A CNN-transformer fusion network for predicting high-grade patterns in stage IA invasive lung adenocarcinoma.

Journal: Medical physics
Published Date: Apr 1, 2025

Abstract

BACKGROUND: Invasive lung adenocarcinoma (LUAD) with the high-grade patterns (HGPs) has the potential for rapid metastasis and frequent recurrence. Therefore, accurately predicting the presence of high-grade components is crucial for doctors to develop personalized treatment plans and improve patient prognosis.

Authors

  • Yali Tao
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  • Rong Sun
    Hefei National Laboratory for Physical Sciences at the Microscale, Hefei, Anhui, P. R. China.
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Wenhui Wu
    State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Department and Institute of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yuanzhong Xie
    Medical Imaging Center, Taian Central Hospital, Taian, Shandong, China. xie01088@126.com.
  • Xiaodan Ye
    Department of Radiology, Shanghai Chest Hospital Shanghai Jiao Tong University, 200030, Shanghai, PR China. Electronic address: yuanyxd@163.com.
  • Xiujuan Li
    School of Electrical Engineering, Guangxi University, Nanning 530004, China. Electronic address: l781453379@163.com.
  • Shengdong Nie
    School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Road, Shanghai, 200093, China.

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