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SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial
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 orde...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685595/ https://www.ncbi.nlm.nih.gov/pubmed/33228567 http://dx.doi.org/10.1186/s12880-020-00522-y |
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author | Franco, Pamela Würtemberger, Urs Dacca, Karam Hübschle, Irene Beck, Jürgen Schnell, Oliver Mader, Irina Binder, Harald Urbach, Horst Heiland, Dieter Henrik |
author_facet | Franco, Pamela Würtemberger, Urs Dacca, Karam Hübschle, Irene Beck, Jürgen Schnell, Oliver Mader, Irina Binder, Harald Urbach, Horst Heiland, Dieter Henrik |
author_sort | Franco, Pamela |
collection | PubMed |
description | 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 mm(3) 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. |
format | Online Article Text |
id | pubmed-7685595 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76855952020-11-25 SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial Franco, Pamela Würtemberger, Urs Dacca, Karam Hübschle, Irene Beck, Jürgen Schnell, Oliver Mader, Irina Binder, Harald Urbach, Horst Heiland, Dieter Henrik BMC Med Imaging Study Protocol 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 mm(3) 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. BioMed Central 2020-11-23 /pmc/articles/PMC7685595/ /pubmed/33228567 http://dx.doi.org/10.1186/s12880-020-00522-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Study Protocol Franco, Pamela Würtemberger, Urs Dacca, Karam Hübschle, Irene Beck, Jürgen Schnell, Oliver Mader, Irina Binder, Harald Urbach, Horst Heiland, Dieter Henrik SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial |
title | SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial |
title_full | SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial |
title_fullStr | SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial |
title_full_unstemmed | SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial |
title_short | SPectroscOpic prediction of bRain Tumours (SPORT): study protocol of a prospective imaging trial |
title_sort | spectroscopic prediction of brain tumours (sport): study protocol of a prospective imaging trial |
topic | Study Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685595/ https://www.ncbi.nlm.nih.gov/pubmed/33228567 http://dx.doi.org/10.1186/s12880-020-00522-y |
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