Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning.
<h4>Objectives</h4>A novel characterization of Clinically Isolated Syndrome (CIS) patients according to lesion patterns is proposed. More specifically, patients are classified according to the nature of inflammatory lesions patterns. It is expected that this characterization can infer ne...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0093024&type=printable |
| _version_ | 1826799187858030592 |
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| author | Alessandro Crimi Olivier Commowick Adil Maarouf Jean-Christophe Ferré Elise Bannier Ayman Tourbah Isabelle Berry Jean-Philippe Ranjeva Gilles Edan Christian Barillot |
| author_facet | Alessandro Crimi Olivier Commowick Adil Maarouf Jean-Christophe Ferré Elise Bannier Ayman Tourbah Isabelle Berry Jean-Philippe Ranjeva Gilles Edan Christian Barillot |
| author_sort | Alessandro Crimi |
| collection | DOAJ |
| description | <h4>Objectives</h4>A novel characterization of Clinically Isolated Syndrome (CIS) patients according to lesion patterns is proposed. More specifically, patients are classified according to the nature of inflammatory lesions patterns. It is expected that this characterization can infer new prospective figures from the earliest imaging signs of Multiple Sclerosis (MS), since it can provide a classification of different types of lesions across patients.<h4>Methods</h4>The method is based on a two-tiered classification. Initially, the spatio-temporal lesion patterns are classified. The discovered lesion patterns are then used to characterize groups of patients. The patient groups are validated using statistical measures and by correlations at 24-month follow-up with hypointense lesion loads.<h4>Results</h4>The methodology identified 3 statistically significantly different clusters of lesion patterns showing p-values smaller than 0.01. Moreover, these patterns defined at baseline correlated with chronic hypointense lesion volumes by follow-up with an R(2) score of 0.90.<h4>Conclusions</h4>The proposed methodology is capable of identifying three major different lesion patterns that are heterogeneously present in patients, allowing a patient classification using only two MRI scans. This finding may lead to more accurate prognosis and thus to more suitable treatments at early stage of MS. |
| first_indexed | 2024-12-22T22:20:08Z |
| format | Article |
| id | doaj.art-147bda123f8f490693590fbeea6b9082 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2025-03-17T00:31:06Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-147bda123f8f490693590fbeea6b90822025-02-21T05:35:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0194e9302410.1371/journal.pone.0093024Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning.Alessandro CrimiOlivier CommowickAdil MaaroufJean-Christophe FerréElise BannierAyman TourbahIsabelle BerryJean-Philippe RanjevaGilles EdanChristian Barillot<h4>Objectives</h4>A novel characterization of Clinically Isolated Syndrome (CIS) patients according to lesion patterns is proposed. More specifically, patients are classified according to the nature of inflammatory lesions patterns. It is expected that this characterization can infer new prospective figures from the earliest imaging signs of Multiple Sclerosis (MS), since it can provide a classification of different types of lesions across patients.<h4>Methods</h4>The method is based on a two-tiered classification. Initially, the spatio-temporal lesion patterns are classified. The discovered lesion patterns are then used to characterize groups of patients. The patient groups are validated using statistical measures and by correlations at 24-month follow-up with hypointense lesion loads.<h4>Results</h4>The methodology identified 3 statistically significantly different clusters of lesion patterns showing p-values smaller than 0.01. Moreover, these patterns defined at baseline correlated with chronic hypointense lesion volumes by follow-up with an R(2) score of 0.90.<h4>Conclusions</h4>The proposed methodology is capable of identifying three major different lesion patterns that are heterogeneously present in patients, allowing a patient classification using only two MRI scans. This finding may lead to more accurate prognosis and thus to more suitable treatments at early stage of MS.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0093024&type=printable |
| spellingShingle | Alessandro Crimi Olivier Commowick Adil Maarouf Jean-Christophe Ferré Elise Bannier Ayman Tourbah Isabelle Berry Jean-Philippe Ranjeva Gilles Edan Christian Barillot Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning. PLoS ONE |
| title | Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning. |
| title_full | Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning. |
| title_fullStr | Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning. |
| title_full_unstemmed | Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning. |
| title_short | Predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium- and USPIO-enhanced MRI and machine learning. |
| title_sort | predictive value of imaging markers at multiple sclerosis disease onset based on gadolinium and uspio enhanced mri and machine learning |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0093024&type=printable |
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