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The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

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Published on: June 30, 2014

Multivariate prediction of multiple sclerosis using robust quantitative MR-based image metrics.

Heiko Neeb¹, Jochen Schenk²

¹Multimodal Imaging Physics Group, University of Applied Sciences Koblenz, RheinAhrCampus Remagen, 53424 Remagen, Germany; Institute for Medical Engineering and Information Processing - MTI Mittelrhein, University of Koblenz, 56070 Koblenz, Germany.

Zeitschrift Fur Medizinische Physik

|November 17, 2018

Summary

Machine learning models accurately predict multiple sclerosis (MS) using quantitative MRI data, even with motion artifacts. Multivariate analysis outperforms univariate methods, demonstrating the potential for automated MS diagnosis from degraded imaging.

Keywords:

Machine learning Multiple sclerosis Myelin imaging Quantitative MRI

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Published on: December 26, 2015

Area of Science:

Neuroimaging
Machine Learning
Medical Diagnostics

Background:

Multiple Sclerosis (MS) diagnosis relies on clinical and imaging data.
Quantitative Magnetic Resonance Imaging (qMRI) offers detailed tissue characterization.
Motion artifacts are a common challenge in clinical MRI, potentially impacting diagnostic accuracy.

Purpose of the Study:

To evaluate multivariate supervised machine learning models for predicting MS presence/absence using qMRI features.
To assess model performance on MRI data with varying degrees of motion-induced artifacts.
To compare multivariate analysis with univariate analysis for MS prediction.

Main Methods:

52 MS patients and 45 healthy controls underwent 3T MRI scans.
Quantitative parameters (T1, T2*, total water content, myelin water content) were extracted from grey and white matter.
Multivariate models were trained and cross-validated for MS prediction.
Univariate analysis used optimized cut-offs for individual parameters.

Main Results:

Cross-validated multivariate models achieved 83.7% correct classification on artifact-free data.
Performance decreased to 74.5% with significant motion artifacts.
T1 in grey matter and myelin water content in white matter were key discriminating features in multivariate analysis.
Univariate analysis yielded up to 77% correct classification for specific parameters.

Conclusions:

Quantitative MRI measures enable automated MS prediction with good specificity.
Multivariate models effectively classify MS even with motion-degraded datasets, especially when combining grey and white matter features.
Multivariate analysis demonstrates superior performance over univariate analysis for quantitative MR data in MS prediction.