18 F-FDOPA PET for the Noninvasive Prediction of Glioma Molecular Parameters: A Radiomics Study.

Autor:	Zaragori T; Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France.; IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France., Oster J; IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France., Roch V; Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France., Hossu G; IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France.; CIC 1433 Innovation Technologique, INSERM, CHRU-Nancy, Université de Lorraine, Nancy, France., Chawki MB; Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France., Grignon R; Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France., Pouget C; Department of Pathology, CHRU-Nancy, Université de Lorraine, Nancy, France.; INSERM U1256, Université de Lorraine, Nancy, France., Gauchotte G; Department of Pathology, CHRU-Nancy, Université de Lorraine, Nancy, France.; INSERM U1256, Université de Lorraine, Nancy, France., Rech F; Department of Neurosurgery, CHRU-Nancy, Université de Lorraine, Nancy, France.; Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France; and., Blonski M; Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France; and.; Department of Neuro-Oncology, CHRU-Nancy, Université de Lorraine, Nancy, France., Taillandier L; Centre de Recherche en Automatique de Nancy CRAN UMR 7039, CNRS, Université de Lorraine, Nancy, France; and.; Department of Neuro-Oncology, CHRU-Nancy, Université de Lorraine, Nancy, France., Imbert L; Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France.; IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France., Verger A; Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU-Nancy, Université de Lorraine, Nancy, France; a.verger@chru-nancy.fr.; IADI UMR 1254, INSERM, Université de Lorraine, Nancy, France.
Jazyk:	angličtina
Zdroj:	Journal of nuclear medicine : official publication, Society of Nuclear Medicine [J Nucl Med] 2022 Jan; Vol. 63 (1), pp. 147-157. Date of Electronic Publication: 2021 May 20.
DOI:	10.2967/jnumed.120.261545
Abstrakt:	The assessment of gliomas by 18 F-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 18 F-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 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 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: 18 F-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. (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu