

PET imaging using radiolabeled amino acids in addition to MRI has become a valuable diagnostic tool in the clinical management of patients with brain tumors. This review provides a comprehensive overview of PET studies in glioma patients with a mutation in the isocitrate dehydrogenase gene (IDH). A considerable fraction of these tumors typically show no contrast enhancement on MRI, especially when classified as grade 2 according to the World Health Organization classification of Central Nervous System tumors. Major diagnostic challenges in this situation are differential diagnosis, target definition for diagnostic biopsies, delineation of glioma extent for treatment planning, differentiation of treatment-related changes from tumor progression, and the evaluation of response to alkylating agents. The main focus of this review is the role of amino acid PET in this setting. Furthermore, in light of clinical trials using IDH inhibitors targeting the mutated IDH enzyme for treating patients with IDH-mutant gliomas, we also aim to give an outlook on PET probes specifically targeting the IDH mutation, which appear potentially helpful for response assessment.

The impact of brain lesions on health-related quality of life in patients with WHO CNS grade 3 or 4 glioma: a lesion-function and resting-state fMRI analysis.

PURPOSE: In glioma patients, tumor development and multimodality therapy are associated with changes in health-related quality of life (HRQoL). It is largely unknown how different types and locations of tumor- and treatment-related brain lesions, as well as their relationship to white matter tracts and functional brain networks, affect HRQoL. METHODS: In 121 patients with pretreated gliomas of WHO CNS grades 3 or 4, structural MRI, O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET, resting-state functional MRI (rs-fMRI) and self-reported HRQoL questionnaires (EORTC QLQ-C30/BN20) were obtained. Resection cavities, T1-enhancing lesions, T2/FLAIR hyperintensities, and lesions with pathologically increased FET uptake were delineated. Effects of tumor lateralization, involvement of white matter tracts or resting-state network nodes by different types of lesions and within-network rs-fMRI connectivity were analyzed in terms of their interaction with HRQoL scores. RESULTS: Right hemisphere gliomas were associated with significantly less favorable outcomes in physical, role, emotional and social functioning, compared with left-sided tumors. Most functional HRQoL scores correlated significantly with right-sided white-matter tracts involvement by T2/FLAIR hyperintensities and with loss of within-network functional connectivity of right-sided nodes. Tumors of the left hemisphere caused significantly more communication deficits. CONCLUSION: In pretreated high-grade gliomas, right hemisphere lesions are associated with reduced HRQoL scores in most functional domains except communication ability, compared to tumors of the left hemisphere. These relationships are mainly observed for T2/FLAIR lesions involving structural and functional networks in the right hemisphere. The data suggest that sparing the right hemisphere from treatment-related tissue damage may improve HRQoL in glioma patients.

Radiomics for the non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to non-small cell lung cancer.

BACKGROUND: The expression level of the programmed cell death ligand 1 (PD-L1) appears to be a predictor for response to immunotherapy using checkpoint inhibitors in patients with non-small cell lung cancer (NSCLC). As differences in terms of PD-L1 expression levels in the extracranial primary tumor and the brain metastases may occur, a reliable method for the non-invasive assessment of the intracranial PD-L1 expression is, therefore of clinical value. Here, we evaluated the potential of radiomics for a non-invasive prediction of PD-L1 expression in patients with brain metastases secondary to NSCLC. PATIENTS AND METHODS: Fifty-three NSCLC patients with brain metastases from two academic neuro-oncological centers (group 1, n = 36 patients; group 2, n = 17 patients) underwent tumor resection with a subsequent immunohistochemical evaluation of the PD-L1 expression. Brain metastases were manually segmented on preoperative T1-weighted contrast-enhanced MRI. Group 1 was used for model training and validation, group 2 for model testing. After image pre-processing and radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. The radiomics model was trained and validated using random stratified cross-validation. Finally, the best-performing radiomics model was applied to the test data. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analyses. RESULTS: An intracranial PD-L1 expression (i.e., staining of at least 1% or more of tumor cells) was present in 18 of 36 patients (50%) in group 1, and 7 of 17 patients (41%) in group 2. Univariate analysis identified the contrast-enhancing tumor volume as a significant predictor for PD-L1 expression (area under the ROC curve (AUC), 0.77). A random forest classifier using a four-parameter radiomics signature, including tumor volume, yielded an AUC of 0.83 ± 0.18 in the training data (group 1), and an AUC of 0.84 in the external test data (group 2). CONCLUSION: The developed radiomics classifiers allows for a non-invasive assessment of the intracranial PD-L1 expression in patients with brain metastases secondary to NSCLC with high accuracy.

Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression.

PURPOSE: To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas. PATIENTS AND METHODS: One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[18F]fluoroethyl)-L-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBRmean, TBRmax) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBRmean, TBRmax, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis. RESULTS: Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBRmean and TBRmax resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%). CONCLUSION: The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.

Radiomics in neuro-oncology: Basics, workflow, and applications.

Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and cost-effective evaluation of imaging data is hardly feasible without the support of methods from the field of artificial intelligence (AI). AI can facilitate and shorten various time-consuming steps in the image processing workflow, e.g., tumor segmentation, thereby optimizing productivity. Besides, the automated and computer-based analysis of imaging data may help to increase data comparability as it is independent of the experience level of the evaluating clinician. Importantly, AI offers the potential to extract new features from the routinely acquired neuroimages of brain tumor patients. In combination with patient data such as survival, molecular markers, or genomics, mathematical models can be generated that allow, for example, the prediction of treatment response or prognosis, as well as the noninvasive assessment of molecular markers. The subdiscipline of AI dealing with the computation, identification, and extraction of image features, as well as the generation of prognostic or predictive mathematical models, is termed radiomics. This review article summarizes the basics, the current workflow, and methods used in radiomics with a focus on feature-based radiomics in neuro-oncology and provides selected examples of its clinical application.

Sequential implementation of DSC-MR perfusion and dynamic [18F]FET PET allows efficient differentiation of glioma progression from treatment-related changes.

PURPOSE: Perfusion-weighted MRI (PWI) and O-(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) PET are both applied to discriminate tumor progression (TP) from treatment-related changes (TRC) in patients with suspected recurrent glioma. While the combination of both methods has been reported to improve the diagnostic accuracy, the performance of a sequential implementation has not been further investigated. Therefore, we retrospectively analyzed the diagnostic value of consecutive PWI and [18F]FET PET. METHODS: We evaluated 104 patients with WHO grade II-IV glioma and suspected TP on conventional MRI using PWI and dynamic [18F]FET PET. Leakage corrected maximum relative cerebral blood volumes (rCBVmax) were obtained from dynamic susceptibility contrast PWI. Furthermore, we calculated static (i.e., maximum tumor to brain ratios; TBRmax) and dynamic [18F]FET PET parameters (i.e., Slope). Definitive diagnoses were based on histopathology (n = 42) or clinico-radiological follow-up (n = 62). The diagnostic performance of PWI and [18F]FET PET parameters to differentiate TP from TRC was evaluated by analyzing receiver operating characteristic and area under the curve (AUC). RESULTS: Across all patients, the differentiation of TP from TRC using rCBVmax or [18F]FET PET parameters was moderate (AUC = 0.69-0.75; p < 0.01). A rCBVmax cutoff > 2.85 had a positive predictive value for TP of 100%, enabling a correct TP diagnosis in 44 patients. In the remaining 60 patients, combined static and dynamic [18F]FET PET parameters (TBRmax, Slope) correctly discriminated TP and TRC in a significant 78% of patients, increasing the overall accuracy to 87%. A subgroup analysis of isocitrate dehydrogenase (IDH) mutant tumors indicated a superior performance of PWI to [18F]FET PET (AUC = 0.8/< 0.62, p < 0.01/≥ 0.3). CONCLUSION: While marked hyperperfusion on PWI indicated TP, [18F]FET PET proved beneficial to discriminate TP from TRC when PWI remained inconclusive. Thus, our results highlight the clinical value of sequential use of PWI and [18F]FET PET, allowing an economical use of diagnostic methods. The impact of an IDH mutation needs further investigation.

18F-FET-PET-guided gross total resection improves overall survival in patients with WHO grade III/IV glioma: moving towards a multimodal imaging-guided resection.

