Deep learning model using CT images for longitudinal prediction of benign and malignant ground-glass nodules.

Journal: European journal of radiology
Published Date:

Abstract

OBJECTIVES: To develop and validate a CT image-based multiple time-series deep learning model for the longitudinal prediction of benign and malignant pulmonary ground-glass nodules (GGNs).

Authors

  • Xiaolong Yang
    Department of Ophthalmology, Huadong Sanatorium, Wuxi, China.
  • Jiayang Wang
    College of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou, China.
  • Ping Wang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China. Electronic address: wangping876@163.com.
  • Yingjie Li
    School of Communication and Information Engineering, Shanghai University, China.
  • Zhubin Wen
    Department of Radiology, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang 150081, China.
  • Jiming Shang
    Department of Radiology, Beidahuang Industry Group General Hospital, Harbin 150001, China.
  • Kaige Chen
    Department of Radiology, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang 150081, China.
  • Chao Tang
    School of Public Health, Dalian Medical University, Dalian, China.
  • Shuang Liang
  • Wei Meng