Focal liver lesion diagnosis with deep learning and multistage CT imaging.

Journal: Nature communications
PMID: 39147767

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.

Authors

  • Yi Wei
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province 610000, China.
  • Meiyi Yang
    Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China.
  • Meng Zhang
    College of Software, Beihang University, Beijing, China.
  • Feifei Gao
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Ning Zhang
    Institute of Nuclear Agricultural Sciences, Zhejiang University, Hangzhou, 310058, China.
  • Fubi Hu
    Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.
  • Xiao Zhang
    Merck & Co., Inc., Rahway, NJ, USA.
  • Shasha Zhang
    Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
  • Zixing Huang
    Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province 610000, China.
  • Lifeng Xu
  • Feng Zhang
    Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China; Key Laboratory of Food Quality and Safety for State Market Regulation, Beijing 100176, China. Electronic address: fengzhang@126.com.
  • Minghui Liu
  • Jiali Deng
  • Xuan Cheng
  • Tianshu Xie
  • Xiaomin Wang
    Key Laboratory of Grain and Oil Processing and Food Safety of Sichuan Province, College of Food and Bioengineering, Xihua University Chengdu 610039 China xingyage1@163.com.
  • Nianbo Liu
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Haigang Gong
  • Shaocheng Zhu
    Department of Radiology, Henan Provincial People's Hospital, Zhengzhou, Henan, China. zsc2686@126.com.
  • Bin Song
    Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Ming Liu
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: mingliu@chd.edu.cn.

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