Deep Learning Model for Pathological Grading and Prognostic Assessment of Lung Cancer Using CT Imaging: A Study on NLST and External Validation Cohorts.

Journal: Academic radiology
Published Date:

Abstract

RATIONALE AND OBJECTIVES: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning.

Authors

  • Runhuang Yang
    Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China.
  • Weiming Li
    Shanghai Nuanhe Brain Technology Co., Ltd, Shanghai, China.
  • Siqi Yu
    Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China (R.Y., W.L., S.Y., H.Z., X.L., L.T., X.G.); Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China (R.Y., W.L., S.Y., H.Z., X.L., L.T., X.G.). Electronic address: ysq@mail.ccmu.edu.cn.
  • Zhiyuan Wu
    Pediatric Intensive Care Units, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
  • Haiping Zhang
    Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Oilseeds processing, Ministry of Agriculture, Oil Crops and Lipids Process Technology National & Local Joint Engineering Laboratory, Hubei Key Laboratory of Lipid Chemistry and Nutrition, Wuhan 430062, China.
  • Xiangtong Liu
    Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China.
  • Lixin Tao
    Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.
  • Xia Li
    Research Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
  • Jian Huang
    Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, P. R. China.
  • Xiuhua Guo
    School of Public Health, Capital Medical University, Beijing 100069, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069. Electronic address: statguo@ccmu.edu.cn.