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, Ming Liu, Ming Liu, Ming Liu
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. The distinction of liver lesions is critical for the accurate diagnosis and treatment of liver cancer. Here, the authors develop LiLNet, a deep learning-based system to identify focal liver lesions as well as benign and malignant liver tumours from CT images with high accuracy across multiple patient cohorts.