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11/18/2020 2:20:37 PM | Browse: 15 | Download: 14
Publication Name World Journal of Clinical Oncology
Manuscript ID 57779
Country/Territory United States
Received
2020-06-24 04:03
Peer-Review Started
2020-06-24 04:04
To Make the First Decision
Return for Revision
2020-09-18 16:31
Revised
2020-10-06 02:09
Second Decision
2020-10-20 10:32
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2020-10-20 21:40
Articles in Press
2020-10-20 21:40
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2020-11-16 13:05
Publish the Manuscript Online
2020-11-18 14:20
ISSN 2218-4333 (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
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Dentistry, Oral Surgery and Medicine
Manuscript Type Retrospective Study
Article Title Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival
Manuscript Source Unsolicited Manuscript
All Author List Man Hung, Jungweon Park, Eric S Hon, Jerry Bounsanga, Sara Moazzami, Bianca Ruiz-Negrón and Dawei Wang
ORCID
Author(s) ORCID Number
Man Hung http://orcid.org/0000-0003-2827-3740
Jungweon Park http://orcid.org/0000-0001-7930-6026
Eric S Hon http://orcid.org/0000-0002-8779-4397
Jerry Bounsanga http://orcid.org/000-0001-6852-4650
Sara Moazzami http://orcid.org/0000-0003-2403-3141
Bianca Ruiz-Negrón http://orcid.org/0000-0001-6354-1582
Dawei Wang http://orcid.org/0000-0003-3842-4258
Funding Agency and Grant Number
Corresponding Author Man Hung, PhD, Professor, Research Dean, College of Dental Medicine, Roseman University of Health Sciences, 10894 S River Front Parkway, South Jordan, UT 84095, United States. mhung@roseman.edu
Key Words Oral cancer survival; Machine learning; Artificial intelligence; Dental medicine; Public health; Surveillance, Epidemiology, and End Results
Core Tip Oral cancer is the sixth most prevalent cancer worldwide. The goal of this study was to come up with machine learning algorithms to predict the length of oral cancer survival and to explore the most important factors that were responsible for it. Age at diagnosis, primary cancer site, tumor size and year of diagnosis were found to be the most important factors predictive of oral cancer survival. Year of diagnosis represents an important new discovery in the literature. Using artificial intelligence, we developed a tool that can be used for oral cancer survival prediction and for medical decision making.
Publish Date 2020-11-18 14:20
Citation Hung M, Park J, Hon ES, Bounsanga J, Moazzami S, Ruiz-Negrón B, Wang D. Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival. World J Clin Oncol 2020; 11(11): 918-934
Url https://www.wjgnet.com/2218-4333/full/v11/i11/918.htm
DOI https://dx.doi.org/10.5306/wjco.v11.i11.918
Full Article (PDF) WJCO-11-918.pdf
Full Article (Word) WJCO-11-918.docx
Manuscript File 57779_Auto_Edited.docx
Answering Reviewers 57779-Answering reviewers.pdf
Audio Core Tip 57779-Audio core tip.mp4
Biostatistics Review Certificate 57779-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form 57779-Conflict-of-interest statement.pdf
Copyright License Agreement 57779-Copyright license agreement.pdf
Signed Informed Consent Form(s) or Document(s) 57779-Informed consent statement.pdf
Institutional Review Board Approval Form or Document 57779-Institutional review board statement.pdf
Peer-review Report 57779-Peer-review(s).pdf
Scientific Misconduct Check 57779-Bing-Ma YJ-1.jpg
Scientific Misconduct Check 57779-Bing-Chen XF-2.jpg
Scientific Misconduct Check 57779-Scientific misconduct check.pdf
Scientific Editor Work List 57779-Scientific editor work list.pdf