Microstructural alterations in different types of lesions and their perilesional white matter in relapsing-remitting multiple sclerosis based on diffusion kurtosis imaging

Shi, Zhuowei; Yang, Ping; Zhang, Yan; Ding, Shuang; Hu, Hai; Wei, Yiqiu; Luo, Dan; Xu, Yuhui; Zhu, Qiyuan; Li, Yongmei

doi:10.1016/j.msard.2023.104572

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…QSM was reconstructed by sepia (SEPIA version 0.8.1.1) on the MATLAB platform (2018a, Mathworks), as previously described ( 17 ).…”

Section: Methodsmentioning

confidence: 99%

Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis

Shi,

Ma,

Ding

et al. 2024

Quant Imaging Med Surg

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Background White matter (WM) lesions can be classified into contrast enhancement lesions (CELs), iron rim lesions (IRLs), and non-iron rim lesions (NIRLs) based on different pathological mechanism in relapsing-remitting multiple sclerosis (RRMS). The application of radiomics established by T2-FLAIR to classify WM lesions in RRMS is limited, especially for 3-class classification among CELs, IRLs, and NIRLs. Methods A total of 875 WM lesions (92 CELs, 367 IRLs, 416 NIRLs) were included in this study. The 2-class classification was only performed between IRLs and NIRLs. For the 2- and 3-class classification tasks, all the lesions were randomly divided into training and testing sets with a ratio of 8:2. We used least absolute shrinkage and selection operator (LASSO), reliefF algorithm, and mutual information (MI) for feature selection, then eXtreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) were used to establish discrimination models. Finally, the area under the curve (AUC), accuracy, sensitivity, specificity, and precision were used to evaluate the performance of the models. Results For the 2-class classification model, LASSO classifier with RF model showed the best discrimination performance with the AUC of 0.893 (95% CI: 0.838–0.942), accuracy of 0.813, sensitivity of 0.833, specificity of 0.781, and precision of 0.851. However, the 3-class classification model of LASSO with XGBoost displayed the highest performance with the AUC of 0.920 (95% CI: 0.887–0.950), accuracy of 0.796, sensitivity of 0.839, specificity of 0.881, and precision of 0.846. Conclusions Radiomics models based on T2-FLAIR images have the potential for discriminating among CELs, IRLs, and NIRLs in RRMS.

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“…QSM was reconstructed by sepia (SEPIA version 0.8.1.1) on the MATLAB platform (2018a, Mathworks), as previously described ( 17 ).…”

Section: Methodsmentioning

confidence: 99%

Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis

Shi,

Ma,

Ding

et al. 2024

Quant Imaging Med Surg

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Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis

Ravano,

Andelova,

Piredda

et al. 2024

J Neurol

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Background and objectives In multiple sclerosis (MS), slowly expanding lesions were shown to be associated with worse disability and prognosis. Their timely detection from cross-sectional data at early disease stages could be clinically relevant to inform treatment planning. Here, we propose to use multiparametric, quantitative MRI to allow a better cross-sectional characterization of lesions with different longitudinal phenotypes. Methods We analysed T1 and T2 relaxometry maps from a longitudinal cohort of MS patients. Lesions were classified as enlarging, shrinking, new or stable based on their longitudinal volumetric change using a newly developed automated technique. Voxelwise deviations were computed as z-scores by comparing individual patient data to T1, T2 and T2/T1 normative values from healthy subjects. We studied the distribution of microstructural properties inside lesions and within perilesional tissue. Results and conclusions Stable lesions exhibited the highest T1 and T2 z-scores in lesion tissue, while the lowest values were observed for new lesions. Shrinking lesions presented the highest T1 z-scores in the first perilesional ring while enlarging lesions showed the highest T2 z-scores in the same region. Finally, a classification model was trained to predict the longitudinal lesion type based on microstructural metrics and feature importance was assessed. Z-scores estimated in lesion and perilesional tissue from T1, T2 and T2/T1 quantitative maps carry discriminative and complementary information to classify longitudinal lesion phenotypes, hence suggesting that multiparametric MRI approaches are essential for a better understanding of the pathophysiological mechanisms underlying disease activity in MS lesions.

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The changes of oxygen extraction fraction in different types of lesions in relapsing–remitting multiple sclerosis: A cross-sectional and longitudinal study

Xie,

Zhang,

et al. 2024

Neurol Sci

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Microstructural alterations in different types of lesions and their perilesional white matter in relapsing-remitting multiple sclerosis based on diffusion kurtosis imaging

Cited by 5 publications

References 36 publications

Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis

Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis

Microstructural characterization of multiple sclerosis lesion phenotypes using multiparametric longitudinal analysis

The changes of oxygen extraction fraction in different types of lesions in relapsing–remitting multiple sclerosis: A cross-sectional and longitudinal study

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