TY - JOUR T1 - <sup>18</sup>F-FDOPA PET for the non-invasive prediction of glioma molecular parameters: a radiomics study JF - Journal of Nuclear Medicine JO - J Nucl Med DO - 10.2967/jnumed.120.261545 SP - jnumed.120.261545 AU - Timothée Zaragori AU - Julien Oster AU - Veronique Roch AU - Gabriela Hossu AU - Mohammad Bilal Chawki AU - Rachel Grignon AU - Celso Pouget AU - Guillaume Gauchotte AU - Fabien Rech AU - Marie Blonski AU - Luc Taillandier AU - Laëtitia Imbert AU - Antoine Verger Y1 - 2021/05/01 UR - http://jnm.snmjournals.org/content/early/2021/05/20/jnumed.120.261545.abstract N2 - Purpose: The assessment of gliomas by 18F-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 18F-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 18F-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) . Conclusion: 18F-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. ER -