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Investigating the feasibility of differentiating MS active lesions from inactive ones using texture analysis and
Farshad Shekari1, Alireza Vard2, Iman Adibi3
1Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran; Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
Background:
Magnetic resonance imaging (MRI) is commonly used in conjunction with a gadolinium-based contrast agent (GBCA) to distinguish active multiple sclerosis (MS) lesions. However, recent studies have raised concerns regarding the long-term effects of the accumulation of GBCA in the body. Thus, the purpose of this study is to investigate the possibility of using texture analysis in diffusion-weighted imaging (DWI) and machine learning algorithms to discriminate active from inactive MS lesions without the use of GBCA.
Methods:
To achieve this purpose, we introduce an image processing pipeline. In the proposed pipeline, following registration and alignment of slices, MS lesions from DWI images are segmented and quantized. Next, different texture analysis methods are employed to extract texture features from the lesions. Then, a two-stage feature reduction method is applied, in which the first stage involves a statistical t-test and the second stage relies on principal component analysis (PCA), sequential forward selection (SFS), sequential backward selection (SBS), and ReliefF algorithms. Finally, we use five classifiers logistic regression (LR), support vector machine (SVM), decision tree (DT), K nearest neighbor (KNN), and linear discriminant analysis (LDA) in a 5-fold cross-validation procedure to determine active and inactive MS lesions.
Results:
In this study, we collected and prepared 255 active/inactive MS lesions from MRI scans of 34 patients diagnosed with MS, with a mean age of 35.56±10.89. Among 89 texture features extracted, 63 features showed statistically significant differences between the means of active and inactive lesions (P<0.05). The SVM classifier with the PCA feature reduction algorithm demonstrated the best performance with an average accuracy of 0.960 (±0.024), specificity and precision of 1.0, sensitivity of 0.913 (±0.053), and AUC of 0.957 (±0.027).
Conclusion:
Our study indicates that DWI changes detected using texture analysis-based machine learning models can precisely differentiate active from inactive MS lesions. This finding provides valuable clinical information for the early diagnosis and effective monitoring of MS disease.
Insights
Texture analysis of diffusion-weighted imaging (DWI) with machine learning accurately differentiates active from inactive multiple sclerosis (MS) lesions, avoiding gadolinium-based contrast agents (GBCAs). This offers a safer, effective method for MS diagnosis and monitoring.
Area of Science:
- Radiology
- Neuroimaging
- Machine Learning
Background:
- Magnetic resonance imaging (MRI) with gadolinium-based contrast agents (GBCAs) is standard for active multiple sclerosis (MS) lesion detection.
- Concerns exist regarding long-term GBCA accumulation in the body.
- Alternative methods for MS lesion characterization are needed.
Purpose of the Study:
- To investigate texture analysis in diffusion-weighted imaging (DWI) combined with machine learning.
- To discriminate active from inactive MS lesions without using GBCAs.
Main Methods:
- Developed an image processing pipeline for DWI lesion segmentation and quantization.
- Extracted texture features and applied a two-stage feature reduction (t-test, PCA, SFS, SBS, ReliefF).
- Utilized five classifiers (LR, SVM, DT, KNN, LDA) with 5-fold cross-validation.
Main Results:
- Analyzed 255 MS lesions from 34 patients.
- Identified 63 statistically significant texture features (P<0.05) differentiating active/inactive lesions.
- Achieved highest performance with SVM and PCA: 0.960 accuracy, 1.0 specificity/precision, 0.913 sensitivity, 0.957 AUC.
Conclusions:
- DWI texture analysis and machine learning models accurately differentiate active from inactive MS lesions.
- This approach provides valuable clinical data for early MS diagnosis and monitoring.
- Offers a GBCA-free method for MS lesion characterization.
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