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

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
December 2019
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Abstract

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 42 patients with suspected primary brain tumor without prior surgical or systemic treatment or biopsy underwent an 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%.

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Johannes Haubold
Johannes Haubold
Medical Lead

My research interests include virtual sequencing, non-invasive tumor decoding and clinical AI integration.

Felix Nensa
Felix Nensa
Lead

My research interests include medical digitalization, computer vision and radiology.