A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images.

Journal: Journal of translational medicine
Published Date: Oct 17, 2023

Abstract

BACKGROUND: Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models. With gigapixel WSIs exhibiting diverse histological appearances, obtaining clinically prognostic and explainable features remains challenging. Therefore, we propose a novel deep learning-based algorithm combining tissue areas with histopathological features to predict cancer survival.

Authors

  • Yan-Jun Li
    School of Chemistry and Chemical Engineering, Chongqing University Chongqing 400044 China qhzhang@cqu.edu.cn +86-023-65102531.
  • Hsin-Hung Chou
    Department of Computer Science and Engineering, National Chi Nan University, Nantou, Taiwan. Electronic address: chouhh@ncnu.edu.tw.
  • Peng-Chan Lin
    Internal Medicine Department.
  • Meng-Ru Shen
    Department of Obstetrics and Gynecology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
  • Sun-Yuan Hsieh
    Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.

Keywords

External Resources

Popular Topics
Recent Journals