Abstract

BackgroundThe revised 2016 WHO-Classification of CNS-tumours now integrates molecular information of glial brain tumours for accurate diagnosis as well as for the development of targeted therapies. In this prospective study, our aim is to investigate the predictive value of MR-spectroscopy in order to establish a solid preoperative molecular stratification algorithm of these tumours. We will process a 1H MR-spectroscopy sequence within a radiomics analytics pipeline.MethodsPatients treated at our institution with WHO-Grade II, III and IV gliomas will receive preoperative anatomical (T2- and T1-weighted imaging with and without contrast enhancement) and proton MR spectroscopy (MRS) by using chemical shift imaging (MRS) (5 × 5 × 15 mm3 voxel size). Tumour regions will be segmented and co-registered to corresponding spectroscopic voxels.Raw signals will be processed by a deep-learning approach for identifying patterns in metabolic data that provides information with respect to the histological diagnosis as well patient characteristics obtained and genomic data such as target sequencing and transcriptional data.DiscussionBy imaging the metabolic profile of a glioma using a customized chemical shift 1H MR spectroscopy sequence and by processing the metabolic profiles with a machine learning tool we intend to non-invasively uncover the genetic signature of gliomas. This work-up will support surgical and oncological decisions to improve personalized tumour treatment.Trial registrationThis study was initially registered under another name and was later retrospectively registered under the current name at the German Clinical Trials Register (DRKS) under DRKS00019855.

Highlights

  • The revised 2016 World Health Organisation (WHO)-Classification of CNS-tumours integrates molecular information of glial brain tumours for accurate diagnosis as well as for the development of targeted therapies

  • Gliomas are a very heterogenic group of tumours arising from glial cells in the central nervous system that are yet to be completely understood

  • MR spectroscopy is used for research purposes only, or is regarded as a somewhat outed method with little diagnostic significance in addition to MRI [5,6,7]

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Summary

Introduction

The revised 2016 WHO-Classification of CNS-tumours integrates molecular information of glial brain tumours for accurate diagnosis as well as for the development of targeted therapies. In this prospective study, our aim is to investigate the predictive value of MR-spectroscopy in order to establish a solid preoperative molecular stratification algorithm of these tumours. The growing interest in developing tools to aid in the diagnosis and classification of brain tumours in a non-invasive and more efficient way. MR spectroscopy has long been a neuroradiological tool used to determine the metabolic alterations in different central nervous system pathologies [2,3,4]. MR spectroscopy can deliver information beyond the metabolic profile of a lesion [5, 8]

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