Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.

Journal: Respiratory research
PMID: 40057743

Abstract

BACKGROUND: To evaluate the clinical applicability of deep learning (DL) models based on automatic segmentation in preoperatively predicting tumor spread through air spaces (STAS) in peripheral stage I lung adenocarcinoma (LUAD).

Authors

  • Cong Liu
    Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St, Chicago, IL, 60607, USA.
  • Yu-Feng Wang
    Departments of Nuclear Medicine, The Xuzhou Hospital Affiliated to Jiangsu University, Xuzhou Cancer Hospital, Xuzhou, People's Republic of China.
  • Ping Gong
    Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Xiu-Qing Xue
    Department of Nuclear Medicine, The First People's Hospital of Yancheng, Yancheng, People's Republic of China.
  • Hong-Ying Zhao
    Department of Radiotherapy, The Xuzhou Hospital Affiliated to Jiangsu University, Xuzhou Cancer Hospital, Xuzhou, People's Republic of China.
  • Hui Qian
    The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China.
  • Chao Jia
    Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China; Shandong Engineering Research Center for Environmental Protection and Remediation on Groundwater, Jinan 250014, China. Electronic address: jiachao@sdu.edu.cn.
  • Xiao-Feng Li
    Department of Radiology, The Xuzhou Hospital Affiliated to Jiangsu University, Xuzhou Cancer Hospital, Xuzhou, People's Republic of China. lxf5818@163.com.

Keywords

External Resources

Popular Topics
Recent Journals