A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal squamous cell carcinoma based on endoscopic images using deep learning.

Journal: The British journal of radiology
PMID: 35451338

Abstract

OBJECTIVES: To propose deep-learning (DL)-based predictive model for pathological complete response rate for resectable locally advanced esophageal squamous cell carcinoma (SCC) after neoadjuvant chemoradiotherapy (NCRT) with endoscopic images.

Authors

  • Daisuke Kawahara
    Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Yuji Murakami
    Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan. Electronic address: Yujimura@hiroshima-u.ac.jp.
  • Shigeyuki Tani
    School of Medicine, Hiroshima University, Hiroshima, Japan.
  • Yasushi Nagata
    Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Keywords

External Resources

Popular Topics
Recent Journals