Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer.

Journal: Computers in biology and medicine
Published Date: Oct 25, 2022

Abstract

BACKGROUND: An accurate preoperative assessment of Non-Muscle-Invasive Bladder Cancer (NMIBC) and Muscle-Invasive Bladder Cancer (MIBC) in Bladder Cancer (BCa) can help the urologist make diagnostic decisions. Considering the absence of multiparametric MRI for contrast medium allergy and economic reasons, this study aims to develop a deep learning method based on T2-Weighted (T2WI) images alone for predicting NMIBC and MIBC.

Authors

  • Yuan Zou
    National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Institute of Technology, Beijing, China.
  • Lingkai Cai
    Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Chunxiao Chen
    Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China. ccxbme@nuaa.edu.cn.
  • Qiang Shao
    Department of Urology, the Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing, China. Electronic address: sq7166822@163.com.
  • Xue Fu
    Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Jie Yu
    Institute of Animal Nutrition, Sichuan Agricultural University, Key Laboratory for Animal Disease-Resistance Nutrition of China Ministry of Education, Key Laboratory of Animal Disease-resistant Nutrition and Feed of China Ministry of Agriculture and Rural Affairs, Key Laboratory of Animal Disease-resistant Nutrition of Sichuan Province, Ya'an, 625014, China.
  • Liang Wang
    Information Department, Dazhou Central Hospital, Dazhou 635000, China.
  • Zhiying Chen
    Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Xiao Yang
    Department of Nephrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Baorui Yuan
    Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Peikun Liu
    Department of Urology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Qiang Lu
    Department of Computer Science and Technology, China University of Petroleum, Beijing 102249, China.

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