Activation of CD4 T cells during prime immunization determines the success of a therapeutic hepatitis B vaccine in HBV-carrier mouse models.

BACKGROUND & AIMS: We recently developed a heterologous therapeutic vaccination scheme (TherVacB) comprising a particulate protein prime followed by a modified vaccinia-virus Ankara (MVA)-vector boost for the treatment of HBV. However, the key determinants required to overcome HBV-specific immune tolerance remain unclear. Herein, we aimed to study new combination adjuvants and unravel factors that are essential for the antiviral efficacy of TherVacB. METHODS: Recombinant hepatitis B surface and core antigen (HBsAg and HBcAg) particles were formulated with different liposome- or oil-in-water emulsion-based combination adjuvants containing saponin QS21 and monophosphoryl lipid A; these formulations were compared to STING-agonist c-di-AMP and conventional aluminium hydroxide formulations. Immunogenicity and the antiviral effects of protein antigen formulations and the MVA-vector boost within TherVacB were evaluated in adeno-associated virus-HBV-infected and HBV-transgenic mice. RESULTS: Combination adjuvant formulations preserved HBsAg and HBcAg integrity for ≥12 weeks, promoted human and mouse dendritic cell activation and, within TherVacB, elicited robust HBV-specific antibody and T-cell responses in wild-type and HBV-carrier mice. Combination adjuvants that prime a balanced HBV-specific type 1 and 2 T helper response induced high-titer anti-HBs antibodies, cytotoxic T-cell responses and long-term control of HBV. In the absence of an MVA-vector boost or following selective CD8 T-cell depletion, HBsAg still declined (mediated mainly by anti-HBs antibodies) but HBV replication was not controlled. Selective CD4 T-cell depletion during the priming phase of TherVacB resulted in a complete loss of vaccine-induced immune responses and its therapeutic antiviral effect in mice. CONCLUSIONS: Our results identify CD4 T-cell activation during the priming phase of TherVacB as a key determinant of HBV-specific antibody and CD8 T-cell responses. IMPACT AND IMPLICATIONS: Therapeutic vaccination is a potentially curative treatment option for chronic hepatitis B. However, it remains unclear which factors are essential for breaking immune tolerance in HBV carriers and determining successful outcomes. Our study provides the first direct evidence that efficient priming of HBV-specific CD4 T cells determines the success of therapeutic hepatitis B vaccination in two preclinical HBV-carrier mouse models. Applying an optimal formulation of HBV antigens that activates CD4 and CD8 T cells during prime immunization provided the foundation for an antiviral effect of therapeutic vaccination, while depletion of CD4 T cells led to a complete loss of vaccine-induced antiviral efficacy. Boosting CD8 T cells was important to finally control HBV in these mouse models. Our findings provide important insights into the rational design of therapeutic vaccines for the cure of chronic hepatitis B.

Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT.

OBJECTIVE: Liver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment. MATERIALS AND METHODS: 252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation. RESULTS: On the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors >0.5cm3 performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89. CONCLUSION: The proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis. PURPOSE: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI. STUDY TYPE: Retrospective. POPULATION: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers. ASSESSMENT: A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T1 CE served as the reference standard. STATISTICAL TESTS: Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant. RESULTS: In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm3 ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm3 ) than detected BMs (0.96 ± 2.4 cm3 ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed. DATA CONCLUSION: Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.

Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation.

PURPOSE: Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation. METHODS: The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus. RESULTS: Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume. CONCLUSION: Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

Bone-Preserving Arthroplasty With Abductor Pollicis Longus Tenodesis for Trapeziometacarpal Osteoarthritis.

