F-FDG PET/CT-based deep learning models and a clinical-metabolic nomogram for predicting high-grade patterns in lung adenocarcinoma.

Journal: BMC medical imaging
PMID: 40295979

Abstract

BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasively predicting high-grade patterns (HGPs) of invasive lung adenocarcinoma (LUAD).

Authors

  • Yue Guo
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
  • Xibin Jia
    Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
  • Chuanxu Yang
    Faculty of Information Technology, Beijing University of Technology, Beijing, China.
  • Chao Fan
    College of Management Science, Chengdu University of Technology, Chengdu, China.
  • Hui Zhu
  • Xu Chen
    School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
  • Fugeng Liu
    Department of Nuclear Medicine, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.

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