Focal liver lesion diagnosis with deep learning and multistage CT imaging

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 Wang, Nianbo Liu, Haigang Gong, Shaocheng Zhu (), Bin Song () and Ming Liu ()
Additional contact information
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
Ming Liu: Quzhou People’s Hospital

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-51260-6

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