Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18 F-FET PET-MRI and MR Fingerprinting

• Published in: European journal of nuclear medicine and molecular imaging Vol. 47; no. 6; p. 1435
• Main Authors: Haubold, Johannes, Demircioglu, Aydin, Gratz, Marcel, Glas, Martin, Wrede, Karsten, Sure, Ulrich, Antoch, Gerald, Keyvani, Kathy, Nittka, Mathias, Kannengiesser, Stephan, Gulani, Vikas, Griswold, Mark, Herrmann, Ken, Forsting, Michael, Nensa, Felix, Umutlu, Lale
• Format: Journal Article
• Language: English
• Published: Germany 01.06.2020
• Subjects: Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Glioma - diagnostic imaging; Glioma - genetics; Humans; Magnetic Resonance Imaging; Positron-Emission Tomography; Tyrosine; PET-MRI; Glioma; FET PET; IDH1; ATRX; MR fingerprinting; 1p19q; Radiomics
• ISSN: 1619-7089

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 F-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. 42 patients with suspected primary brain tumor without prior surgical or systemic treatment or biopsy underwent an F-FET PET-MRI examination. To differentiate the mutational status and the WHO grade of the cerebral tumors, support vector machine and random forest were trained with the radiomics signature of the multiparametric PET-MRI data including MR fingerprinting. Surgical sampling served as a gold standard for histopathological reference and assessment of mutational status. The 5-fold cross-validated area under the curve in predicting the ATRX mutation was 85.1%, MGMT mutation was 75.7%, IDH1 was 88.7%, and 1p19q was 97.8%. The area under the curve of differentiating low-grade glioma vs. high-grade glioma was 85.2%. F-FET PET-MRI and MR fingerprinting enable high-quality imaging-based tumor decoding and phenotyping for differentiation of low-grade vs. high-grade gliomas and for prediction of the mutational status of ATRX, IDH1, and 1p19q. These initial results underline the potential of F-FET PET-MRI to serve as an alternative to invasive tissue characterization.