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Purpose: For the clinical evaluation of O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) PET images, the use of standard summation images obtained 20-40 min after injection is recommended. However, early summation images obtained 5-15 min after injection have been reported to allow better differentiation between low-grade glioma (LGG) and high-grade glioma (HGG) by capturing the early 18F-FET uptake peak specific for HGG. We compared early and standard summation images with regard to delineation of the PET-derived biological tumour volume (BTV) in correlation with the molecular genetic profile according the updated 2016 WHO classification.Methods: The analysis included 245 patients with newly diagnosed, histologically verified glioma and a positive 18F-FET PET scan prior to any further treatment. BTVs were delineated during the early 5-15 min and standard 20-40 min time frames using a threshold of 1.6 × background activity and were compared intraindividually. Volume differences between early and late summation images of >20% were considered significant and were correlated with WHO grade and the molecular genetic profile (IDH mutation and 1p/19q codeletion status).Results: In 52.2% of the patients (128/245), a significant difference in BTV of >20% between early and standard summation images was found. While 44.3% of WHO grade II gliomas (31 of 70) showed a significantly smaller BTV in the early summation images, 35.0% of WHO grade III gliomas (28/80) and 37.9% of WHO grade IV gliomas (36/95) had a significantly larger BTVs. Among IDH-wildtype gliomas, an even higher portion (44.4%, 67/151) showed significantly larger BTVs in the early summation images, which was observed in 5.3% (5/94) of IDH-mutant gliomas only: most of the latter had significantly smaller BTVs in the early summation images, i.e. 51.2% of IDH-mutant gliomas without 1p/19q codeletion (21/41) and 39.6% with 1p/19q codeletion (21/53).Conclusion: BTVs delineated in early and standard summation images differed significantly in more than half of gliomas. While the standard summation images seem appropriate for delineation of LGG as well as IDH-mutant gliomas, a remarkably high percentage of HGG and, particularly, IDH-wildtype gliomas were depicted with significantly larger volumes in early summation images. This finding might be of interest for optimization of treatment planning (e.g. radiotherapy) in accordance with the individual IDH mutation status. [ABSTRACT FROM AUTHOR]

The name of M. Unterrainer was inadvertently presented as M. Unterrrainer in the original article. [ABSTRACT FROM AUTHOR]

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., Competing Interests: Declaration of interests NLA has received honoraria for consultation or advisory board participation from Novartis (Advanced Accelerator Applications), Telix Pharmaceuticals, and Servier; and research funding from Novocure. NG received honoraria for lectures from Blue Earth Diagnostics and honoraria for advisory board participation from Telix Pharmaceuticals. BME is on the advisory board and is a paid consultant for Medicenna, MedQIA, Servier Pharmaceuticals, Siemens, Janssen Pharmaceuticals, Imaging Endpoints, Kazia, Chimerix, Sumitomo Dainippon Pharma Oncology, ImmunoGenesis, Ellipses Pharma, Monteris, Neosoma, Alpheus Medical, Sagimet Biosciences, Sapience Therapeutics, Orbus Therapeutics, and the Global Coalition for Adaptive Research. MJvdB has received honoraria from Roche, AstraZeneca, Servier, Boehringer, Genenta, Fore Biotherapeutics, Incyte, and Nerviano. ELR reports personal financial interests as an advisory board member for Bayer, Janssen, Leo Pharma, Pierre Fabre, Roche, Seattle Genetics, and Servier; and institutional funding from Bristol Myers Squibb. MJM has received research support from Bristol Myers Squibb and travel support from Pierre Fabre. GM has received honoraria for lectures from BrainLab. RR has received research grants from Bayer, and honoraria for lectures or consulting from UCB, Novocure, Genenta, Curevac, and Servier. AMS has received research support to his institution from Medimmune, AVID, Telix Pharmaceuticals, ITM, Fusion, Cyclotek, Adalta, TheraMyc, Curis, Humanigen, and Antengene; funding support from the Medical Research Future Fund, Australian Brain Cancer Mission, Cure Brain Cancer Foundation, National Breast Cancer Foundation, Australian Cancer Research Foundation, National Imaging Facility, and Victorian Cancer Agency; and is a National Health and Medical Research Council Investigator. MS has received honoraria for consultancy from Bracco and honoraria for lectures from GE Healthcare, AuntMinnie, and Fondazione Internazionale Menarini (all paid to their institution). BS has received honoraria for lectures and consultation from Novocure. J-CT has received honoraria for lectures, consultation, or advisory board participation from SeagGen, AAA-Novartis, Novocure, and Munich Surgical Imaging. AV has received honoraria for lectures and advisory board participation from Curium, Eisai, and General Electrics. MW has received research grants from Quercis and Versameb; and honoraria for lectures or advisory board participation or consulting from Bayer, Curevac, Medac, Novartis, Novocure, Orbus, Philogen, Roche, and Servier. PYW has received research support from AstraZeneca, Black Diamond, Bristol Myers Squibb, Celgene, Chimerix, Eli Lilly, Erasca, Genentech/Roche, Kazia, MediciNova, Merck, Novartis, Nuvation Bio, Servier, Vascular Biogenics, and VBI Vaccines; and honoraria for serving as a consultant or on advisory boards for AstraZeneca, Black Diamond, Celularity, Chimerix, Day One Bio, Genenta, GSK, Merck, Mundipharma, Novartis, Novocure, Nuvation Bio, Prelude Therapeutics, Sapience, Servier, Sagimet, Vascular Biogenics, and VBI Vaccines. MP has received honoraria for lectures, consultation, or advisory board participation from Bayer, Bristol Myers Squibb, Novartis, Gerson Lehrman Group, CMC Contrast, GSK, Mundipharma, Roche, BMJ Journals, MedMedia, AstraZeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dohme, Tocagen, Adastra, Gan & Lee Pharmaceuticals, and Servier; and travel support from Servier, Roche, and Medsir., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: The aim of this study was to build and evaluate a prediction model which incorporates clinical parameters and radiomic features extracted from static as well as dynamic [ 18 F]FET PET for the survival stratification in patients with newly diagnosed IDH-wildtype glioblastoma., Methods: A total of 141 patients with newly diagnosed IDH-wildtype glioblastoma and dynamic [ 18 F]FET PET prior to surgical intervention were included. Patients with a survival time ≤ 12 months were classified as short-term survivors. First order, shape, and texture radiomic features were extracted from pre-treatment static (tumor-to-background ratio; TBR) and dynamic (time-to-peak; TTP) images, respectively, and randomly divided into a training (n = 99) and a testing cohort (n = 42). After feature normalization, recursive feature elimination was applied for feature selection using 5-fold cross-validation on the training cohort, and a machine learning model was constructed to compare radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were calculated to assess the predictive performance for identifying short-term survivors in both the training and testing cohort., Results: A combined clinical-radiomic model comprising six clinical parameters and six selected dynamic radiomic features achieved highest predictability of short-term survival with an AUC of 0.74 (95% confidence interval, 0.60-0.88) in the independent testing cohort., Conclusions: This study successfully built and evaluated prediction models using [ 18 F]FET PET-based radiomic features and clinical parameters for the individualized assessment of short-term survival in patients with a newly diagnosed IDH-wildtype glioblastoma. The combination of both clinical parameters and dynamic [ 18 F]FET PET-based radiomic features reached highest accuracy in identifying patients at risk. Although the achieved accuracy level remained moderate, our data shows that the integration of dynamic [ 18 F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers., (© 2022. The Author(s).)

