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

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.

Keywords: Glioma; Machine-learning; Magnetic resonance spectroscopy; Radiogenomics.