Is It Possible to Discriminate Active MS Lesions with Diffusion Weighted Imaging?

Investigating the feasibility of differentiating MS active lesions from inactive ones using texture analysis and machine learning methods in DWI images

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.

Diffusion-Weighted Images and Contrast-Enhanced MRI in the Diagnosis of Different Stages of Multiple Sclerosis of the Central Nervous System

Introduction Multiple sclerosis (MS) is one of the most prevalent disorders of the central nervous system (CNS), and it can be observed in the field of radiological cross-sectional magnetic resonance imaging (MRI). The prevalence of MS in Saudi Arabia has increased as compared to the past few years. MRI is the gold standard non-invasive modality of choice in MS diagnosis according to the National Multiple Sclerosis Society (NMSS), New York City. This study aimed to highlight the significance of using diffusion-weighted images (DWIs) and the use of contrast media in the MS protocol, as well as the importance of identifying the suitable time of imaging after contrast enhancement to detect active lesions. Methods A retrospective cross-sectional study was conducted of 100 MS patients with an age range of 17 to 56 years. The data set included 41 active cases and 59 inactive cases. All patients had an MRI standard protocol of both the brain and spine in addition to DWI sequence and contrast agent (CA) injection, with images taken in early and delayed time. Results Of the patients, 71% were female and 29% were male. Active MS disease was more significant at younger ages than at older ages. Active lesions were significantly enhanced in delayed contrast images and showed high signal intensity in both the DWI and apparent diffusion coefficient (ADC) map, while inactive lesions showed no enhancement after contrast injection and showed an iso-signal intensity in both the DWI and ADC map. Conclusion The use of CA has developed over the years in the diagnosis of MS patients. In this study, the relationship between active lesions, DWI, and delayed contrast enhancement is very strong. In future research, we recommend adding a DWI sequence for the suspected active MS spine lesions in addition to delayed enhancement time in active MS after contrast injection to increase MRI sensitivity toward active MS lesions of the brain and spinal cord as well.

What Else Can We Use in The Discrimination of Activated MS Plaques in Addition to Diffusion MRI?

Acute respiratory distress syndrome is characterized by dyspnea at presentation, tachypnea on physical examination, findings of bilateral infiltration in chest radiography, refractory hypoxia, and high mortality. Although the main treatment approach is to address the underlying disease, there are also pharmacological and nonpharmacological options for supportive treatment. There is currently no pharmacological agent with proven efficacy in this syndrome, and many drugs are being studied for this purpose. One of these is the endothelin receptor antagonist bosentan.