Highlights
- •Radiomic features based on MRI-FLAIR sequences are effective in predicting MOGAD in ADEM-like presentation patients after subgrouping using age and gender.
- •Inflammatory lesions tend to have a smoother texture and a longer shape(coronal axis) in MOGAD with ADEM-like presentation patients.
- •Support vector machine (SVM) and multilayer perceptron (MLP) models have better performance in this study.
Abstract
Objective
The differences in magnetic resonance imaging (MRI) between children with classic
acute disseminated encephalomyelitis (ADEM) and myelinal oligodendrocyte glycoprotein
antibody associated disease (MOGAD) with ADEM-like presentation are controversial.
The purpose of this study was to investigate whether the radiological characteristics
of the MRI-FLAIR sequence can predict MOGAD in children with ADEM-like presentation
and to further explore its imaging differences.
Methods
We extracted 1041 radiomics features from MRI-FLAIR lesions. Then we used the redundancy
analysis (Spearman correlation coefficient), significance test (student test or Mann-Whitney
U test), least absolute contraction and selection operator (LASSO) to select potential
predictors from the feature groups. The selected potential predictors and MOG antibody
test results were used to fit the machine learning model for classification. Combined
with feature selection and machine learning classifiers, the optimal model for each
subgroup was derived. The resulting models have been evaluated using the receiver
operator characteristic curve (ROC) at the lesion level and the model performance
was evaluated at the case level using decision curve analysis.
Results
We retrospectively reviewed and re-diagnosed 70 ADEM-like presentation cases in our
center from April 2015 to January 2020. Including 49 cases with classic ADEM and 21
cases with MOGAD. 30(43%) were female, with a median age of 5.3 years. On the four
subgroups by age and gender, the area under the curve (AUC) of the optimal models
were 89%, 90%, 98%, and 99%, and the MOGAD detection rates (Specificity) were 83%,
83%, 92%, and 75%, respectively.
Conclusions
The machine learning model trained on radiomics features of MR-FLAIR images can effectively
predict patients' MOGAD. This study provides a fast, objective, and quantifiable method
for MOGAD diagnosis.
Keywords
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Article info
Publication history
Published online: December 31, 2022
Accepted:
December 30,
2022
Received in revised form:
December 26,
2022
Received:
September 29,
2022
Identification
Copyright
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