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Updated: Jul 12, 2025
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients
Hassan Tavakoli1,2,3, Gila Pirzad Jahromi1, Abdolrasoul Sedaghat4
1Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Background:
Multiple sclerosis (MS) is the most common non-traumatic disabling disease.
Objective:
The aim of this study is to investigate the ability of radiomics features for diagnosing active plaques in patients with MS from T2 Fluid Attenuated Inversion Recovery (FLAIR) images.
Material And Methods:
In this experimental study, images of 82 patients with 122 MS lesions were investigated. Boruta and Relief algorithms were used for feature selection on the train data set (70%). Four different classifier algorithms, including Multi-Layer Perceptron (MLP), Gradient Boosting (GB), Decision Tree (DT), and Extreme Gradient Boosting (XGB) were used as classifiers for modeling. Finally, Performance metrics were obtained on the test data set (30%) with 1000 bootstrap and 95% confidence intervals (95% CIs).
Results:
A total of 107 radiomics features were extracted for each lesion, of which 7 and 8 features were selected by the Relief method and Boruta method, respectively. DT classifier had the best performance in the two feature selection algorithms. The best performance on the test data set was related to Boruta-DT with an average accuracy of 0.86, sensitivity of 1.00, specificity of 0.84, and Area Under the Curve (AUC) of 0.92 (95% CI: 0.92-0.92).
Conclusion:
Radiomics features have the potential for diagnosing MS active plaque by T2 FLAIR image features. Additionally, choosing the feature selection and classifier algorithms plays an important role in the diagnosis of active plaque in MS patients. The radiomics-based predictive models predict active lesions accurately and non-invasively.
Insights
Radiomics features show promise for diagnosing multiple sclerosis (MS) active plaques using T2 FLAIR images. Feature selection and classifier choice significantly impact diagnostic accuracy for MS patients.
Area of Science:
- Medical Imaging
- Radiology
- Artificial Intelligence in Medicine
Background:
- Multiple sclerosis (MS) is a leading non-traumatic cause of disability.
- Accurate diagnosis of active MS lesions is crucial for effective treatment.
Purpose of the Study:
- To evaluate the efficacy of radiomics features for diagnosing active MS plaques.
- To assess the performance of different feature selection and classification algorithms.
Main Methods:
- Extracted 107 radiomics features from T2 FLAIR images of 82 MS patients.
- Employed Boruta and Relief algorithms for feature selection.
- Utilized Decision Tree (DT), MLP, GB, and XGB classifiers for modeling.
Main Results:
- Boruta and Relief methods selected 8 and 7 features, respectively.
- The Boruta-DT model achieved the highest performance: 0.86 accuracy, 1.00 sensitivity, 0.84 specificity, and 0.92 AUC.
- The chosen feature selection and classification algorithms significantly influenced diagnostic outcomes.
Conclusions:
- Radiomics features extracted from T2 FLAIR images can effectively aid in diagnosing MS active plaques.
- The selection of appropriate feature selection and classification methods is critical for accurate MS plaque diagnosis.
- Radiomics-based models offer a non-invasive approach for predicting active MS lesions.
