Mapping of Metabolic Heterogeneity of Glioma Using MR-Spectroscopy

Pamela Franco,Dieter Henrik Heiland,Hansjoerg Mast,Juergen Beck,Marco Prinz,Oliver Schnell,Jonas A Hosp,Daniel Delev,Irene Huebschle,Urs Wuertemberger,Horst Urbach,Karam Dacca,Carl Philipp Simon-Gabriel,Irina Mader

doi:10.3390/cancers13102417

Abstract

Simple SummaryRadiomics 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 (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.

Highlights

With the revised WHO-Classification of Tumors of the Central Nervous System of 2016 it was made clear that the molecular biology of the tumors surpasses in importance the histological features, due to the fact that the first is crucial for diagnosis, and for prognosis in terms of response to adjuvant therapy and survival [1]
The most frequent pathology was glioma, representing 73% (n = 65) of all patients. Those with a wildtype IDH-status were more common (n = 35, 53.8%, histopathologically defined glioblastomas, WHO grade IV), followed by those with a mutated IDH-status (n = 17, 26.2%, histopathologically defined as diffuse and anaplastic astrocytoma, WHO grades II and III respectively) and lastly there were n = 13 patients with a mutated IDH-status combined with a codeletion of 1p/19q (LOH, 20%, histopathologically defined as oligodendrogliomas WHO grades II and III), Table S2
We included a broad spectrum of different pathologies including dysembrioplastic neuroepithelial tumors (DNET), ependymoma, pineal gland tumors, metastatic lesions from melanoma and nonsmall-cell lung carcinoma (NSCLC), focal cortical dysplasia (FCD) and others, Figure S1b and Table S1

Summary

Introduction

With the revised WHO-Classification of Tumors of the Central Nervous System of 2016 it was made clear that the molecular biology of the tumors surpasses in importance the histological features, due to the fact that the first is crucial for diagnosis, and for prognosis in terms of response to adjuvant therapy and survival [1]. The ultimate goal is a more personalized approach in the overall oncological treatment of patients with brain tumors, with physicians being able to better plan a surgical treatment, being biopsy or surgical resection, and the extent and complexity of it as well as preparing the patient for the adjuvant treatment afterwards. Such data can improve therapy monitoring and allows for early detection of recurrences. We present our prospective imaging trial and the correlation between the metabolic and molecular profiles of different brain pathologies across varied ages, which we used for characterization and training of an automated tool

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