Improving the prediction of patient survival with the aid of residual convolutional neural network (ResNet) in colorectal cancer with unresectable liver metastases treated with bevacizumab-based chemotherapy.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID: 39696483

Abstract

BACKGROUND: To verify overall survival predictions made with residual convolutional neural network-determined morphological response (ResNet-MR) in patients with unresectable synchronous liver-only metastatic colorectal cancer (mCRC) treated with bevacizumab-based chemotherapy (BBC).

Authors

  • Sung-Hua Chiu
    Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan.
  • Hsiao-Chi Li
    Department of Computer Science and Information Engineering, Fu Jen Catholic University, No. 510, Zhongzheng Road, Xinzhuang District, New Taipei City 242, Taiwan.
  • Wei-Chou Chang
    Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Chao-Cheng Wu
    Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan.
  • Hsuan-Hwai Lin
    Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Cheng-Hsiang Lo
    Department of Radiotherapy, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Ping-Ying Chang
    Division of Hematology/Oncology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan. max-chang@yahoo.com.tw.

Keywords

External Resources

Popular Topics
Recent Journals