TY - JOUR T1 - <sup>18</sup>F-FDOPA PET for the Noninvasive Prediction of Glioma Molecular Parameters: A Radiomics Study JF - Journal of Nuclear Medicine JO - J Nucl Med SP - 147 LP - 157 DO - 10.2967/jnumed.120.261545 VL - 63 IS - 1 AU - Timothée Zaragori AU - Julien Oster AU - Véronique Roch AU - Gabriela Hossu AU - Mohammad B. 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 - 2022/01/01 UR - http://jnm.snmjournals.org/content/63/1/147.abstract N2 - The assessment of gliomas by 18F-FDOPA PET imaging as an adjunct to MRI showed high performance by combining static and dynamic features to noninvasively predict the isocitrate dehydrogenase (IDH) mutations and the 1p/19q codeletion, which the World Health Organization classified as significant parameters in 2016. The current study evaluated whether other 18F-FDOPA PET radiomics features further improve performance and the contributions of each of these features to performance. Methods: Our study included 72 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 codeletion. Models were based on a machine-learning algorithm built from stable, relevant, and uncorrelated features selected by hierarchic clustering followed by a bootstrapped feature selection process. Models were assessed by comparing area under the curve using a nested cross-validation approach. Feature importance was assessed using Shapley additive explanations values. Results: The best models were able to predict IDH mutations (logistic regression with L2 regularization) and the 1p/19q codeletion (support vector machine with radial basis function kernel) with an area under the curve of 0.831 (95% CI, 0.790–0.873) and 0.724 (95% CI, 0.669–0.782), respectively. For the prediction of IDH mutations, dynamic features were the most important features in the model (time to peak, 35.5%). In contrast, other radiomics features were the most useful for predicting the 1p/19q codeletion (up to 14.5% of importance for the small-zone low-gray-level emphasis). Conclusion: 18F-FDOPA PET is an effective tool for the noninvasive 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 prediction of the IDH mutations as well as developing the use of radiomics features in routine practice for prediction of the 1p/19q codeletion. ER -