| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| 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/ |
| Copyright |
© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Retrospective Study |
| Article Title |
Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Su-E Cao, Lin-Qi Zhang, Si-Chi Kuang, Wen-Qi Shi, Bing Hu, Si-Dong Xie, Yi-Nan Chen, Hui Liu, Si-Min Chen, Ting Jiang, Meng Ye, Han-Xi Zhang and Jin Wang |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
91959118 |
| Science and Technology Program of Guangzhou, China |
201704020016 |
| SKY Radiology Department International Medical Research Foundation of China |
Z-2014-07-1912-15 |
| Clinical Research Foundation of the 3rd Affiliated Hospital of Sun Yat-Sen University |
YHJH201901 |
|
| Corresponding Author |
Jin Wang, MD, Doctor, 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 |
| Key Words |
Deep learning; Convolutional neural networks; Focal liver lesions; Classification; Multiphase computed tomography; Dynamic enhancement pattern |
| 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 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. |
| Publish Date |
2020-07-07 14:50 |
| Citation |
Cao SE, Zhang LQ, Kuang SC, Shi WQ, Hu B, Xie SD, Chen YN, Liu H, Chen SM, Jiang T, Ye M, Zhang HX, Wang J. Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography. World Journal of Gastroenterol 2020; 26(25): 3660-3672 |
| URL |
https://www.wjgnet.com/1007-9327/full/v26/i25/3660.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v26.i25.3660 |
