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Automatic classification of MSA subtypes using Whole-brain gray matter function and Structure-Based radiomics

Boyu Chen¹, Jiachuan He¹, Ming Xu²

¹Department of Radiology, The First Hospital of China Medical University, Shenyang 110001, Liaoning, PR China.

European Journal of Radiology

|February 16, 2023

Summary

This study developed a radiomics method using brain imaging to accurately distinguish between multiple system atrophy subtypes, MSA-P and MSA-C. The approach shows potential for supporting clinical diagnosis of these neurological disorders.

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Area of Science:

Neuroimaging
Radiomics
Neurology

Background:

Multiple system atrophy (MSA) presents with subtypes: MSA with predominant Parkinsonism (MSA-P) and MSA with predominant cerebellar ataxia (MSA-C).
Accurate differentiation between MSA-P and MSA-C is crucial for appropriate clinical management and treatment strategies.
Current diagnostic methods can be challenging, necessitating advanced techniques for precise classification.

Purpose of the Study:

To develop and validate a radiomics-based method for classifying MSA-P and MSA-C.
To utilize whole-brain gray matter function and structure for accurate subtype differentiation.
To assess the potential of radiomics in supporting clinical diagnostic systems for MSA.

Main Methods:

Extracted 7,308 radiomic features from 3D-T1 and resting-state fMRI data, including gray matter volume (GMV), mALFF, mReHo, DC, VMHC, and RSFC.
Applied t-test and Lasso for feature selection, identifying 12 key features (1 ALFF, 1 DC, 10 RSFC).
Employed machine learning classifiers (SVM, random forest, logistic regression) and evaluated performance using ROC curves and DeLong's test.

Main Results:

The random forest model achieved high classification performance with AUC values of 0.91 (validation) and 0.80 (test).
Key features distinguishing MSA subtypes included brain functional activity and connectivity in the cerebellum, orbitofrontal lobe, and limbic system.
The selected radiomics features effectively differentiated between MSA-C and MSA-P, even with similar disease severity and duration.

Conclusions:

The developed radiomics approach demonstrates significant potential for accurate classification of MSA-P and MSA-C patients.
This method can serve as a valuable tool to augment clinical diagnostic systems for neurological disorders like MSA.
Radiomics offers a non-invasive, data-driven strategy for individual-level diagnosis of MSA subtypes.