Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1 H-Magnetic Resonance Spectroscopy and Machine Learning

The ( ) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for t...

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Published in:Cancers 2022-06, Vol.14 (11)
Main Authors: Bumes, Elisabeth, Fellner, Claudia, Fellner, Franz A, Fleischanderl, Karin, Häckl, Martina, Lenz, Stefan, Linker, Ralf, Mirus, Tim, Oefner, Peter J, Paar, Christian, Proescholdt, Martin Andreas, Riemenschneider, Markus J, Rosengarth, Katharina, Weis, Serge, Wendl, Christina, Wimmer, Sibylle, Hau, Peter, Gronwald, Wolfram, Hutterer, Markus
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container_issue 11
container_start_page
container_title Cancers
container_volume 14
creator Bumes, Elisabeth
Fellner, Claudia
Fellner, Franz A
Fleischanderl, Karin
Häckl, Martina
Lenz, Stefan
Linker, Ralf
Mirus, Tim
Oefner, Peter J
Paar, Christian
Proescholdt, Martin Andreas
Riemenschneider, Markus J
Rosengarth, Katharina
Weis, Serge
Wendl, Christina
Wimmer, Sibylle
Hau, Peter
Gronwald, Wolfram
Hutterer, Markus
description The ( ) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of mutation status employing proton magnetic resonance spectroscopy ( H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2-95.1%) and a specificity of 72.7% (95% CI, 57.2-85.0%) could be achieved. We concluded that our H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining status that is well-applicable in an everyday clinical setting.
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title Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1 H-Magnetic Resonance Spectroscopy and Machine Learning
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