Publication Name	World Journal of Translational Medicine
Manuscript ID	60193
Country	United States

Received	2020-10-19 18:32
Peer-Review Started	2020-10-19 18:32
To Make the First Decision
Return for Revision	2020-11-16 22:05
Revised	2020-12-22 05:05
Second Decision	2021-02-26 07:50
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2021-03-02 03:17
Articles in Press	2021-03-02 03:17
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2021-03-08 02:31
Publish the Manuscript Online	2021-03-08 11:20

ISSN	2220-6132 (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.
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Website	http://www.wjgnet.com

Category	Oncology
Manuscript Type	Editorial
Article Title	Machine intelligence for precision oncology
Manuscript Source	Invited Manuscript
All Author List	Nelson S Yee
ORCID	Author(s) ORCID Number Nelson S Yee http://orcid.org/0000-0002-1457-9047
Funding Agency and Grant Number
Corresponding Author	Nelson S Yee, BPharm, FACP, MD, PhD, Associate Professor, Attending Doctor, Department of Medicine, The Pennsylvania State University College of Medicine, Penn State Cancer Institute, Penn State Health Milton S. Hershey Medical Center, 500 University Drive, Hershey, PA 17033-0850, United States. nyee@pennstatehealth.psu.edu
Key Words	Artificial intelligence; Deep learning; Machine learning; Precision oncology; Radiomics; Radiogenomics
Core Tip	Artificial intelligence represents the future of healthcare particularly precision oncology for prevention, detection, risk assessment, and treatment of cancer. Application of machine learning- and deep learning-based algorithms in translational research has been demonstrated to improve accuracy of cancer diagnosis and anti-cancer drug development. Multi-disciplinary collaboration with resolution of ethical and regulatory issues of multi-modal machine intelligence are indicated for implementation of computer-assisted clinical decision on individualized patient management.
Publish Date	2021-03-08 11:20
Citation	Yee NS. Machine intelligence for precision oncology. World J Transl Med 2021; 9(1): 1-10
URL	https://www.wjgnet.com/2220-6132/full/v9/i1/1.htm
DOI	https://dx.doi.org/10.5528/wjtm.v9.i1.1

Full Article (PDF)	WJTM-9-1.pdf
Full Article (Word)	WJTM-9-1.docx
Manuscript File	60193_Auto_Edited_LM.docx
Answering Reviewers	60193-Answering reviewers.pdf
Audio Core Tip	60193-Audio core tip.mp3
Conflict-of-Interest Disclosure Form	60193-Conflict-of-interest statement.pdf
Copyright License Agreement	60193-Copyright license agreement.pdf
Peer-review Report	60193-Peer-review(s).pdf
Scientific Misconduct Check	60193-Scientific misconduct check.pdf
Scientific Editor Work List	60193-Scientific editor work list.pdf
CrossCheck Report	60193-CrossCheck report.pdf