No abstract

Purpose:The assessment of gliomas by 18 F-FDOPA PET imaging in adjunct to MRI showed high performance by combining static and dynamic features to non-invasively predict the isocitrate dehydrogenase (IDH) mutations and the 1p/19q co-deletion, which the World Health Organization classified as significant parameters in 2016. The current study evaluates whether other 18 F-FDOPA PET radiomics features further improve performance and the contributions of each of these features to performance.Methods: Our study included seventy-two, retrospectively selected, newly diagnosed, glioma patients with 18 F-FDOPA PET dynamic acquisitions. A set of 114 features, including conventional static features and dynamic features as well as other radiomics features were extracted and machine-learning models trained to predict IDH mutations and the 1p/19q co-deletion. Models were based on a machine-learning algorithm built from stable, relevant, and uncorrelated features selected by hierarchical clustering followed by a bootstrapped feature selection process. Models were assessed by comparing area under the curve (AUC) using a nested cross-validation approach.Feature importance was assessed using SHapley Additive exPlanations (SHAP) values. Results:The best models were able to predict IDH mutations (logistic regression with L2 regularization) and the 1p/19q co-deletion (support vector machine with radial basis function kernel) with an AUC of 0.831[0.790;0.873] and 0.724[0.669;0.782] respectively. For the prediction of IDH mutations, dynamic features were the most important features in the model (TTP: 35.5%).In contrast, other radiomics features were the most useful for predicting the 1p/19q co-deletion (up to 14.5% of importance for the small zone low grey level emphasis).4 Conclusions: 18 F-FDOPA PET is an effective tool for the non-invasive prediction of glioma molecular parameters using a full set of amino-acid PET radiomics features. The contribution of each feature set shows the importance of systematically integrating dynamic acquisition for the prediction of the IDH mutations as well as developing the use of radiomics features in routine practice for the prediction the 1p/19q co-deletion.

Background: Static [ 18 F]-F-DOPA PET images are currently used for identifying patients with glioma recurrence/ progression after treatment, although the additional diagnostic value of dynamic parameters remains unknown in this setting. The aim of this study was to evaluate the performances of static and dynamic [ 18 F]-F-DOPA PET parameters for detecting patients with glioma recurrence/progression as well as assess further relationships with patient outcome. Methods: Fifty-one consecutive patients who underwent an [ 18 F]-F-DOPA PET for a suspected glioma recurrence/ progression at post-resection MRI, were retrospectively included. Static parameters, including mean and maximum tumor-to-normal-brain (TBR) ratios, tumor-to-striatum (TSR) ratios, and metabolic tumor volume (MTV), as well as dynamic parameters with time-to-peak (TTP) values and curve slope, were tested for predicting the following: (1) glioma recurrence/progression at 6 months after the PET exam and (2) survival on longer follow-up. Results: All static parameters were significant predictors of glioma recurrence/progression (accuracy ≥ 94%) with all parameters also associated with mean progression-free survival (PFS) in the overall population (all p < 0.001, 29.7 vs. 0.4 months for TBR max , TSR max , and MTV). The curve slope was the sole dynamic PET predictor of glioma recurrence/ progression (accuracy = 76.5%) and was also associated with mean PFS (p < 0.001, 18.0 vs. 0.4 months). However, no additional information was provided relative to static parameters in multivariate analysis. Conclusion: Although patients with glioma recurrence/progression can be detected by both static and dynamic [ 18 F]-F-DOPA PET parameters, most of this diagnostic information can be achieved by conventional static parameters.