PURPOSE: PET using radiolabeled amino acid [18F]-fluoro-ethyl-L-tyrosine (FET-PET) is a well-established imaging modality for glioma diagnostics. The biological tumor volume (BTV) as depicted by FET-PET often differs in volume and location from tumor volume of contrast enhancement (CE) in MRI. Our aim was to investigate whether a gross total resection of BTVs defined as < 1 cm3 of residual BTV (PET GTR) correlates with better oncological outcome. METHODS: We retrospectively analyzed imaging and survival data from patients with primary and recurrent WHO grade III or IV gliomas who underwent FET-PET before surgical resection. Tumor overlap between FET-PET and CE was evaluated. Completeness of FET-PET resection (PET GTR) was calculated after superimposition and semi-automated segmentation of pre-operative FET-PET and postoperative MRI imaging. Survival analysis was performed using the Kaplan-Meier method and the log-rank test. RESULTS: From 30 included patients, PET GTR was achieved in 20 patients. Patients with PET GTR showed improved median OS with 19.3 compared to 13.7 months for patients with residual FET uptake (p = 0.007; HR 0.3; 95% CI 0.12-0.76). This finding remained as independent prognostic factor after performing multivariate analysis (HR 0.19, 95% CI 0.06-0.62, p = 0.006). Other survival influencing factors such as age, IDH-mutation, MGMT promotor status, and adjuvant treatment modalities were equally distributed between both groups. CONCLUSION: Our results suggest that PET GTR improves the OS in patients with WHO grade III or IV gliomas. A multimodal imaging approach including FET-PET for surgical planning in newly diagnosed and recurrent tumors may improve the oncological outcome in glioma patients.

A Novel Anti-Inflammatory d-Peptide Inhibits Disease Phenotype Progression in an ALS Mouse Model.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterised by selective neuronal death in the brain stem and spinal cord. The cause is unknown, but an increasing amount of evidence has firmly certified that neuroinflammation plays a key role in ALS pathogenesis. Neuroinflammation is a pathological hallmark of several neurodegenerative disorders and has been implicated as driver of disease progression. Here, we describe a treatment study demonstrating the therapeutic potential of a tandem version of the well-known all-d-peptide RD2 (RD2RD2) in a transgenic mouse model of ALS (SOD1*G93A). Mice were treated intraperitoneally for four weeks with RD2RD2 vs. placebo. SOD1*G93A mice were tested longitudinally during treatment in various behavioural and motor coordination tests. Brain and spinal cord samples were investigated immunohistochemically for gliosis and neurodegeneration. RD2RD2 treatment in SOD1*G93A mice resulted not only in a reduction of activated astrocytes and microglia in both the brain stem and lumbar spinal cord, but also in a rescue of neurons in the motor cortex. RD2RD2 treatment was able to slow progression of the disease phenotype, especially the motor deficits, to an extent that during the four weeks treatment duration, no significant progression was observed in any of the motor experiments. Based on the presented results, we conclude that RD2RD2 is a potential therapeutic candidate against ALS.

Radiomics in radiation oncology-basics, methods, and limitations.

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.

BACKGROUND: Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. METHODS: This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. RESULTS: Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80-90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. CONCLUSION: Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

Prediction of survival in patients with IDH-wildtype astrocytic gliomas using dynamic O-(2-[18F]-fluoroethyl)-L-tyrosine PET.

PURPOSE: Integrated histomolecular diagnostics of gliomas according to the World Health Organization (WHO) classification of 2016 has refined diagnostic accuracy and prediction of prognosis. This study aimed at exploring the prognostic value of dynamic O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET in newly diagnosed, histomolecularly classified astrocytic gliomas of WHO grades III or IV. METHODS: Before initiation of treatment, dynamic FET PET imaging was performed in patients with newly diagnosed glioblastoma (GBM) and anaplastic astrocytoma (AA). Static FET PET parameters such as maximum and mean tumour/brain ratios (TBRmax/mean), the metabolic tumour volume (MTV) as well as the dynamic FET PET parameters time-to-peak (TTP) and slope, were obtained. The predictive ability of FET PET parameters was evaluated concerning the progression-free and overall survival (PFS, OS). Using ROC analyses, threshold values for FET PET parameters were obtained. Subsequently, univariate Kaplan-Meier and multivariate Cox regression survival analyses were performed to assess the predictive power of these parameters for survival. RESULTS: Sixty patients (45 GBM and 15 AA patients) of two university centres were retrospectively identified. Patients with isocitrate dehydrogenase (IDH)-mutant or O6-methylguanine-DNA-methyltransferase (MGMT) promoter-methylated tumours had a significantly longer PFS and OS (both P < 0.001). Furthermore, ROC analysis of IDH-wildtype glioma patients (n = 45) revealed that a TTP > 25 min (AUC, 0.90; sensitivity, 90%; specificity, 87%; P < 0.001) was highly prognostic for longer PFS (13 vs. 7 months; P = 0.005) and OS (29 vs. 12 months; P < 0.001). In contrast, at a lower level of significance, TBRmax, TBRmean, and MTV were only prognostic for longer OS (P = 0.004, P = 0.038, and P = 0.048, respectively). Besides complete resection and a methylated MGMT promoter, TTP remained significant in multivariate survival analysis (all P ≤ 0.02), indicating an independent predictor for OS. CONCLUSIONS: Our data suggest that dynamic FET PET allows the identification of patients with longer OS among patients with newly diagnosed IDH-wildtype GBM and AA.

