A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

Journal: European radiology
PMID: 31811427

Abstract

OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists.

Authors

  • Jing Gong
    Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China.
  • Jiyu Liu
    Department of Radiology, Shanghai Pulmonary Hospital, 507 Zheng Min Road, Shanghai, 200433, China.
  • Wen Hao
    Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200032, China.
  • Shengdong Nie
    School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Road, Shanghai, 200093, China.
  • Bin Zheng
    School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA.
  • Shengping Wang
    Tianneng Battery Group Co., Ltd, Zhejiang 313100, P. R. China.
  • Weijun Peng
    Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.

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