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 yea...
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| Published in: | Journal of neurology Vol. 268; no. 12; pp. 4834 - 4845 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2021
Springer Nature B.V |
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| ISSN: | 0340-5354, 1432-1459, 1432-1459 |
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| Abstract | 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. |
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| AbstractList | To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS.
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.
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.
Disability progression in MS may be predicted via machine learning classifiers, mostly evaluating neuroradiological features. To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS.OBJECTIVESTo evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS.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.METHODSWe 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.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.RESULTSAt 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.Disability progression in MS may be predicted via machine learning classifiers, mostly evaluating neuroradiological features.CONCLUSIONSDisability progression in MS may be predicted via machine learning classifiers, mostly evaluating neuroradiological features. 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. ObjectivesTo evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS.MethodsWe 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.ResultsAt 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.ConclusionsDisability progression in MS may be predicted via machine learning classifiers, mostly evaluating neuroradiological features. |
| Author | Pozzilli, Carlo Pantano, Patrizia Ruggieri, Serena Cocozza, Sirio Giannì, Costanza Pontillo, Giuseppe Petracca, Maria De Giglio, Laura Petsas, Nikolaos Taloni, Alessandro Tommasin, Silvia Brunetti, Arturo |
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| Keywords | Multiple sclerosis Disability progression Magnetic resonance imaging Machine learning |
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| Snippet | Objectives
To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS.
Methods
We... To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS. We analyzed structural... ObjectivesTo evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS.MethodsWe... To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS.OBJECTIVESTo evaluate... |
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| SubjectTerms | Accuracy Anisotropy Brain - diagnostic imaging Cerebellum Disability Evaluation Disease Progression Gray Matter - diagnostic imaging Humans Learning algorithms Machine Learning Magnetic Resonance Imaging Medicine Medicine & Public Health Multiple sclerosis Multiple Sclerosis - diagnostic imaging Neurology Neuroradiology Neurosciences Original Communication Patients Phenotypes Substantia alba Substantia grisea Thalamus |
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| Title | Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis |
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