Diamandis E, Gabriel CPS, Würtemberger U, Guggenberger K, Urbach H, Staszewski O, Lassmann S, Schnell O, Grauvogel J, Mader I, Heiland DH (2018)
Publication Type: Journal article
Publication year: 2018
Book Volume: 139
Pages Range: 431-440
Journal Issue: 2
DOI: 10.1007/s11060-018-2881-x
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.
APA:
Diamandis, E., Gabriel, C.P.S., Würtemberger, U., Guggenberger, K., Urbach, H., Staszewski, O.,... Heiland, D.H. (2018). MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification. Journal of Neuro-Oncology, 139(2), 431-440. https://doi.org/10.1007/s11060-018-2881-x
MLA:
Diamandis, Elie, et al. "MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification." Journal of Neuro-Oncology 139.2 (2018): 431-440.
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