A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

Journal: European journal of radiology
Published Date: Nov 15, 2021

Abstract

OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs).

Authors

  • XiangMeng Chen
    The Department of Radiology, Jiangmen Central Hospital/Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China.
  • Bao Feng
    The Department of Radiology, Jiangmen Central Hospital/Affiliated Jiangmen Hospital of Sun Yat-Sen University, No. 23 Haibang Street, Jiangmen, 529000, Guangdong, China.
  • Yehang Chen
    Biomedical and Artificial Intelligence Laboratory, Guilin University of Aerospace Technology, Guilin, China.
  • Xiaobei Duan
    Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, PR China. Electronic address: 258573168@qq.com.
  • KunFeng Liu
    The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-Sen University, NO.52 Meihuadong Street, Zhuhai, 519000, Guangdong Province, China.
  • KunWei Li
    The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-Sen University, NO.52 Meihuadong Street, Zhuhai, 519000, Guangdong Province, China.
  • Chaotong Zhang
    Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province 529030, PR China. Electronic address: zct3820@sina.com.
  • XueGuo Liu
    The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-Sen University, NO.52 Meihuadong Street, Zhuhai, 519000, Guangdong Province, China. liuxueg@mail.sysu.edu.cn.
  • Wansheng Long
    Department of Radiology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Guangdong Medical University, Affiliated Jiangmen Hospital of SUN YAT-SEN University, 23 Beijie Haibang Street, Jiangmen, 529030, China.

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