Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To compare the diagnostic performance of a novel deep learning (DL) method based on T2-weighted imaging with the vesical imaging-reporting and data system (VI-RADS) in predicting muscle invasion in bladder cancer (MIBC).

Authors

  • Jianpeng Li
    Department of Cardiology, Taizhou Second People's Hospital, The Affiliated Taizhou Second People's Hospital of Yangzhou University, Taizhou, China.
  • Kangyang Cao
    Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
  • Hongxin Lin
    Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China.
  • Lei Deng
    1] Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing 100084, China [2] Optical Memory National Engineering Research Center, Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
  • Shuiqing Yang
    School of Information Management and Engineering, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, People's Republic of China.
  • Yun Gao
    The Cancer Research Institute, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.
  • Manqiu Liang
    Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China.
  • Chuxuan Lin
    Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
  • Weijing Zhang
    Imaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
  • Chuanmiao Xie
    Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
  • Kunlin Zhang
    Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China.
  • Jiexin Luo
    Department of Urology, Affiliated Dongguan Hospital, Southern Medical University, Dongguan, Guangdong, China.
  • Zhaohong Pan
    Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
  • Peiyan Yue
    Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
  • Yujian Zou
    Department of Radiology, The People's Hospital of Dongguan, Dongguan, Guangdong, China.
  • Bingsheng Huang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.