Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study
Qi Yang,
Jingwei Wei,
Xiaohan Hao,
Dexing Kong,
Xiaoling Yu,
Tianan Jiang,
Junqing Xi,
Wenjia Cai,
Yanchun Luo,
Xiang Jing,
Yilin Yang,
Zhigang Cheng,
Jinyu Wu,
Huiping Zhang,
Jintang Liao,
Pei Zhou,
Yu Song,
Yao Zhang,
Zhiyu Han,
Wen Cheng,
Lina Tang,
Fangyi Liu,
Jianping Dou,
Rongqin Zheng,
Jie Yu,
Jie Tian,
Ping Liang
Affiliations
Qi Yang
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Jingwei Wei
Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
Xiaohan Hao
Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Centers for Biomedical Engineering, University of Science and Technology of China, University of Science and Technology of China, Hefei, China
Dexing Kong
School of Mathematical Sciences, Zhejiang University, Hangzhou, China
Xiaoling Yu
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Tianan Jiang
Department of Ultrasound, the First Affiliated hospital, College of Medicine, Zhejiang University, Hangzhou, Jiangsu, China
Junqing Xi
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Wenjia Cai
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Yanchun Luo
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Xiang Jing
Department of Ultrasound, Tianjin Third Central Hospital, Tianjin, China
Yilin Yang
Department of Ultrasound Diagnosis, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
Zhigang Cheng
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Jinyu Wu
Department of Ultrasound, Harbin The First Hospital, Harbin, China
Huiping Zhang
Department of Medical Ultrasound, Ma'anshan People's Hospital, Ma'anshan, China
Jintang Liao
Department of Diagnostic Ultrasound, Xiangya Hospital, Changsha, China
Pei Zhou
Department of Ultrasound, Central Theater Command General Hospital, Chinese People's Liberation Army, Wuhan, China
Yu Song
Department of Diagnostic Ultrasound, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
Yao Zhang
Department of Ultrasound, Beijing Ditan Hospital, Capital Medical University, Beijing, China
Zhiyu Han
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Wen Cheng
Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
Lina Tang
Department of Ultrasound, Fujian Cancer Hospital&Fujian Medical University Cancer Hospita, Fuzhou, China
Fangyi Liu
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Jianping Dou
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Rongqin Zheng
Guangdong Key Laboratory of Liver Disease Research, Department of Medical Ultrasound, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Corresponding author at: Guangdong Key Laboratory of Liver Disease Research, Department of Medical Ultrasound, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
Jie Yu
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China; Corresponding author at: Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Jie Tian
Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Corresponding author at: Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Ping Liang
Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China; Corresponding author at: Department of Interventional Ultrasound, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China
Background: The diagnosis performance of B-mode ultrasound (US) for focal liver lesions (FLLs) is relatively limited. We aimed to develop a deep convolutional neural network of US (DCNN-US) for aiding radiologists in classification of malignant from benign FLLs. Materials and methods: This study was conducted in 13 hospitals and finally 2143 patients with 24,343 US images were enrolled. Patients who had non-cystic FLLs with pathological results were enrolled. The FLLs from 11 hospitals were randomly divided into training and internal validations (IV) cohorts with a 4:1 ratio for developing and evaluating DCNN-US. Diagnostic performance of the model was verified using external validation (EV) cohort from another two hospitals. The diagnosis value of DCNN-US was compared with that of contrast enhanced computed tomography (CT)/magnetic resonance image (MRI) and 236 radiologists, respectively. Findings: The AUC of ModelLBC for FLLs was 0.924 (95% CI: 0.889–0.959) in the EV cohort. The diagnostic sensitivity and specificity of ModelLBC were superior to 15-year skilled radiologists (86.5% vs 76.1%, p = 0.0084 and 85.5% vs 76.9%, p = 0.0051, respectively). Accuracy of ModelLBC was comparable to that of contrast enhanced CT (both 84.7%) but inferior to contrast enhanced MRI (87.9%) for lesions detected by US. Interpretation: DCNN-US with high sensitivity and specificity in diagnosing FLLs shows its potential to assist less-experienced radiologists in improving their performance and lowering their dependence on sectional imaging in liver cancer diagnosis.
