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Purpose: Glioma patients, especially recurrent glioma, suffer from a poor prognosis. While advances to classify glioma on a molecular level improved prognostication at initial diagnosis, markers to prognosticate survival in the recurrent situation are still needed. As 18 kDa translocator protein (TSPO) was previously reported to be associated with aggressive histopathological glioma features, we correlated the TSPO positron emission tomography (PET) signal using [18F]GE180 in a large cohort of recurrent glioma patients with their clinical outcome. Methods: In patients with [18F]GE180 PET at glioma recurrence, [18F]GE180 PET parameters (e.g., SUVmax) as well as other imaging features (e.g., MRI volume, [18F]FET PET parameters when available) were evaluated together with patient characteristics (age, sex, Karnofsky-Performance score) and neuropathological features (e.g. WHO 2021 grade, IDH-mutation status). Uni- and multivariate Cox regression and Kaplan–Meier survival analyses were performed to identify prognostic factors for post-recurrence survival (PRS) and time to treatment failure (TTF). Results: Eighty-eight consecutive patients were evaluated. TSPO tracer uptake correlated with tumor grade at recurrence (p < 0.05), with no significant differences in IDH-wild-type versus IDH-mutant tumors. Within the subgroup of IDH-mutant glioma (n = 46), patients with low SUVmax (median split, ≤ 1.60) had a significantly longer PRS (median 41.6 vs. 25.3 months, p = 0.031) and TTF (32.2 vs 8.7 months, p = 0.001). Also among IDH-wild-type glioblastoma (n = 42), patients with low SUVmax (≤ 1.89) had a significantly longer PRS (median not reached vs 8.2 months, p = 0.002). SUVmax remained an independent prognostic factor for PRS in the multivariate analysis including CNS WHO 2021 grade, IDH status, and age. Tumor volume defined by [18F]FET PET or contrast-enhanced MRI correlated weakly with TSPO tracer uptake. Treatment regimen did not differ among the median split subgroups. Conclusion: Our data suggest that TSPO PET using [18F]GE180 can help to prognosticate recurrent glioma patients even among homogeneous molecular subgroups and may therefore serve as valuable non-invasive biomarker for individualized patient management. [ABSTRACT FROM AUTHOR]

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 [18F]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 [18F]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 [18F]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 [18F]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 [18F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers. [ABSTRACT FROM AUTHOR]