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Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis
OBJECTIVES: To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS. METHODS: We analyzed structural brain images of 163 subjects diagnosed with MS acquired at two different sites. Participants were followed up for 2–6 y...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563671/ https://www.ncbi.nlm.nih.gov/pubmed/33970338 http://dx.doi.org/10.1007/s00415-021-10605-7 |
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author | Tommasin, Silvia Cocozza, Sirio Taloni, Alessandro Giannì, Costanza Petsas, Nikolaos Pontillo, Giuseppe Petracca, Maria Ruggieri, Serena De Giglio, Laura Pozzilli, Carlo Brunetti, Arturo Pantano, Patrizia |
author_facet | Tommasin, Silvia Cocozza, Sirio Taloni, Alessandro Giannì, Costanza Petsas, Nikolaos Pontillo, Giuseppe Petracca, Maria Ruggieri, Serena De Giglio, Laura Pozzilli, Carlo Brunetti, Arturo Pantano, Patrizia |
author_sort | Tommasin, Silvia |
collection | PubMed |
description | OBJECTIVES: To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS. METHODS: We analyzed structural brain images of 163 subjects diagnosed with MS acquired at two different sites. Participants were followed up for 2–6 years, with disability progression defined according to the expanded disability status scale (EDSS) increment at follow-up. T2-weighted lesion load (T2LL), thalamic and cerebellar gray matter (GM) volumes, fractional anisotropy of the normal appearing white matter were calculated at baseline and included in supervised machine learning classifiers. Age, sex, phenotype, EDSS at baseline, therapy and time to follow-up period were also included. Classes were labeled as stable or progressed disability. Participants were randomly chosen from both sites to build a sample including 50% patients showing disability progression and 50% patients being stable. One-thousand machine learning classifiers were applied to the resulting sample, and after testing for overfitting, classifier confusion matrix, relative metrics and feature importance were evaluated. RESULTS: At follow-up, 36% of participants showed disability progression. The classifier with the highest resulting metrics had accuracy of 0.79, area under the true positive versus false positive rates curve of 0.81, sensitivity of 0.90 and specificity of 0.71. T2LL, thalamic volume, disability at baseline and administered therapy were identified as important features in predicting disability progression. Classifiers built on radiological features had higher accuracy than those built on clinical features. CONCLUSIONS: Disability progression in MS may be predicted via machine learning classifiers, mostly evaluating neuroradiological features. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00415-021-10605-7. |
format | Online Article Text |
id | pubmed-8563671 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85636712021-11-04 Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis Tommasin, Silvia Cocozza, Sirio Taloni, Alessandro Giannì, Costanza Petsas, Nikolaos Pontillo, Giuseppe Petracca, Maria Ruggieri, Serena De Giglio, Laura Pozzilli, Carlo Brunetti, Arturo Pantano, Patrizia J Neurol Original Communication OBJECTIVES: To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS. METHODS: We analyzed structural brain images of 163 subjects diagnosed with MS acquired at two different sites. Participants were followed up for 2–6 years, with disability progression defined according to the expanded disability status scale (EDSS) increment at follow-up. T2-weighted lesion load (T2LL), thalamic and cerebellar gray matter (GM) volumes, fractional anisotropy of the normal appearing white matter were calculated at baseline and included in supervised machine learning classifiers. Age, sex, phenotype, EDSS at baseline, therapy and time to follow-up period were also included. Classes were labeled as stable or progressed disability. Participants were randomly chosen from both sites to build a sample including 50% patients showing disability progression and 50% patients being stable. One-thousand machine learning classifiers were applied to the resulting sample, and after testing for overfitting, classifier confusion matrix, relative metrics and feature importance were evaluated. RESULTS: At follow-up, 36% of participants showed disability progression. The classifier with the highest resulting metrics had accuracy of 0.79, area under the true positive versus false positive rates curve of 0.81, sensitivity of 0.90 and specificity of 0.71. T2LL, thalamic volume, disability at baseline and administered therapy were identified as important features in predicting disability progression. Classifiers built on radiological features had higher accuracy than those built on clinical features. CONCLUSIONS: Disability progression in MS may be predicted via machine learning classifiers, mostly evaluating neuroradiological features. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00415-021-10605-7. Springer Berlin Heidelberg 2021-05-10 2021 /pmc/articles/PMC8563671/ /pubmed/33970338 http://dx.doi.org/10.1007/s00415-021-10605-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Communication Tommasin, Silvia Cocozza, Sirio Taloni, Alessandro Giannì, Costanza Petsas, Nikolaos Pontillo, Giuseppe Petracca, Maria Ruggieri, Serena De Giglio, Laura Pozzilli, Carlo Brunetti, Arturo Pantano, Patrizia Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis |
title | Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis |
title_full | Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis |
title_fullStr | Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis |
title_full_unstemmed | Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis |
title_short | Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis |
title_sort | machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis |
topic | Original Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563671/ https://www.ncbi.nlm.nih.gov/pubmed/33970338 http://dx.doi.org/10.1007/s00415-021-10605-7 |
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