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

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.