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Articles Published Processes
9/15/2021 10:33:58 AM | Browse: 335 | Download: 737
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Received |
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2021-04-30 07:39 |
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Peer-Review Started |
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2021-04-30 07:41 |
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To Make the First Decision |
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Return for Revision |
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2021-06-23 01:45 |
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Revised |
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2021-07-07 16:27 |
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Second Decision |
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2021-08-24 03:27 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Company Editor-in-Chief |
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2021-08-25 08:15 |
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Articles in Press |
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2021-08-25 08:15 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2021-09-10 14:23 |
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Publish the Manuscript Online |
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2021-09-15 10:33 |
| 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) 2021. 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 |
Radiology, Nuclear Medicine & Medical Imaging |
| Manuscript Type |
Retrospective Study |
| Article Title |
Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Róbert Stollmayer, Bettina K Budai, Ambrus Tóth, Ildikó Kalina, Erika Hartmann, Péter Szoldán, Viktor Bérczi, Pál Maurovich-Horvat and Pál N Kaposi |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Bettina K Budai, MD, N/A, Department of Radiology, Medical Imaging Centre, Faculty of Medicine, Semmelweis University, Korányi Sándor st. 2., Budapest 1083, Hungary. budai.bettina@med.semmelweis-univ.hu |
| Key Words |
Artificial intelligence; Multi-parametric magnetic resonance imaging; Hepatocyte-specific contrast; Densely connected convolutional network; Hepatocellular carcinoma; Focal nodular hyperplasia |
| Core Tip |
Our study aimed to assess the performance of two-dimensional (2D) and three-dimensional (3D) densely connected convolutional neural networks (DenseNets) in the classification of focal liver lesions (FLLs) based on multi-parametric magnetic resonance imaging (MRI) with hepatocyte-specific contrast. We used multi-channel data input to train our networks and found that both 2D and 3D-DenseNets can differentiate between focal nodular hyperplasias, hepatocellular carcinomas or liver metastases with excellent accuracy. We conclude that DensNets can reliably classify FLLs based on multi-parametric and hepatocyte-specific post-contrast MRI. Meanwhile, multi-channel input is advantageous when the number of clinical cases available for model training is limited. |
| Publish Date |
2021-09-15 10:33 |
| Citation |
Stollmayer R, Budai BK, Tóth A, Kalina I, Hartmann E, Szoldán P, Bérczi V, Maurovich-Horvat P, Kaposi PN. Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging. World J Gastroenterol 2021; 27(35): 5978-5988 |
| URL |
https://www.wjgnet.com/1007-9327/full/v27/i35/5978.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v27.i35.5978 |
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