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. 2021 May 17;13(10):2417.
doi: 10.3390/cancers13102417.

Mapping of Metabolic Heterogeneity of Glioma Using MR-Spectroscopy

Pamela Franco  1   2 , Irene Huebschle  1   2 , Carl Philipp Simon-Gabriel  2   3 , Karam Dacca  1   2 , Oliver Schnell  1   2 , Juergen Beck  1   2 , Hansjoerg Mast  4 , Horst Urbach  2   4 , Urs Wuertemberger  2   4 , Marco Prinz  2   5   6   7 , Jonas A Hosp  2   8 , Daniel Delev  9 , Irina Mader  2   4   10 , Dieter Henrik Heiland  1   2   11
Affiliations

Mapping of Metabolic Heterogeneity of Glioma Using MR-Spectroscopy

Pamela Franco et al. Cancers (Basel). .

Abstract

Proton magnetic resonance spectroscopy (1H-MRS) delivers information about the non-invasive metabolic landscape of brain pathologies. 1H-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 1H-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.

Keywords: 1H-MRS; MR spectroscopy; MRS; chemical shift imaging; glioma; magnetic resonance spectroscopy; neurooncology; neuroradiology; neurosurgery; radiomics.

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Conflict of interest statement

No potential conflicts of interest were disclosed by the authors.

Figures

Figure 1
Figure 1
(a) Workflow for post-hoc data analysis. After segmentation and baseline correction, we performed a deep autoencoder for data denoising of fitted metabolites. (b) Characterization: the analysis was split into a first part in which data were deeply characterized including dimensional reduction and clustering, and a second part where a novel prediction model was established to allow precise prediction of the molecular subgroups through MRS.
Figure 2
Figure 2
(a) 2D representation of a UMAP dimensional reduction. Colors illustrate the SNN clusters (1–10). (b) Bar plot indicates the distribution of segmented voxel within the cluster analysis. (c) Dot plot of significantly enriched metabolites of each cluster. Colors indicate the global intensity of each metabolite. (d) 2D representation of a UMAP dimensional reduction. Colors illustrate the metabolite intensity and marker metabolites of each cluster.
Figure 3
Figure 3
(a) 2D representation of a UMAP dimensional reduction. Colors illustrate the regions, red: normal-appearing matter (NAM), yellow: FLAIR-hyperintensity, brown: tumor, and purple: ventricle. FLAIR-hyperintensity regions are partly assigned to the ventricle regions (which contained a ow signal) and the tumor clusters, illustrated by an arrow. (b) On the left: 2D representation of a UMAP dimensional reduction. Colors illustrate marker metabolites myo-inositol (Ins), highly enriched in an edema region and a macro-molecule band (09), highly enriched within tumor-infiltrative regions compared to edema-related FLAR hyperintensity. On the right: Representative spectra of the FLAIR-hyperintensity edema region (top spectra) and FLAIR-hyperintensity tumor infiltration-related voxels (bottom spectra).
Figure 4
Figure 4
(a) Workflow of our prediction model. (b) 3D map of group prediction using an LDA. Colors indicate the subgroups: purple: normal-appearing matter, orange: pathological lesion (non-tumor pathology), blue: glioma and green: metastasis. (c,d) Representative presentations of the AUC from our prediction model. (e) Prediction maps of two patients, a metastasis (left) and an IDH-wildtype glioblastoma (right). Each voxel is colored based on its probability to contain voxels of the predicted entity. Yellow voxels represent low prediction scores, red-purple voxels represent higher prediction scores. The color scheme is plasma.
Figure 5
Figure 5
(a) 2D representation of a UMAP dimensional reduction of pathological confirmed lesions, representative spectra are shown. (b) Dot plot of significantly enriched metabolites for each representative brain disease type. (c) 2D representation of a UMAP dimensional reduction. Colors illustrate metabolite intensity.
Figure 6
Figure 6
(a) 2D representation of an LDA map of gliomas. (b–d) AUC representations of the predictive value of each glioma subtype. (e,f) Example SPORT prediction model of glioma subtypes in one patient. Each voxel is colored based on its probability to contain voxels of the predicted entity. Yellow voxels represent low prediction scores, red-purple voxels represent higher prediction scores. The color scheme is plasma. (g) Representative SPORT classifier by summarizing all tumor voxels from level 1 prediction (layer 1) into a distribution of probabilities. The presented boxplot indicates the mean prediction score of each tumor voxel (from layer 1). Here, the patient’s classifier showed the highest score for the IDH-mutated subgroup.
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