MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification

作者：Diamandis Elie; Gabriel Carl Phillip Simon; Wuertemberger Urs; Guggenberger Konstanze; Urbach Horst; Staszewski Ori; Lassmann Silke; Schnell Oliver; Grauvogel Juergen; Mader Irina; Heiland Dieter Henrik^*

来源：Journal of Neuro-Oncology, 2018, 139(2): 431-440.

DOI：10.1007/s11060-018-2881-x

• Magnetic resonance spectroscopy
• Radiogenomics
• Glioma
• Machine-learning

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.
65 patients received anatomical and chemical shift imaging (5 x 5 x 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.
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%.
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.

• 出版日期2018-9