Publication Name	World Journal of Gastroenterology
Manuscript ID	67143
Country	Hungary

Received	2021-04-30 07:39
Peer-Review Started	2021-04-30 07:41
First Decision by Editorial Office Director	2021-06-23 01:45
Return for Revision	2021-06-23 01:45
Revised	2021-07-07 16:27
Publication Fee Transferred
Second Decision by Editor	2021-08-24 03:27
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2021-08-25 08:15
Articles in Press	2021-08-25 08:15
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-09-10 14:23
Publish the Manuscript Online	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/
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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
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	Author(s) ORCID Number Róbert Stollmayer http://orcid.org/0000-0003-4673-7588 Bettina K Budai http://orcid.org/0000-0002-3982-7887 Ambrus Tóth http://orcid.org/0000-0002-1150-957X Ildikó Kalina http://orcid.org/0000-0002-2647-9123 Erika Hartmann http://orcid.org/0000-0001-6073-9286 Péter Szoldán http://orcid.org/0000-0002-3808-8541 Viktor Bérczi http://orcid.org/0000-0003-4386-2527 Pál Maurovich-Horvat http://orcid.org/0000-0003-0885-736X Pál N Kaposi http://orcid.org/0000-0002-7150-3495
Funding Agency and Grant Number
Corresponding Author	Bettina K Budai, MD, N/A, 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	Focal nodular hyperplasia; Hepatocellular carcinoma; Densely connected convolutional network; Hepatocyte-specific contrast; Multi-parametric magnetic resonance imaging; Artificial intelligence
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

Full Article (PDF)	WJG-27-5978.pdf
Full Article (Word)	WJG-27-5978.docx
Manuscript File	67143_Auto_Edited-JPY.docx
Answering Reviewers	67143-Answering reviewers.pdf
Audio Core Tip	67143-Audio core tip.mp3
Biostatistics Review Certificate	67143-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	67143-Conflict-of-interest statement.pdf
Copyright License Agreement	67143-Copyright license agreement.PDF
Signed Informed Consent Form(s) or Document(s)	67143-Informed consent statement.pdf
Institutional Review Board Approval Form or Document	67143-Institutional review board statement.pdf
Non-Native Speakers of English Editing Certificate	67143-Language certificate.pdf
Peer-review Report	67143-Peer-review(s).pdf
Scientific Misconduct Check	67143-Bing-Fan JR-2.png
Scientific Misconduct Check	67143-Scientific misconduct check.pdf
Scientific Editor Work List	67143-Scientific editor work list.pdf
CrossCheck Report	67143-CrossCheck report.pdf
CrossCheck Report	67143-CrossCheck.png