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Ying-Zhi Sun

Fourth Military Medical University

11

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266

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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.

2021 BMC Medical Imaging
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Classification of Prostate Transitional Zone Cancer and Hyperplasia Using Deep Transfer Learning From Disease-Related Images

2021 Cureus
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Amide Proton Transfer Imaging in Predicting Isocitrate Dehydrogenase 1 Mutation Status of Grade II/III Gliomas Based on Support Vector Machine

2020 Frontiers in Neuroscience
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Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

2020 BMC Neurology
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Perfusion, Diffusion, Or Brain Tumor Barrier Integrity: Which Represents The Glioma Features Best?

2019 Cancer management and research
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Radiomics strategy for glioma grading using texture features from multiparametric MRI

2018 Journal of Magnetic Resonance Imaging
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Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis

2018 BMC Cancer
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Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

2017 Oncotarget
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Comparison between ultra-high and conventional mono b-value DWI for preoperative glioma grading

2017 Oncotarget
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Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial

2017 BMC Medical Imaging
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Citations (24)