Predicting IDH genotype in gliomas using FET PET radiomics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Philipp Lohmann, Christoph Lerche, Elena K. Bauer, Jan Steger, Gabriele Stoffels, Tobias Blau, Veronika Dunkl, Martin Kocher, Shivakumar Viswanathan, Christian P. Filss, Carina Stegmayr, Maximillian I. Ruge, Bernd Neumaier, Nadim J. Shah, Gereon R. Fink, Karl-Josef Langen, Norbert Galldiks

ABSTRACT

Mutations in the isocitrate dehydrogenase (IDH mut) gene have gained paramount importance for the prognosis of glioma patients. To date, reliable techniques for a preoperative evaluation of IDH genotype remain scarce. Therefore, we investigated the potential of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET radiomics using textural features combined with static and dynamic parameters of FET uptake for noninvasive prediction of IDH genotype. Prior to surgery, 84 patients with newly diagnosed and untreated gliomas underwent FET PET using a standard scanner (15 of 56 patients with IDH mut) or a dedicated high-resolution hybrid PET/MR scanner (11 of 28 patients with IDH mut). Static, dynamic and textural parameters of FET uptake in the tumor area were evaluated. Diagnostic accuracy of the parameters was evaluated using the neuropathological result as reference. Additionally, FET PET and textural parameters were combined to further increase the diagnostic accuracy. The resulting models were validated using cross-validation. Independent of scanner type, the combination of standard PET parameters with textural features increased significantly diagnostic accuracy. The highest diagnostic accuracy of 93% for prediction of IDH genotype was achieved with the hybrid PET/MR scanner. Our findings suggest that the combination of conventional FET PET parameters with textural features provides important diagnostic information for the non-invasive prediction of the IDH genotype. More... »

PAGES

13328

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-31806-7

DOI

http://dx.doi.org/10.1038/s41598-018-31806-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106476163

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30190592


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