Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.

Journal: The Lancet. Digital health
PMID:

Abstract

BACKGROUND: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade detection. Convolutional neural networks (CNNs) have shown promise in image analysis, but the networks' potential for pancreatic cancer detection and diagnosis is unclear. We aimed to investigate whether CNN could distinguish individuals with and without pancreatic cancer on CT, compared with radiologist interpretation.

Authors

  • Kao-Lang Liu
    Department of Medical Imaging, National Taiwan University Cancer Center, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
  • Tinghui Wu
    Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan.
  • Po-Ting Chen
    Department of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
  • Yuhsiang M Tsai
    Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, Taiwan.
  • Holger Roth
    Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
  • Ming-Shiang Wu
    Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan; Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
  • Wei-Chih Liao
    Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan; Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan. Electronic address: david.ntuh@gmail.com.
  • Weichung Wang
    Graduate Program of Data Science, National Taiwan University and Academia Sinica, Taipei, Taiwan.