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Feiyue Yin

As seen in: MDPI, Academic Radiology
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Articles

NODDI-derived U-Fiber MRI Markers for Risk Stratification of Progression Independent of Relapse Activity in Relapsing-remitting Multiple Sclerosis

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By Kai Zhang, Yixian Li, Zichun Yan, Zhuowei Shi, Qiyuan Zhu, Wenjie Liu, Feiyue Yin, Xiaojuan Dong, Ruirui Yang, Lisha Nie, Yongmei Li
| Academic Radiology Verified
Key Words Relapsing-remitting multiple sclerosis Progression Independent of Relapse Activity NODDI U-fibers MRI Get full text access Log in, subscribe or purchase for full access. REFERENCES 1. Buyukturkoglu, K. ∙ Vergara, C. ∙ Fuentealba, V. ... Machine learning to investigate superficial white matter integrity in early multiple sclerosis J Neuroimaging. 2022; 32(1):36-47 2. Komnenić, D. ∙ Phillips, O.R. ∙ Joshi, S.H. ...
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Radiomics Nomograms Based on Multi-sequence MRI for Identifying Cognitive Impairment and Predicting Cognitive Progression in Relapsing-Remitting Multiple Sclerosis

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By Xiaohua Wang, Shangqing Liu, Zichun Yan, Feiyue Yin
| Academic Radiology Verified
To build radiomics nomograms based on multi-sequence MRI to facilitate the identification of cognitive impairment (CI) and prediction of cognitive progression (CP) in patients with relapsing-remitting multiple sclerosis (RRMS). We retrospectively included two RRMS cohorts with multi-sequence MRI and Symbol Digit Modalities Test (SDMT) data: dataset1 (n = 149, for training and validation) and dataset2 (n = 29, for external validation). 80 patients of dataset1 had a 2-year follow-up SDMT.
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JPM | Free Full-Text | Multimodal Investigation of Deep Gray Matter Nucleus in Patients with Multiple Sclerosis and Their Clinical Correlations: A Multivariate Pattern Analysis Study

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By Zichun Yan, Yongmei Li, Shuang Ding, Feiyue Yin
| MDPI
4. Discussion This study employed high-resolution, quantitative and multi-parametric MRI methods to investigate morphometrics, iron concentration variations and diffusion metrics of the DGM in MS patients to investigate the microstructural changes in DGM in the absence of lesions.
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