Top

Published in:

Open Access 01-12-2020 | Glioma | Study protocol

SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial

Authors: Pamela Franco, Urs Würtemberger, Karam Dacca, Irene Hübschle, Jürgen Beck, Oliver Schnell, Irina Mader, Harald Binder, Horst Urbach, Dieter Henrik Heiland

Published in: BMC Medical Imaging | Issue 1/2020

Abstract

Background

The 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.

Methods

Patients 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.

Discussion

By 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 registration

This 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.

Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131:803–20. https://doi.org/10.1007/s00401-016-1545-1.CrossRefPubMed

Oz G, Alger JR, Barker PB, Bartha R, Bizzi A, Boesch C, et al. Clinical proton MR spectroscopy in central nervous system disorders. Radiology. 2014;270:658–79. https://doi.org/10.1148/radiol.13130531.CrossRefPubMed

Tognarelli JM, Dawood M, Shariff MIF, Grover VPB, Crossey MME, Cox IJ, et al. Magnetic resonance spectroscopy: principles and techniques: lessons for clinicians. J Clin Exp Hepatol. 2015;5:320–8. https://doi.org/10.1016/j.jceh.2015.10.006.CrossRefPubMedPubMedCentral

Berrington A, Schreck KC, Barron BJ, Blair L, Lin DDM, Hartman AL, et al. Cerebral ketones detected by 3T MR spectroscopy in patients with high-grade glioma on an atkins-based diet. AJNR Am J Neuroradiol. 2019;40:1908–15. https://doi.org/10.3174/ajnr.A6287.CrossRefPubMedPubMedCentral

Lin A, Ross BD, Harris K, Wong W. Efficacy of proton magnetic resonance spectroscopy in neurological diagnosis and neurotherapeutic decision making. NeuroRx. 2005;2:197–214. https://doi.org/10.1602/neurorx.2.2.197.CrossRefPubMedPubMedCentral

Hellström J, Romanos Zapata R, Libard S, Wikström J, Ortiz-Nieto F, Alafuzoff I, et al. The value of magnetic resonance spectroscopy as a supplement to MRI of the brain in a clinical setting. PLoS ONE. 2018;13:e0207336. https://doi.org/10.1371/journal.pone.0207336.CrossRefPubMedPubMedCentral

Verma A, Kumar I, Verma N, Aggarwal P, Ojha R. Magnetic resonance spectroscopy - revisiting the biochemical and molecular milieu of brain tumors. BBA Clin. 2016;5:170–8. https://doi.org/10.1016/j.bbacli.2016.04.002.CrossRefPubMedPubMedCentral

Bulik M, Jancalek R, Vanicek J, Skoch A, Mechl M. Potential of MR spectroscopy for assessment of glioma grading. Clin Neurol Neurosurg. 2013;115:146–53. https://doi.org/10.1016/j.clineuro.2012.11.002.CrossRefPubMed

Bertholdo D, Watcharakorn A, Castillo M. Brain proton magnetic resonance spectroscopy: introduction and overview. Neuroimaging Clin N Am. 2013;23:359–80. https://doi.org/10.1016/j.nic.2012.10.002.CrossRefPubMed

10.

Majós C, Aguilera C, Alonso J, Julià-Sapé M, Castañer S, Sánchez JJ, et al. Proton MR spectroscopy improves discrimination between tumor and pseudotumoral lesion in solid brain masses. AJNR Am J Neuroradiol. 2009;30:544–51. https://doi.org/10.3174/ajnr.A1392.CrossRefPubMedPubMedCentral

11.

Horská A, Barker PB. Imaging of brain tumors: MR spectroscopy and metabolic imaging. Neuroimaging Clin N Am. 2010;20:293–310. https://doi.org/10.1016/j.nic.2010.04.003.CrossRefPubMedPubMedCentral

12.

Mader I, Roser W, Hagberg G, Schneider M, Sauter R, Seelig J, et al. Proton chemical shift imaging, metabolic maps, and single voxel spectroscopy of glial brain tumors. MAGMA. 1996;4:139–50.CrossRefPubMed

13.

Durmo F, Rydelius A, Cuellar Baena S, Askaner K, Lätt J, Bengzon J, et al. Multivoxel 1H-MR spectroscopy biometrics for preoprerative differentiation between brain tumors. Tomography. 2018;4:172–81. https://doi.org/10.18383/j.tom.2018.00051.CrossRefPubMedPubMedCentral

14.

Choi C, Ganji SK, DeBerardinis RJ, Hatanpaa KJ, Rakheja D, Kovacs Z, et al. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat Med. 2012;18:624–9. https://doi.org/10.1038/nm.2682.CrossRefPubMedPubMedCentral

15.

