Abstract
Background
The purpose of this study is to map spatial metabolite differences across three molecular subgroups of glial tumors, defined by the IDH1/2 mutation and 1p19q-co-deletion, using magnetic resonance spectroscopy. This work reports a new MR spectroscopy based classification algorithm by applying a radiomics analytics pipeline.
Materials
65 patients received anatomical and chemical shift imaging (5 × 5 × 20 mm voxel size). Tumor regions were segmented and registered to corresponding spectroscopic voxels. Spectroscopic features were computed (n = 860) in a radiomic approach and selected by a classification algorithm. Finally, a random forest machine-learning model was trained to predict the molecular subtypes.
Results
A cluster analysis identified three robust spectroscopic clusters based on the mean silhouette widths. Molecular subgroups were significantly associated with the computed spectroscopic clusters (Fisher’s Exact test p < 0.01). A machine-learning model was trained and validated by public available MRS data (n = 19). The analysis showed an accuracy rate in the Random Forest model by 93.8%.
Conclusions
MR spectroscopy is a robust tool for predicting the molecular subtype in gliomas and adds important diagnostic information to the preoperative diagnostic work-up of glial tumor patients. MR-spectroscopy could improve radiological diagnostics in the future and potentially influence clinical and surgical decisions to improve individual tumor treatment.
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DHH is funded by the German Cancer Society (Seeding Grand TII) and by the Müller-Fahnenberg-Stiftung, Freiburg, Germany.
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Diamandis, E., Gabriel, C.P.S., Würtemberger, U. et al. MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification. J Neurooncol 139, 431–440 (2018). https://doi.org/10.1007/s11060-018-2881-x
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DOI: https://doi.org/10.1007/s11060-018-2881-x