Purpose: The objective of this study was to describe an original method of bone-preserving arthroplasty with abductor pollicis longus (APL) tenodesis and determine its safety and effectiveness as a treatment for early-stage osteoarthritis of the trapeziometacarpal joint. Methods: Eleven patients underwent a trapezium-preserving arthroplasty with APL tenodesis for stage 1 and 2 osteoarthritis were retrospectively reviewed. This arthroplasty consisted of a distally-based APL tendon being passed through the trapeziometacarpal joint. The proximal end of the tendon was then pulled and passed through a drill hole made at the neck of the second metacarpal and sutured to itself. Thus, distraction of the first metacarpal and interposition of the tendon were created. Postoperative radiologic and clinical follow-up visits were performed at 4, 8, and 12 weeks. Range of motion and strength were assessed after surgery. Patient satisfaction and outcome were assessed, and the disabilities of the arm, shoulder, and hand (DASH) score was used. Results: After a mean follow-up of 29.5 months (range, 16-43 months), the mean patient visual analog scale pain score improved from 7.1 to 2.3. The average DASH score of all patients at the follow-up examination was 18.3 ± 19.8. Patients' mean grip strength was 25.3 kg, which represented 102% of the value on the contralateral side. The key-pinch strength was 6.2 kg on the operated hand compared with 6.5 kg on the contralateral side. The mean thumb opposition Kapandji index was 9.4, which was similar to that of the contralateral side. Three patients were very satisfied with the postoperative outcome and 3 patients were satisfied. Two patients were lost to follow-up, 1 patient did not consent to share her data, and 2 patients had to undergo trapeziectomy. Conclusions: Although a larger study population and a longer follow-up period are needed to draw conclusions, bone-preserving arthroplasty with APL tenodesis showed satisfying results in patients presenting with early-stage osteoarthritis. This method is technically simple and time-efficient, does not reduce the range of motion, and leaves open all other surgical options. Type of study/level of evidence: Therapeutic IV, Case Series.

Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.

BACKGROUND: Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-consuming and impeded by intra- and interrater variabilities. PURPOSE: To investigate the performance of a deep-learning model (DLM) to automatically detect and segment primary central nervous system lymphoma (PCNSL) on clinical MRI. STUDY TYPE: Retrospective. POPULATION: Sixty-nine scans (at initial and/or follow-up imaging) from 43 patients with PCNSL referred for clinical MRI tumor assessment. FIELD STRENGTH/SEQUENCE: T1 -/T2 -weighted, T1 -weighted contrast-enhanced (T1 CE), and FLAIR at 1.0, 1.5, and 3.0T from different vendors and study centers. ASSESSMENT: Fully automated voxelwise segmentation of tumor components was performed using a 3D convolutional neural network (DeepMedic) trained on gliomas (n = 220). DLM segmentations were compared to manual segmentations performed in a 3D voxelwise manner by two readers (radiologist and neurosurgeon; consensus reading) from T1 CE and FLAIR, which served as the reference standard. STATISTICAL TESTS: Dice similarity coefficient (DSC) for comparison of spatial overlap with the reference standard, Pearson's correlation coefficient (r) to assess the relationship between volumetric measurements of segmentations, and Wilcoxon rank-sum test for comparison of DSCs obtained in initial and follow-up imaging. RESULTS: The DLM detected 66 of 69 PCNSL, representing a sensitivity of 95.7%. Compared to the reference standard, DLM achieved good spatial overlap for total tumor volume (TTV, union of tumor volume in T1 CE and FLAIR; average size 77.16 ± 62.4 cm3 , median DSC: 0.76) and tumor core (contrast enhancing tumor in T1 CE; average size: 11.67 ± 13.88 cm3 , median DSC: 0.73). High volumetric correlation between automated and manual segmentations was observed (TTV: r = 0.88, P < 0.0001; core: r = 0.86, P < 0.0001). Performance of automated segmentations was comparable between pretreatment and follow-up scans without significant differences (TTV: P = 0.242, core: P = 0.177). DATA CONCLUSION: In clinical MRI scans, a DLM initially trained on gliomas provides segmentation of PCNSL comparable to manual segmentation, despite its complex and multifaceted appearance. Segmentation performance was high in both initial and follow-up scans, suggesting its potential for application in longitudinal tumor imaging. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

A Broad-Spectrum Antiviral Peptide Blocks Infection of Viruses by Binding to Phosphatidylserine in the Viral Envelope.

The ongoing threat of viral infections and the emergence of antiviral drug resistance warrants a ceaseless search for new antiviral compounds. Broadly-inhibiting compounds that act on elements shared by many viruses are promising antiviral candidates. Here, we identify a peptide derived from the cowpox virus protein CPXV012 as a broad-spectrum antiviral peptide. We found that CPXV012 peptide hampers infection by a multitude of clinically and economically important enveloped viruses, including poxviruses, herpes simplex virus-1, hepatitis B virus, HIV-1, and Rift Valley fever virus. Infections with non-enveloped viruses such as Coxsackie B3 virus and adenovirus are not affected. The results furthermore suggest that viral particles are neutralized by direct interactions with CPXV012 peptide and that this cationic peptide may specifically bind to and disrupt membranes composed of the anionic phospholipid phosphatidylserine, an important component of many viral membranes. The combined results strongly suggest that CPXV012 peptide inhibits virus infections by direct interactions with phosphatidylserine in the viral envelope. These results reiterate the potential of cationic peptides as broadly-acting virus inhibitors.

Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading.

