Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study.

Journal: Frontiers in immunology
PMID:

Abstract

OBJECTIVE: Develop a predictive model utilizing weakly supervised deep learning techniques to accurately forecast major pathological response (MPR) in patients with resectable non-small cell lung cancer (NSCLC) undergoing neoadjuvant chemoimmunotherapy (NICT), by leveraging whole slide images (WSIs).

Authors

  • Dan Han
    College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xin Zheng
    Department of Clinical Laboratory, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China. Electronic address: dearjanna@126.com.
  • Shenbo Fu
    Department of Radiation Oncology, Shanxi Provincial Tumor Hospital, Xi'an, Shanxi, China.
  • Ran Wei
  • Qian Zhao
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Chengxin Liu
    Shandong Luoxin Pharmaceutical Group Stock Co. Ltd, Linyi, Shandong, China.
  • Zhongtang Wang
    Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China.
  • Wei Huang
    Shaanxi Institute of Flexible Electronics, Northwestern Polytechnical University, 710072 Xi'an, China.
  • Shaoyu Hao
    Department of Thoracic Surgery, Shandong University Cancer Center, Jinan, Shandong, China.