A computed tomography-based deep learning radiomics model for predicting the gender-age-physiology stage of patients with connective tissue disease-associated interstitial lung disease.

Journal: Computers in biology and medicine
PMID: 40209580

Abstract

OBJECTIVES: To explore the feasibility of using a diagnostic model constructed with deep learning-radiomics (DLR) features extracted from chest computed tomography (CT) images to predict the gender-age-physiology (GAP) stage of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD).

Authors

  • Bingqing Long
    Department of Radiology, People's Hospital of Longhua, Shenzhen, 518109, China. Electronic address: 775076175@qq.com.
  • Rui Li
    Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China.
  • Ronghua Wang
    Shenzhen Polytechnic, Shenzhen 518055, China.
  • Anyu Yin
    Department of Radiology, People's Hospital of Longhua, Shenzhen, 518109, China. Electronic address: 493303706@qq.com.
  • Ziyi Zhuang
    Department of Radiology, People's Hospital of Longhua, Shenzhen, 518109, China. Electronic address: zhuangziyi0123@163.com.
  • Yang Jing
    Huiying Medical Technology Co., Ltd, Dongsheng Science and Technology Park, Haidian District, Beijing, China.
  • Linning E
    Department of Radiology, People's Hospital of Longhua, Shenzhen, China.

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