8 Papers
2 Citations
Yan Su is an academic researcher from Fujian Medical University. The author has contributed to research in topics: Medicine & Diffusion MRI. The author has an hindex of 1, co-authored 2 publications.
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Papers
Perinatal risk factors for neonatal early-onset sepsis: a meta-analysis of observational studies.
Liyan Guo,Wenxiao Han,Yan Su,Na Wang,Xinqing Chen,Jinjin Ma,Jiao-Jiao Liang,Ling Hao,Changjun Ren +8 more
TL;DR: Perinatal asphyxia or intrauterine distress, meconium contamination in amniotic fluid, GBS colonization in pregnant women, chorioamnionitis, premature rupture of membranes, lower gestational age, maternal urinary tract or reproductive tract infection, perinatal fever, very low birth weight, and vaginal examinations ≥3 times may increase the risk of EONS.
10
Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading
TL;DR: The whole-tumor histogram analysis of DWI and DSC-PWI is a promising method for grading pediatric gliomas.
5
Differentiation between supratentorial pilocytic astrocytoma and extraventricular ependymoma using multiparametric MRI.
TL;DR: The differentiation of supratentorial pilocytic astrocytomas and extraventricular ependymomas (STEEs) is clinically pivotal because of distinct therapeutic manageme... as discussed by the authors.
3
A multimodal domain adaptive segmentation framework for IDH genotype prediction
Hai-Tao Zeng,Zhen Xing,Fenglian Gao,Zhigang Wu,Wan-Wen Huang,Yan Su,Zhong Chen,Shuhui Cai,Dairong Cao,Congbo Cai +9 more
TL;DR: Experimental results demonstrate that the domain adaptive approach outperforms the methods utilizing direct transfer learning on the public BraTS 2019 dataset and 110 astrocytoma cases of grade II–IV brain tumors from the authors' hospital.
2
Predicting 1p/19q codeletion status using diffusion-, susceptibility-, perfusion-weighted, and conventional MRI in IDH-mutant lower-grade gliomas.
TL;DR: 1p/19q codeletion status of IDH-mutant LGGs can be stratified using cMRI and advanced MRI techniques, including DWI, SWI, and DSC-PWI and a combination of cMRI, rADC, ITSSs, and rCBVmax may improve the diagnostic performance for predicting 1p/ 19q code letion status.