OBJECTIVE: Meningioma grading is relevant to therapy decisions in complete or partial resection, observation, and radiotherapy because higher grades are associated with tumor growth and recurrence. The differentiation of low and intermediate grades is particularly challenging. This study attempts to apply radiomics-based shape and texture analysis on routine multiparametric magnetic resonance imaging (MRI) from different scanners and institutions for grading. METHODS: We used MRI data (T1-weighted/T2-weighted, T1-weighted-contrast-enhanced [T1CE], fluid-attenuated inversion recovery [FLAIR], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC]) of grade I (n = 46) and grade II (n = 25) nontreated meningiomas with histologic workup. Two experienced radiologists performed manual tumor segmentations on FLAIR, T1CE, and ADC images in consensus. The MRI data were preprocessed through T1CE and T1-subtraction, coregistration, resampling, and normalization. A PyRadiomics package was used to generate 990 shape/texture features. Stepwise dimension reduction and robust radiomics feature selection were performed. Biopsy results were used as standard of reference. RESULTS: Four statistically independent radiomics features were identified as showing the strongest predictive values for higher tumor grades: roundness-of-FLAIR-shape (area under curve [AUC], 0.80), cluster-shades-of-FLAIR/T1CE-gray-level (AUC, 0.80), DWI/ADC-gray-level-variability (AUC, 0.72), and FLAIR/T1CE-gray-level-energy (AUC, 0.76). In a multivariate logistic regression model, the combination of the features led to an AUC of 0.91 for the differentiation of grade I and grade II meningiomas. CONCLUSIONS: Our results indicate that radiomics-based feature analysis applied on routine MRI is viable for meningioma grading, and a multivariate logistic regression model yielded strong classification performances. More advanced tumor stages are identifiable through certain shape parameters of the lesion, textural patterns in morphologic MRI sequences, and DWI/ADC variability.

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.

OBJECTIVES: Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. METHODS: We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. RESULTS: The DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE. CONCLUSIONS: The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity. KEY POINTS: • Deep learning allows for accurate meningioma detection and segmentation • Deep learning helps clinicians to assess patients with meningiomas • Meningioma monitoring and treatment planning can be improved.

Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.

OBJECTIVES: The aims of this study were, first, to evaluate a deep learning-based, automatic glioblastoma (GB) tumor segmentation algorithm on clinical routine data from multiple centers and compare the results to a ground truth, manual expert segmentation, and second, to evaluate the quality of the segmentation results across heterogeneous acquisition protocols of routinely acquired clinical magnetic resonance imaging (MRI) examinations from multiple centers. MATERIALS AND METHODS: The data consisted of preoperative MRI scans (T1, T2, FLAIR, and contrast-enhanced [CE] T1) of 64 patients with an initial diagnosis of primary GB, which were acquired in 15 institutions with varying protocols. All images underwent preprocessing (coregistration, skull stripping, resampling to isotropic resolution, normalization) and were fed into an independently trained deep learning model based on DeepMedic, a multilayer, multiscale convolutional neural network for detection and segmentation of tumor compartments. Automatic segmentation results for the whole tumor, necrosis, and CE tumor were compared with manual segmentations. RESULTS: Whole tumor and CE tumor compartments were correctly detected in 100% of the cases; necrosis was correctly detected in 91% of the cases. A high segmentation accuracy comparable to interrater variability was achieved for the whole tumor (mean dice similarity coefficient [DSC], 0.86 ± 0.09) and CE tumor (DSC, 0.78 ± 0.15). The DSC for tumor necrosis was 0.62 ± 0.30. We have observed robust segmentation quality over heterogeneous image acquisition protocols, for example, there were no correlations between resolution and segmentation accuracy of the single tumor compartments. Furthermore, no relevant correlation was found between quality of automatic segmentation and volume of interest properties (surface-to-volume ratio and volume). CONCLUSIONS: The proposed approach for automatic segmentation of GB proved to be robust on routine clinical data and showed on all tumor compartments a high automatic detection rate and a high accuracy, comparable to interrater variability. Further work on improvements of the segmentation accuracy for the necrosis compartments should be guided by the evaluation of the clinical relevance.Therefore, we propose this approach as a suitable building block for automatic tumor segmentation to support radiologists or neurosurgeons in the preoperative reading of GB MRI images and characterization of primary GB.

T Cells Engineered to Express a T-Cell Receptor Specific for Glypican-3 to Recognize and Kill Hepatoma Cells In Vitro and in Mice.

