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Feature selection from magnetic resonance imaging data in ALS: a systematic review
BACKGROUND: With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521429/ https://www.ncbi.nlm.nih.gov/pubmed/34729157 http://dx.doi.org/10.1177/20406223211051002 |
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author | Kocar, Thomas D. Müller, Hans-Peter Ludolph, Albert C. Kassubek, Jan |
author_facet | Kocar, Thomas D. Müller, Hans-Peter Ludolph, Albert C. Kassubek, Jan |
author_sort | Kocar, Thomas D. |
collection | PubMed |
description | BACKGROUND: With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection. METHODS: We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported. RESULTS: Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS. CONCLUSIONS: These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS. |
format | Online Article Text |
id | pubmed-8521429 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-85214292021-11-01 Feature selection from magnetic resonance imaging data in ALS: a systematic review Kocar, Thomas D. Müller, Hans-Peter Ludolph, Albert C. Kassubek, Jan Ther Adv Chronic Dis Review BACKGROUND: With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection. METHODS: We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported. RESULTS: Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS. CONCLUSIONS: These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS. SAGE Publications 2021-10-13 /pmc/articles/PMC8521429/ /pubmed/34729157 http://dx.doi.org/10.1177/20406223211051002 Text en © The Author(s), 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Review Kocar, Thomas D. Müller, Hans-Peter Ludolph, Albert C. Kassubek, Jan Feature selection from magnetic resonance imaging data in ALS: a systematic review |
title | Feature selection from magnetic resonance imaging data in ALS: a systematic review |
title_full | Feature selection from magnetic resonance imaging data in ALS: a systematic review |
title_fullStr | Feature selection from magnetic resonance imaging data in ALS: a systematic review |
title_full_unstemmed | Feature selection from magnetic resonance imaging data in ALS: a systematic review |
title_short | Feature selection from magnetic resonance imaging data in ALS: a systematic review |
title_sort | feature selection from magnetic resonance imaging data in als: a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521429/ https://www.ncbi.nlm.nih.gov/pubmed/34729157 http://dx.doi.org/10.1177/20406223211051002 |
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