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Focal liver lesion diagnosis with deep learning and multistage CT imaging

Author

Listed:
  • Yi Wei

    (Sichuan University)

  • Meiyi Yang

    (University of Electronic Science and Technology of China)

  • Meng Zhang

    (Sanya People’s Hospital)

  • Feifei Gao

    (Sichuan University)

  • Ning Zhang

    (Henan Provincial People’s Hospital)

  • Fubi Hu

    (The First Affiliated Hospital of Chengdu Medical College)

  • Xiao Zhang

    (Leshan People’s Hospital)

  • Shasha Zhang

    (Guizhou Provincial People’s Hospital)

  • Zixing Huang

    (Sichuan University)

  • Lifeng Xu

    (Quzhou People’s Hospital)

  • Feng Zhang

    (Quzhou People’s Hospital)

  • Minghui Liu

    (University of Electronic Science and Technology of China)

  • Jiali Deng

    (University of Electronic Science and Technology of China)

  • Xuan Cheng

    (University of Electronic Science and Technology of China)

  • Tianshu Xie

    (University of Electronic Science and Technology of China)

  • Xiaomin Wang

    (University of Electronic Science and Technology of China)

  • Nianbo Liu

    (University of Electronic Science and Technology of China)

  • Haigang Gong

    (University of Electronic Science and Technology of China)

  • Shaocheng Zhu

    (Henan Provincial People’s Hospital)

  • Bin Song

    (Sichuan University
    Sanya People’s Hospital)

  • Ming Liu

    (Quzhou People’s Hospital
    University of Electronic Science and Technology of China)

Abstract

Diagnosing liver lesions is crucial for treatment choices and patient outcomes. This study develops an automatic diagnosis system for liver lesions using multiphase enhanced computed tomography (CT). A total of 4039 patients from six data centers are enrolled to develop Liver Lesion Network (LiLNet). LiLNet identifies focal liver lesions, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), metastatic tumors (MET), focal nodular hyperplasia (FNH), hemangioma (HEM), and cysts (CYST). Validated in four external centers and clinically verified in two hospitals, LiLNet achieves an accuracy (ACC) of 94.7% and an area under the curve (AUC) of 97.2% for benign and malignant tumors. For HCC, ICC, and MET, the ACC is 88.7% with an AUC of 95.6%. For FNH, HEM, and CYST, the ACC is 88.6% with an AUC of 95.9%. LiLNet can aid in clinical diagnosis, especially in regions with a shortage of radiologists.

Suggested Citation

  • Yi Wei & Meiyi Yang & Meng Zhang & Feifei Gao & Ning Zhang & Fubi Hu & Xiao Zhang & Shasha Zhang & Zixing Huang & Lifeng Xu & Feng Zhang & Minghui Liu & Jiali Deng & Xuan Cheng & Tianshu Xie & Xiaomin, 2024. "Focal liver lesion diagnosis with deep learning and multistage CT imaging," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51260-6
    DOI: 10.1038/s41467-024-51260-6
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