Current Landscape and Emerging Fields of PET Imaging in Patients with Brain Tumors.

The number of positron-emission tomography (PET) tracers used to evaluate patients with brain tumors has increased substantially over the last years. For the management of patients with brain tumors, the most important indications are the delineation of tumor extent (e.g., for planning of resection or radiotherapy), the assessment of treatment response to systemic treatment options such as alkylating chemotherapy, and the differentiation of treatment-related changes (e.g., pseudoprogression or radiation necrosis) from tumor progression. Furthermore, newer PET imaging approaches aim to address the need for noninvasive assessment of tumoral immune cell infiltration and response to immunotherapies (e.g., T-cell imaging). This review summarizes the clinical value of the landscape of tracers that have been used in recent years for the above-mentioned indications and also provides an overview of promising newer tracers for this group of patients.

FET PET reveals considerable spatial differences in tumour burden compared to conventional MRI in newly diagnosed glioblastoma.

PURPOSE: Areas of contrast enhancement (CE) on MRI are usually the target for resection or radiotherapy target volume definition in glioblastomas. However, the solid tumour mass may extend beyond areas of CE. Amino acid PET can detect parts of the tumour that show no CE. We systematically investigated tumour volumes delineated by amino acid PET and MRI in patients with newly diagnosed, untreated glioblastoma. METHODS: Preoperatively, 50 patients with neuropathologically confirmed glioblastoma underwent O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET, and fluid-attenuated inversion recovery (FLAIR) and contrast-enhanced MRI. Areas of CE were manually segmented. FET PET tumour volumes were segmented using a tumour-to-brain ratio of ≥1.6. The percentage overlap volumes, and Dice and Jaccard spatial similarity coefficients (DSC, JSC) were calculated. FLAIR images were evaluated visually. RESULTS: In 43 patients (86%), the FET tumour volume was significantly larger than the CE volume (21.5 ± 14.3 mL vs. 9.4 ± 11.3 mL; P < 0.001). Forty patients (80%) showed both increased uptake of FET and CE. In these 40 patients, the spatial similarity between FET uptake and CE was low (mean DSC 0.39 ± 0.21, mean JSC 0.26 ± 0.16). Ten patients (20%) showed no CE, and one of these patients showed no FET uptake. In five patients (10%), increased FET uptake was present outside areas of FLAIR hyperintensity. CONCLUSION: Our results show that the metabolically active tumour volume delineated by FET PET is significantly larger than tumour volume delineated by CE. Furthermore, the results strongly suggest that the information derived from both imaging modalities should be integrated into the management of patients with newly diagnosed glioblastoma.

Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI.

