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

• Published in: Cancers Vol. 14; no. 11; p. 2762
• 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
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 02.06.2022; MDPI
• Subjects: 1H-MRS; 2-hydroxyglutarate; Artificial intelligence; Astrocytoma; Biopsy; Brain cancer; Brain tumors; glioma; Glioma cells; IDH mutation; Immunohistochemistry; independent validation; Isocitrate dehydrogenase; Learning algorithms; linear support vector machine; Machine learning; Magnetic resonance imaging; Magnetic resonance spectroscopy; Medical prognosis; Metabolites; Mutation; Neuropathology; Oligodendroglioma; Patients; Software; Spectrum analysis; Therapeutic applications; Therapeutic targets; Tumors; Netherlands; United Kingdom > UK; United States > US; Germany
• ISSN: 2072-6694; 2072-6694
• DOI: 10.3390/cancers14112762

The isocitrate dehydrogenase (IDH) 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 IDH 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 IDH mutation status employing proton magnetic resonance spectroscopy (1H-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 1H-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 1H-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 IDH status that is well-applicable in an everyday clinical setting.