作者
Johannes Haubold, Aydin Demircioglu, Marcel Gratz, Martin Glas, Karsten Wrede, Ulrich Sure, Gerald Antoch, Kathy Keyvani, Mathias Nittka, Stephan Kannengiesser, Vikas Gulani, Mark Griswold, Ken Herrmann, Michael Forsting, Felix Nensa, Lale Umutlu
发表日期
2020/6
期刊
European Journal of Nuclear Medicine and Molecular Imaging
卷号
47
页码范围
1435-1445
出版商
Springer Berlin Heidelberg
简介
Objectives
The introduction of the 2016 WHO classification of CNS tumors has made the combined molecular and histopathological characterization of tumors a pivotal part of glioma patient management. Recent publications on radiogenomics-based prediction of the mutational status have demonstrated the predictive potential of imaging-based, non-invasive tissue characterization algorithms. Hence, the aim of this study was to assess the potential of multiparametric 18F-FET PET-MRI including MR fingerprinting accelerated with machine learning and radiomic algorithms to predict tumor grading and mutational status of patients with cerebral gliomas.
Materials and methods
42 patients with suspected primary brain tumor without prior surgical or systemic treatment or biopsy underwent an 18F-FET PET-MRI examination. To differentiate the mutational status and the WHO grade of the cerebral tumors, support …
学术搜索中的文章
J Haubold, A Demircioglu, M Gratz, M Glas, K Wrede… - European Journal of Nuclear Medicine and Molecular …, 2020