Study of AI algorithms on mpMRI and PHI for the diagnosis of clinically significant prostate cancer.

Journal: Urologic oncology
Published Date:

Abstract

OBJECTIVE: To study the feasibility of multiple factors in improving the diagnostic accuracy of clinically significant prostate cancer (csPCa).

Authors

  • Zeyu Luo
    Chongqing Key Laboratory of Vector Insects.
  • Jialei Li
    The Affiliated Hospital of Jiaxing University, Jiaxing, China.
  • Kexin Wang
    Clifford Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Song Li
    Department of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State University Blacksburg, VA, USA.
  • Yi Qian
    Jinhua People's Hospital, Jinhua, China. qianyicosta@163.com.
  • Wenhua Xie
    The Affiliated Hospital of Jiaxing University, Jiaxing, China.
  • Pengsheng Wu
    Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China.
  • Xiangpeng Wang
    Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
  • Jun Han
    School of Basic Medical Sciences, Yunnan Traditional Chinese Medical College, Kunming 650500, China. Electronic address: hanzjn@126.com.
  • Wei Zhu
    The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine Guangzhou 510120 China zhuwei9201@163.com.
  • Hu Wang
  • Yi He
    National Institutes for Food and Drug Control, 2 Tiantan Xili, Beijing 100050, China.

Keywords

No keywords available for this article.