BACKGROUND: Following brain cancer treatment, the capacity of anatomical MRI to differentiate neoplastic tissue from treatment-related changes (e.g., pseudoprogression) is limited. This study compared apparent diffusion coefficients (ADC) obtained by diffusion-weighted MRI (DWI) with static and dynamic parameters of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation of treatment-related changes from tumour progression. PATIENTS AND METHODS: Forty-eight pretreated high-grade glioma patients with anatomical MRI findings suspicious for progression (median time elapsed since last treatment was 16 weeks) were investigated using DWI and dynamic FET PET. Maximum and mean tumour-to-brain ratios (TBRmax, TBRmean) as well as dynamic parameters (time-to-peak and slope values) of FET uptake were calculated. For mean ADC calculation, regions-of-interest analyses were performed on ADC maps calculated from DWI coregistered with the contrast-enhanced MR image. Diagnoses were confirmed neuropathologically (21%) or clinicoradiologically. Diagnostic performance was evaluated using receiver-operating-characteristic analyses or Fisher's exact test for a combinational approach. RESULTS: Ten of 48 patients had treatment-related changes (21%). The diagnostic performance of FET PET was significantly higher (threshold for both TBRmax and TBRmean, 1.95; accuracy, 83%; AUC, 0.89 ± 0.05; P < 0.001) than that of ADC values (threshold ADC, 1.09 × 10-3 mm2/s; accuracy, 69%; AUC, 0.73 ± 0.09; P = 0.13). The addition of static FET PET parameters to ADC values increased the latter's accuracy to 89%. The highest accuracy was achieved by combining static and dynamic FET PET parameters (93%). Moreover, in contrast to ADC values, TBRs <1.95 at suspected progression predicted a significantly longer survival (P = 0.01). CONCLUSIONS: Data suggest that static and dynamic FET PET provide valuable information concerning the differentiation of early treatment-related changes from tumour progression and outperform ADC measurement for this highly relevant clinical question.

Static and dynamic 18F-FET PET for the characterization of gliomas defined by IDH and 1p/19q status.

PURPOSE: The molecular features isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion have gained major importance for both glioma typing and prognosis and have, therefore, been integrated in the World Health Organization (WHO) classification in 2016. The aim of this study was to characterize static and dynamic O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) PET parameters in gliomas with or without IDH mutation or 1p/19q co-deletion. METHODS: Ninety patients with newly diagnosed and untreated gliomas with a static and dynamic 18F-FET PET scan prior to evaluation of tumor tissue according to the 2016 WHO classification were identified retrospectively. Mean and maximum tumor-to-brain ratios (TBRmean/max), as well as dynamic parameters (time-to-peak and slope) of 18F-FET uptake were calculated. RESULTS: Sixteen (18%) oligodendrogliomas (IDH mutated, 1p/19q co-deleted), 27 (30%) astrocytomas (IDH mutated only), and 47 (52%) glioblastomas (IDH wild type only) were identified. TBRmean, TBRmax, TTP and slope discriminated between IDH mutated astrocytomas and IDH wild type glioblastomas (P < 0.01). TBRmean showed the best diagnostic performance (cut-off 1.95; sensitivity, 89%; specificity, 67%; accuracy, 81%). None of the parameters discriminated between oligodendrogliomas (IDH mutated, 1p/19q co-deleted) and glioblastomas or astrocytomas. Furthermore, TBRmean, TBRmax, TTP, and slope discriminated between gliomas with and without IDH mutation (p < 0.01). The best diagnostic performance was obtained for the combination of TTP with TBRmax or slope (accuracy, 73%). CONCLUSION: Data suggest that static and dynamic 18F-FET PET parameters may allow determining non-invasively the IDH mutation status. However, IDH mutated and 1p/19q co-deleted oligodendrogliomas cannot be differentiated from glioblastomas and astrocytomas by 18F-FET PET.

Early treatment response evaluation using FET PET compared to MRI in glioblastoma patients at first progression treated with bevacizumab plus lomustine.

