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PET/MRI Radiomics in Patients With Brain Metastases

Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data...

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Autores principales: Lohmann, Philipp, Kocher, Martin, Ruge, Maximillian I., Visser-Vandewalle, Veerle, Shah, N. Jon, Fink, Gereon R., Langen, Karl-Josef, Galldiks, Norbert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020230/
https://www.ncbi.nlm.nih.gov/pubmed/32116995
http://dx.doi.org/10.3389/fneur.2020.00001
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author Lohmann, Philipp
Kocher, Martin
Ruge, Maximillian I.
Visser-Vandewalle, Veerle
Shah, N. Jon
Fink, Gereon R.
Langen, Karl-Josef
Galldiks, Norbert
author_facet Lohmann, Philipp
Kocher, Martin
Ruge, Maximillian I.
Visser-Vandewalle, Veerle
Shah, N. Jon
Fink, Gereon R.
Langen, Karl-Josef
Galldiks, Norbert
author_sort Lohmann, Philipp
collection PubMed
description Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data. The growing availability of high-performance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Using machine learning techniques and advanced statistical methods, subsets of such imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. The identification of appropriate, characteristic image features as well as the generation of predictive or prognostic mathematical models is summarized under the term radiomics. This review summarizes the current status of radiomics in patients with brain metastases.
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spelling pubmed-70202302020-02-28 PET/MRI Radiomics in Patients With Brain Metastases Lohmann, Philipp Kocher, Martin Ruge, Maximillian I. Visser-Vandewalle, Veerle Shah, N. Jon Fink, Gereon R. Langen, Karl-Josef Galldiks, Norbert Front Neurol Neurology Although a variety of imaging modalities are used or currently being investigated for patients with brain tumors including brain metastases, clinical image interpretation to date uses only a fraction of the underlying complex, high-dimensional digital information from routinely acquired imaging data. The growing availability of high-performance computing allows the extraction of quantitative imaging features from medical images that are usually beyond human perception. Using machine learning techniques and advanced statistical methods, subsets of such imaging features are used to generate mathematical models that represent characteristic signatures related to the underlying tumor biology and might be helpful for the assessment of prognosis or treatment response, or the identification of molecular markers. The identification of appropriate, characteristic image features as well as the generation of predictive or prognostic mathematical models is summarized under the term radiomics. This review summarizes the current status of radiomics in patients with brain metastases. Frontiers Media S.A. 2020-02-07 /pmc/articles/PMC7020230/ /pubmed/32116995 http://dx.doi.org/10.3389/fneur.2020.00001 Text en Copyright © 2020 Lohmann, Kocher, Ruge, Visser-Vandewalle, Shah, Fink, Langen and Galldiks. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Lohmann, Philipp
Kocher, Martin
Ruge, Maximillian I.
Visser-Vandewalle, Veerle
Shah, N. Jon
Fink, Gereon R.
Langen, Karl-Josef
Galldiks, Norbert
PET/MRI Radiomics in Patients With Brain Metastases
title PET/MRI Radiomics in Patients With Brain Metastases
title_full PET/MRI Radiomics in Patients With Brain Metastases
title_fullStr PET/MRI Radiomics in Patients With Brain Metastases
title_full_unstemmed PET/MRI Radiomics in Patients With Brain Metastases
title_short PET/MRI Radiomics in Patients With Brain Metastases
title_sort pet/mri radiomics in patients with brain metastases
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020230/
https://www.ncbi.nlm.nih.gov/pubmed/32116995
http://dx.doi.org/10.3389/fneur.2020.00001
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