Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer using FDG PET/CT images.

Journal: Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Published Date: Aug 8, 2025

Abstract

BACKGROUND: This study aimed to develop and validate a hybrid deep learning (DL) model that integrates convolutional neural network (CNN) and vision transformer (ViT) architectures to predict distant metastasis (DM) in patients with non-small cell lung cancer (NSCLC) using F-FDG PET/CT images.

Authors

  • Na Hu
    Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
  • Yunpeng Luo
    Department of Anesthesiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou Province, China.
  • Maowen Tang
    Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
  • Gang Yan
    School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China.
  • Shengmei Yuan
    Department of Ultrasound Center, Affliated Hospital of Guizhou Medical University, Guiyang, 550004, China.
  • Fangyan Li
    Department of Radiology, The Affiliated Hospital of Guizhou Medical University, No. 28, Guiyi Street, Yunyan District, Guiyang, 550004, China.
  • Pinggui Lei
    Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China.

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