BACKGROUND: The goal of this prospective study was to compare the value of both conventional MRI and O-(2-18F-fluoroethyl)-L-tyrosine (FET) PET for response evaluation in glioblastoma patients treated with bevacizumab plus lomustine (BEV/LOM) at first progression. METHODS: After chemoradiation with concomitant and adjuvant temozolomide, 21 IDH wild-type glioblastoma patients at first progression (age range, 33-75 years; MGMT promoter unmethylated, 81%) were treated with BEV/LOM. Contrast-enhanced MRI and FET-PET scans were performed at baseline and after 8-10 weeks. We obtained FET metabolic tumor volumes (MTV) and tumor/brain ratios. Threshold values of FET-PET parameters for treatment response were established by ROC analyses using the post-progression overall survival (OS) ≤/>9 months as the reference. MRI response assessment was based on RANO criteria. The predictive ability of FET-PET thresholds and MRI changes on early response assessment was evaluated subsequently concerning OS using uni- and multivariate survival estimates. RESULTS: Early treatment response as assessed by RANO criteria was not predictive for an OS>9 months (P = 0.203), whereas relative reductions of all FET-PET parameters significantly predicted an OS>9 months (P < 0.05). The absolute MTV at follow-up enabled the most significant OS prediction (sensitivity, 85%; specificity, 88%; P = 0.001). Patients with an absolute MTV below 5 ml at follow-up survived significantly longer (12 vs. 6 months, P < 0.001), whereas early responders defined by RANO criteria lived only insignificantly longer (9 vs. 6 months; P = 0.072). The absolute MTV at follow-up remained significant in the multivariate survival analysis (P = 0.006). CONCLUSIONS: FET-PET appears to be useful for identifying responders to BEV/LOM early after treatment initiation.

Radiomics derived from amino-acid PET and conventional MRI in patients with high-grade gliomas.

Radiomics is a technique that uses high-throughput computing to extract quantitative features from tomographic medical images such as MRI and PET that usually are beyond visual perception. Importantly, the radiomics approach can be performed using neuroimages that have already been acquired during the routine follow-up of the patients allowing an additional data evaluation at low cost. In Neuro-Oncology, these features can potentially be used for differential diagnosis of newly diagnosed cerebral lesions suggestive for brain tumors or for the prediction of response to a neurooncological treatment option. Furthermore, especially in the light of the recent update of the World Health Organization classification of brain tumors, radiomics also has the potential to non-invasively assess important prognostic and predictive molecular markers such as a mutation in the isocitrate dehydrogenase gene or a 1p/19q codeletion which are not accessible by conventional visual interpretation of MRI or PET findings. This review summarizes the current status of the rapidly evolving field of radiomics with a special focus on patients with high-grade gliomas.

Functional magnetic resonance imaging in glioma patients: from clinical applications to future perspectives.

Functional magnetic resonance imaging (fMRI) allows the non-invasive assessment of human brain activity in vivo. In glioma patients, fMRI is frequently used to determine the individual functional anatomy of the motor and language network in a presurgical setting to optimize surgical procedures and prevent extensive damage to functionally eloquent areas. Novel developments based on resting-state fMRI may help to improve presurgical planning for patients which are unable to perform structured tasks and might extend presurgical mapping to include additional functional networks. Recent advances indicate a promising potential for future applications of fMRI in glioma patients which might help to identify neoplastic tissue or predict the long-term functional outcome of individual patients.

Imaging of amino acid transport in brain tumours: Positron emission tomography with O-(2-[18F]fluoroethyl)-L-tyrosine (FET).

The assessment of cerebral gliomas using magnetic resonance imaging (MRI) provides excellent structural images but cannot solve all diagnostic problems satisfactorily. The differentiation of tumour tissue from non-neoplastic changes may be difficult especially in the post-treatment phase. In recent years, positron emission tomography (PET) using radiolabelled amino acids has gained considerable interest as an additional tool to improve the diagnosis of cerebral gliomas and brain metastases. A key step for this advancement was the development of the F-18 labelled amino acid O-(2-[18F]fluoroethyl)-L-tyrosine (FET) which has spread rapidly in the last decade and replaced carbon-11 labelled amino acid tracers such as 11C-methyl-L-methionine (MET) in many centres in Europe. FET can be produced with high efficiency and distributed in a satellite concept like 2-[18F]fluoro-2-deoxy-D-glucose (FDG). Furthermore, FET exhibits favourable properties such as high in vivo stability, high tumour to background contrast and tissue specific tracer kinetics, which provides additional information for tumour grading or differential diagnosis. The Response Assessment in Neuro-Oncology (RANO) working group - an international effort to develop new standardized response criteria for clinical trials in brain tumours - has recently recommended the additional use of amino acid PET imaging for brain tumour management. FET PET can provide important diagnostic information in crucial situations such as the definition of biopsy site, the delineation of cerebral gliomas for therapy planning, sensitive monitoring of treatment response and an improved differentiation of tumour recurrence from treatment-related changes. In this article the basic information, methodological aspects and the actual status of clinical application of FET PET are reviewed.