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Xiaoying Wang

As seen in: Arthritis & Rheumatology, Nature, Wiley Online Library, Yahoo Canada, arXiv, PLOS, PLOS Biology, PLOS ONE, ScienceDirect, The Journal of Nutrition and
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Articles

An Apparent Diffusion Coefficient-Based Radiomics Tool for Clinically Significant Prostate Cancer Detection in PI-RADS 3 Lesions: A Multicenter Study

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By Ge Gao, Kexin Wang, Xiaoying Wang, Yi Liu, Pengsheng Wu, Ming Liu Verified, Miao Wang, Juan Hu, Bing Fan, Si Nie, tong liu, Shaoxi Niu, Guangyong Shi, Naishan Qin
| Academic Radiology Verified
Abbreviations PI-RADS (Prostate Imaging Reporting and Data System) csPCa (clinically significant prostate cancer) tPSA (total prostate-specific antigen) PSAD (prostate-specific antigen density) PV (prostate volume) LV (lesion volume) TRUS (transrectal ultrasound) TBx (target biopsy) SBx (systematic biopsy) TURP (transurethral resection of the prostate) GS (Gleason score) ISUP (International Society of Urological Pathology) AS (active surveillance) LoG (Laplacian-of-Gaussian) GLCM (gray-level...
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Your high school friends may shape whether you become an entrepreneur decades later Original

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By Seok-Woo Kwon, Xiaoying Wang
| The Conversation Verified
With more young Canadians considering self-employment amid a high youth unemployment rate, entrepreneurship programs are facing growing demand. Applications to Futurpreneur, the national organization that finances young Canadian entrepreneurs, rose 50 per cent last quarter, compared with 15 per cent growth a year earlier.
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Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer

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By Xiaoying Wang, Maoteng Duan, Anthony Snyder, Po-Lan Su, Jianying Li, Jordan E. Krull, Jiacheng Jin, Yang Xu, Yuhan Sun, Hu Chen, Weidong Wu, Weiqing Chen, Kai He, Chi Zhang, Sha Cao, Jing Zhao, Dong Xu, Guangyu Wang, Lang Li, Gang Xin, David P. Carbone, Zihai Li, Richard Carpenter, Qin Ma
| Nature Verified
Abstract Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers.
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