Di Ieva A, Magnussen JS, McIntosh J, Mulcahy MJ, Pardey M, Choi C. Magnetic resonance spectroscopic assessment of isocitrate dehydrogenase status in gliomas: the new frontiers of spectrobiopsy in neurodiagnostics. World Neurosurg. 2020;133:e421–7. https://doi.org/10.1016/j.wneu.2019.09.040.CrossRefPubMed

16.

Kauppinen RA, Peet AC. Using magnetic resonance imaging and spectroscopy in cancer diagnostics and monitoring: preclinical and clinical approaches. Cancer Biol Ther. 2011;12:665–79. https://doi.org/10.4161/cbt.12.8.18137.CrossRefPubMedPubMedCentral

17.

Cianfoni A, Law M, Re TJ, Dubowitz DJ, Rumboldt Z, Imbesi SG. Clinical pitfalls related to short and long echo times in cerebral MR spectroscopy. J Neuroradiol. 2011;38:69–75. https://doi.org/10.1016/j.neurad.2010.10.001.CrossRefPubMed

18.

Hagberg G, Burlina AP, Mader I, Roser W, Radue EW, Seelig J. In vivo proton MR spectroscopy of human gliomas: definition of metabolic coordinates for multi-dimensional classification. Magn Reson Med. 1995;34:242–52. https://doi.org/10.1002/mrm.1910340216.CrossRefPubMed

19.

Heiland DH, Mader I, Schlosser P, Pfeifer D, Carro MS, Lange T, et al. Integrative network-based analysis of magnetic resonance spectroscopy and genome wide expression in glioblastoma multiforme. Sci Rep. 2016;6:29052. https://doi.org/10.1038/srep29052.CrossRefPubMedPubMedCentral

20.

Demerath T, Simon-Gabriel CP, Kellner E, Schwarzwald R, Lange T, Heiland DH, et al. Mesoscopic imaging of glioblastomas: are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J. 2017;30:36–47. https://doi.org/10.1177/1971400916678225.CrossRefPubMed

21.

Diamandis E, Gabriel CPS, Würtemberger U, Guggenberger K, Urbach H, Staszewski O, et al. MR-spectroscopic imaging of glial tumors in the spotlight of the 2016 WHO classification. J Neurooncol. 2018;139:431–40. https://doi.org/10.1007/s11060-018-2881-x.CrossRefPubMed

22.

Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106. https://doi.org/10.1186/gb-2010-11-10-r106.CrossRefPubMedPubMedCentral

23.

Hess M, Lenz S, Blaette T, Bullinger L, Binder H. Partitioned learning of deep Boltzmann machines for SNP data. BioRxiv. 2016. https://doi.org/10.1101/095638.CrossRef

24.

Neftel C, Laffy J, Filbin MG, Hara T, Shore ME, Rahme GJ, et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell. 2019;178(835–849):e21. https://doi.org/10.1016/j.cell.2019.06.024.CrossRef

Title: SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial
Authors: Pamela Franco
Urs Würtemberger
Karam Dacca
Irene Hübschle
Jürgen Beck
Oliver Schnell
Irina Mader
Harald Binder
Horst Urbach
Dieter Henrik Heiland
Publication date: 01-12-2020
Publisher: BioMed Central
Keywords: Glioma
Glioma
Glioma
Published in: BMC Medical Imaging / Issue 1/2020
Electronic ISSN: 1471-2342
DOI: https://doi.org/10.1186/s12880-020-00522-y

At a glance: The STEP trials

Springer Medicine

SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial

Abstract

Background

Methods

Discussion

Trial registration

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Discussion

Trial registration

Please log in to get access to this content

Other articles of this Issue 1/2020

Comparison of 18F-sodium fluoride PET/CT, 18F-fluorocholine PET/CT and diffusion-weighted MRI for the detection of bone metastases in recurrent prostate cancer: a cost-effectiveness analysis in France

Advanced hepatic vasculobiliary imaging segmentation and 3D reconstruction as an aid in the surgical management of high biliary stenosis

Radiation exposure of a mobile 3D C-arm with large flat-panel detector for intraoperative imaging and navigation - an experimental study using an anthropomorphic Alderson phantom

Discriminative pattern of reduced cerebral blood flow in Parkinson’s disease and Parkinsonism-Plus syndrome: an ASL-MRI study

Feasibility of pulmonary MRI for nodule detection in comparison to computed tomography

Validation of PET/MRI attenuation correction methodology in the study of brain tumours