BACKGROUND & AIMS: Cancer therapies are being developed based on our ability to direct T cells against tumor antigens. Glypican-3 (GPC3) is expressed by 75% of all hepatocellular carcinomas (HCC), but not in healthy liver tissue or other organs. We aimed to generate T cells with GPC3-specific receptors that recognize HCC and used them to eliminate GPC3-expressing xenograft tumors grown from human HCC cells in mice. METHODS: We used mass spectrometry to obtain a comprehensive peptidome from GPC3-expressing hepatoma cells after immune-affinity purification of human leukocyte antigen (HLA)-A2 and bioinformatics to identify immunodominant peptides. To circumvent GPC3 tolerance resulting from fetal expression, dendritic cells from HLA-A2-negative donors were cotransfected with GPC3 and HLA-A2 RNA to stimulate and expand antigen-specific T cells. RESULTS: Peptide GPC3367 was identified as a predominant peptide on HLA-A2. We used A2-GPC3367 multimers to detect, select for, and clone GPC3-specific T cells. These clones bound the A2-GPC3367 multimer and secreted interferon-γ when cultured with GPC3367, but not with control peptide-loaded cells. By genomic sequencing of these T-cell clones, we identified a gene encoding a dominant T-cell receptor. The gene was cloned and the sequence was codon optimized and expressed from a retroviral vector. Primary CD8(+) T cells that expressed the transgenic T-cell receptor specifically bound GPC3367 on HLA-A2. These T cells killed GPC3-expressing hepatoma cells in culture and slowed growth of HCC xenograft tumors in mice. CONCLUSIONS: We identified a GPC3367-specific T-cell receptor. Expression of this receptor by T cells allows them to recognize and kill GPC3-positive hepatoma cells. This finding could be used to advance development of adoptive T-cell therapy for HCC.

Cardiopulmonary dysfunction in the Osteogenesis imperfecta mouse model Aga2 and human patients are caused by bone-independent mechanisms.

Osteogenesis imperfecta (OI) is an inherited connective tissue disorder with skeletal dysplasia of varying severity, predominantly caused by mutations in the collagen I genes (COL1A1/COL1A2). Extraskeletal findings such as cardiac and pulmonary complications are generally considered to be significant secondary features. Aga2, a murine model for human OI, was systemically analyzed in the German Mouse Clinic by means of in vivo and in vitro examinations of the cardiopulmonary system, to identify novel mechanisms accounting for perinatal lethality. Pulmonary and, especially, cardiac fibroblast of perinatal lethal Aga2/+ animals display a strong down-regulation of Col1a1 transcripts in vivo and in vitro, resulting in a loss of extracellular matrix integrity. In addition, dysregulated gene expression of Nppa, different types of collagen and Agt in heart and lung tissue support a bone-independent vicious cycle of heart dysfunction, including hypertrophy, loss of myocardial matrix integrity, pulmonary hypertension, pneumonia and hypoxia leading to death in Aga2. These murine findings are corroborated by a pediatric OI cohort study, displaying significant progressive decline in pulmonary function and restrictive pulmonary disease independent of scoliosis. Most participants show mild cardiac valvular regurgitation, independent of pulmonary and skeletal findings. Data obtained from human OI patients and the mouse model Aga2 provide novel evidence for primary effects of type I collagen mutations on the heart and lung. The findings will have potential benefits of anticipatory clinical exams and early intervention in OI patients.

The quantification of dynamic FET PET imaging and correlation with the clinical outcome in patients with glioblastoma.

The PET tracer O-(2-[18F]Fluoroethyl)-l-tyrosine (FET) has been shown to be valuable for different roles in the management of brain tumours. The aim of this study was to evaluate several quantitative measures of dynamic FET PET imaging in patients with resected glioblastoma. We evaluated dynamic FET PET in nine patients with histologically confirmed glioblastoma. Following FET PET, all subjects had radiation and chemotherapy. Tumour ROIs were defined by a threshold-based region-growing algorithm. We compared several standard measures of tumour uptake and uptake kinetics: SUV, SUV/background, distribution volume ratio (DVR), weighted frame differences and compartment model parameters. These measures were correlated with disease-free and overall survival, and analysed for statistical significance. We found that several measures allowed robust quantification. SUV and distribution volume did not correlate with clinical outcome. Measures that are based on a background region (SUV/BG, Logan-DVR) highly correlated with disease-free survival (r = -0.95, p < 0.0001), but not overall survival. Some advanced measures also showed a prognostic value but no improvement over the simpler methods. We conclude that FET PET probably has a prognostic value in patients with resected glioblastoma. The ratio of SUV to background may provide a simple and valuable predictive measure of the clinical outcome. Further studies are needed to confirm these explorative results.