Mapping of Metabolic Heterogeneity of Glioma Using MR-Spectroscopy

Simple Summary Radiomics is a research field that integrates radiological and genetic information, but the application of the techniques that have been developed to this purpose have not been widely established in daily clinical practice. The purpose of our study is the development of a straightforward tool that can easily be used to preoperatively predict and correlate the metabolic signature of different CNS-lesions. Particularly in gliomas, we hope to integrate the molecular profile of these tumors into our prediction model. Our goal is to deliver an open-software tool with the intention of advancing the diagnostic work-up of gliomas to the latest standards. Proton magnetic resonance spectroscopy (H-1-MRS) delivers information about the non-invasive metabolic landscape of brain pathologies. H-1-MRS is used in clinical setting in addition to MRI for diagnostic, prognostic and treatment response assessments, but the use of this radiological tool is not entirely widespread. The importance of developing automated analysis tools for H-1-MRS lies in the possibility of a straightforward application and simplified interpretation of metabolic and genetic data that allow for incorporation into the daily practice of a broad audience. Here, we report a prospective clinical imaging trial (DRKS00019855) which aimed to develop a novel MR-spectroscopy-based algorithm for in-depth characterization of brain lesions and prediction of molecular traits. Dimensional reduction of metabolic profiles demonstrated distinct patterns throughout pathologies. We combined a deep autoencoder and multi-layer linear discriminant models for voxel-wise prediction of the molecular profile based on MRS imaging. Molecular subtypes were predicted by an overall accuracy of 91.2% using a classifier score. Our study indicates a first step into combining the metabolic and molecular traits of lesions for advancing the pre-operative diagnostic workup of brain tumors and improve personalized tumor treatment.

Automated summary

Radiomics is a research field that aims to integrate radiological and genetic information, with the goal of developing a simple tool for preoperatively predicting and correlating metabolic signatures of CNS lesions, particularly in gliomas. Proton magnetic resonance spectroscopy (H-1-MRS) provides non-invasive metabolic information about brain pathologies, but its use is not widespread. Developing automated analysis tools for H-1-MRS could simplify interpretation of metabolic and genetic data for a broader audience in clinical practice.