Lin-Feng Yan
Fourth Military Medical University
74 Papers
110 Citations
Lin-Feng Yan is an academic researcher from Fourth Military Medical University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 14, co-authored 57 publications.
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Papers
Patent
Device for preventing harm of formaldehyde to teaching staff during anatomy
Yu Jun,Quanrui Ma,Baoying Chen,Fang Wei,Haikang Zhao,Ping Qu,Gang Li,Lin-Feng Yan,Haiyan Nan,Haifeng Zhang,Xuece Zhang +10 more
- 28 Nov 2012
TL;DR: In this paper, the authors describe a device for preventing harm of formaldehyde to teaching staff during anatomy, which consists of a box body with five open surfaces, wherein a formalin solution is accommodated in the box body; and an anatomy table for holding a body is arranged above the liquid surface of the solution.
Patent
Adaptive breast magnetic resonance imaging coil
Cui Guangbin,Lin-Feng Yan,Wen Wang +2 more
- 11 Feb 2015
TL;DR: In this paper, an adaptive breast magnetic resonance imaging coil is characterized by at least comprising a breast bracket, where the breast bracket is of a cavity structure and the lower end of the cavity is provided with a left coil and a right coil.
Intravoxel incoherent motion diffusion-weighted MR imaging of gliomas: efficacy in preoperative grading.
Yu-Chuan Hu,Lin-Feng Yan,Lang Wu,Pang Du,Baoying Chen,Liang Wang,Shu-Mei Wang,Yu Han,Qiang Tian,Ying Yu,Tian-Yong Xu,Wen Wang,Guangbin Cui +12 more
TL;DR: The IVIM DWI demonstrates efficacy in differentiating the low- from high-grade gliomas in newly diagnosed glioma patients, as well as the sensitivity and specificity for grading.
The brain structure and function abnormalities of migraineurs: A systematic review and neuroimaging meta-analysis
Zhuanghong Chen,Yu-Ling Cui,Jing-Ting Sun,Yu-Ting Li,Chi Zhang,Yan Zhang,Zehua Li,Yu-Xuan Shang,Min-Hua Ni,Bo Hu,Lin-Feng Yan,Wen Wang +11 more
TL;DR: In this paper , an activation likelihood estimation (ALE) was performed to assess the differentiation of functional connectivity (FC), regional homogeneity (ReHo), and gray matter volume (GMV) of migraine patients.
Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T 1 -weighted Contrast-enhanced Imaging
Ying-Zhi Sun,Lin-Feng Yan,Yu Han,Hai-Yan Nan,Gang Xiao,Qiang Tian,Wen-Hui Pu,Ze-Yang Li,Xiao-Cheng Wei,Wen Wang,Guangbin Cui +10 more
TL;DR: In this paper, the authors evaluated the diagnostic performance of machine learning using radiomics model from T1-weighted contrast enhanced imaging (T1CE) in differentiating pseudoprogression from true progression after standard treatment for GBM.