The purpose of this study was to evaluate the possibility of extracting relevant information from radiomic features even in apparently [ 18 F]FET-negative gliomas. A total of 46 patients with a newly diagnosed, histologically verified glioma that was visually classified as [ 18 F]FET-negative were included. Tumor volumes were defined using routine T2/FLAIR MRI data and applied to extract information from dynamic [ 18 F]FET PET data, i.e., early and late tumor-to-background (TBR 5-15 , TBR 20-40 ) and time-to-peak (TTP) images. Radiomic features of healthy background were calculated from the tumor volume of interest mirrored in the contralateral hemisphere. The ability to distinguish tumors from healthy tissue was assessed using the Wilcoxon test and logistic regression. A total of 5, 15, and 69% of features derived from TBR 20-40 , TBR 5-15 , and TTP images, respectively, were significantly different. A high number of significantly different TTP features was even found in isometabolic gliomas (after exclusion of photopenic gliomas) with visually normal [ 18 F]FET uptake in static images. However, the differences did not reach satisfactory predictability for machine-learning-based identification of tumor tissue. In conclusion, radiomic features derived from dynamic [ 18 F]FET PET data may extract additional information even in [ 18 F]FET-negative gliomas, which should be investigated in larger cohorts and correlated with histological and outcome features in future studies.

Purpose: The role of temozolomide chemotherapy alone in isocitrate dehydrogenase (IDH)-mutant astrocytomas has not been conclusively determined. Radiotherapy might be superior to temozolomide. Recent studies have linked temozolomide with induction of hypermutation and poor clinical course in some IDH-mutant gliomas., Methods: In this retrospective study, 183 patients with astrocytoma, IDH-mutant, CNS WHO grade 2 or 3 and diagnosed between 2001 and 2019 were included. Patients initially monitored by wait-and-scan strategies or treated with radiotherapy or temozolomide alone were studied. Patient data were correlated with outcome. Matched pair and subgroup analyses were conducted., Results: Radiotherapy was associated with longer progression-free survival than temozolomide (6.2 vs 3.4 years, p = 0.02) and wait-and-scan strategies (6.2 vs 4 years, p = 0.03). Patients treated with radiotherapy lived longer than patients treated with temozolomide (14.4 vs 10.7 years, p = 0.02). Survival was longer in the wait-and-scan cohort than in the temozolomide cohort (not reached vs 10.7 years, p < 0.01). Patients from the wait-and-scan cohort receiving temozolomide at first progression had significantly shorter survival times than patients treated with any other therapy at first progression (p < 0.01). Post-surgical T2 tumor volume, contrast enhancement on MRI and WHO grade were associated with overall survival in univariate analyses (p < 0.01)., Conclusion: The results suggest superiority of radiotherapy over temozolomide and wait-and-scan strategies regarding progression-free survival and superiority of radiotherapy over temozolomide regarding overall survival. Our results are consistent with the notion that early temozolomide might compromise outcome in some patients., (© 2022. The Author(s).)