Copyright
©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jul 7, 2020; 26(25): 3660-3672
Published online Jul 7, 2020. doi: 10.3748/wjg.v26.i25.3660
Published online Jul 7, 2020. doi: 10.3748/wjg.v26.i25.3660
Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography
Su-E Cao, Lin-Qi Zhang, Si-Chi Kuang, Wen-Qi Shi, Bing Hu, Si-Dong Xie, Si-Min Chen, Ting Jiang, Han-Xi Zhang, Jin Wang, Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, Guangdong Province, China
Yi-Nan Chen, Hui Liu, Meng Ye, Department of Scientific and Technological Research, 12 Sigma Technologies, Beijing 100102, China
Author contributions: Cao SE, Zhang LQ, Shi WQ, Chen YN, Liu H, and Ye M contributed to the conception and design of the study; Cao SE, Kuang SC, Shi WQ, Hu B, Jiang T, Chen SM, and Zhang HX collected the patient data, analyzed and interpreted the data; Cao SE wrote original draft and revised the manuscript; Wang J contributed to the conception of the study and provided final approval of the version to be submitted and any revised versions.
Supported by National Natural Science Foundation of China , No. 91959118 ; Science and Technology Program of Guangzhou , China, No. 201704020016 ; SKY Radiology Department International Medical Research Foundation of China , No. Z-2014-07-1912-15 ; and Clinical Research Foundation of the 3rd Affiliated Hospital of Sun Yat-Sen University , No. YHJH201901 .
Institutional review board statement: The study was reviewed and approved for publication by our Institutional Reviewer.
Informed consent statement: All study participants or their legal guardian provided informed written consent about personal and medical data collection prior to study enrolment.
Conflict-of-interest statement: All the Authors have no conflict of interest related to the manuscript.
Data sharing statement: The original anonymous dataset is available on request from the corresponding author at wangjin3@mail.sysu.edu.cn.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Jin Wang, MD, Doctor, Professor, Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, No. 600, Tianhe Road, Tianhe District, Guangzhou 510630, Guangdong Province, China. wangjin3@mail.sysu.edu.cn
Received: March 16, 2020
Peer-review started: March 16, 2020
First decision: April 25, 2020
Revised: May 8, 2020
Accepted: June 4, 2020
Article in press: June 4, 2020
Published online: July 7, 2020
Peer-review started: March 16, 2020
First decision: April 25, 2020
Revised: May 8, 2020
Accepted: June 4, 2020
Article in press: June 4, 2020
Published online: July 7, 2020
Core Tip
Core tip: We developed and evaluated a deep learning-based convolutional neural network (CNN) to classify focal liver lesions (FLLs) on multiphase computed tomography. The most important highlight of the current study is that, to the best of our knowledge, this study is the first to employ four-channel input data to preserve the dynamic enhancement properties. The combination of the lesion's dynamic enhancement pattern with a CNN can imitate the image diagnosis of radiologists and is expected to improve diagnostic accuracy. It was interesting to note that the accuracy and specificity of differentiating each category from others were high. This model may become an efficient tool to assist radiologists in the classification of FLLs.