| 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 |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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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 |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Han Ma, Zhong-Xin Liu, Jing-Jing Zhang, Feng-Tian Wu, Cheng-Fu Xu, Zhe Shen, Chao-Hui Yu and You-Ming Li |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| the National Natural Science Foundation of China |
81900509 |
| Fundamental Research Funds for the Central Universities |
2018XZZX002-10 |
| “Ten thousand plan”-High-Level Talents Special Support Plan of Zhejiang Province |
ZJWR0108008 |
|
| Corresponding Author |
Chao-Hui Yu, MD, PhD, Chief Doctor, Professor, Department of Gastroenterology, Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, College of Medicine, Zhejiang University, No. 79 Qingchun Road, Hangzhou 310003, Zhejiang Province, China. zyyyych@zju.edu.cn |
| Key Words |
Deep learning; Convolutional neural networks; Pancreatic cancer; Computed tomography; ; |
| Core Tip |
We developed a deep learning-based, computer-aided pancreatic ductal adenocarcinoma model trained on CT images with pathological confirmed pancreatic cancer in this retrospective study. We evaluated our approach on the datasets in terms of both binary and ternary classifier, with the purposes of detecting and localizing mass, respectively. In the binary classifier, the performance of plain, arterial and venous phase had no difference, its accuracy on plain scan achieved 95.47%, sensitivity achieved 91.58%, and specificity achieved 98.27%. In the ternary classifier, the arterial phase had the highest sensitivity in detecting cancer in the head of the pancreas among three phases. Our model is suitable for screening purposes in general medical practice. |
| Publish Date |
2020-09-09 11:11 |
| Citation |
Ma H, Liu ZX, Zhang JJ, Wu FT, Xu CF, Shen Z, Yu CH, Li YM. Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis. World J Gastroenterol 2020; 26(34): 5156-5168 |
| URL |
https://www.wjgnet.com/1007-9327/full/v26/i34/5156.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v26.i34.5156 |
