Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date:

Abstract

BACKGROUND: Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL.

Authors

  • Zhaonan Sun
    Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
  • Pengsheng Wu
    Beijing Smart Tree Medical Technology Co. Ltd, Beijing, China.
  • Yingpu Cui
    Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
  • Xiang Liu
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China; Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse, Anhui Jianzhu University, Hefei 230009, China.
  • Kexin Wang
    Clifford Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Ge Gao
    School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou 213000, China.
  • Huihui Wang
    School of Mechanical Engineering & Automation, Dalian Polytechnic University, Qinggongyuan 1, Ganjingzi District, Dalian 116034, PR China; National Engineering Research Center of Seafood, Dalian Polytechnic University, Qinggongyuan 1, Ganjingzi District, Dalian 116034, PR China; Engineering Research Center of Seafood of Ministry of Education of China, Dalian 116034, PR China; Collaborative Innovation Center of Seafood Deep Processing, Dalian 116034, PR China. Electronic address: wanghh@dlpu.edu.cn.
  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • XiaoYing Wang