Publication Name	Artificial Intelligence in Cancer
Manuscript ID	54872
Country	Japan

Received	2020-03-21 00:21
Peer-Review Started	2020-03-21 00:21
To Make the First Decision
Return for Revision	2020-04-22 00:00
Revised	2020-05-02 02:05
Second Decision	2020-06-05 08:13
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2020-06-07 03:51
Articles in Press	2020-06-07 03:51
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2020-07-02 08:23
Publish the Manuscript Online	2020-07-07 10:45

ISSN	2644-3228 (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	Pathology
Manuscript Type	Basic Study
Article Title	Impact of blurs on machine-learning aided digital pathology image analysis
Manuscript Source	Invited Manuscript
All Author List	Maki Ogura, Tomoharu Kiyuna and Hiroshi Yoshida
Funding Agency and Grant Number
Corresponding Author	Hiroshi Yoshida, MD, PhD, Staff Physician, Department of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 1040045, Japan. hiroyosh@ncc.go.jp
Key Words	Machine learning; Digital pathology image; Automated image analysis; Blur; Color; Reproducibility
Core Tip	Little attention has been paid to the reproducibility of machine learning (ML)-based histological classification in heterochronously obtained Digital pathology images (DPIs) of the same hematoxylin and eosin slide. This study elucidated the frequency and preventable causes of discordant classification results of DPI analysis using ML for the heterochronously obtained DPIs. We observed discordant classification results in 23.1% of the paired DPIs obtained by two independent scans of the same microscope slide. The group with discordant classification results showed a significantly higher blur index than the other group. Our results suggest that differences in the blur of the paired DPIs may cause discordant classification results.
Publish Date	2020-07-07 10:45
Citation	Ogura M, Kiyuna T, Yoshida H. Impact of blurs on machine-learning aided digital pathology image analysis. Artif Intell Cancer 2020; 1(1): 31-38
URL	https://www.wjgnet.com/2644-3228/full/v1/i1/31.htm
DOI	https://dx.doi.org/10.35713/aic.v1.i1.31

Full Article (PDF)	AIC-1-31.pdf
Full Article (Word)	AIC-1-31.docx
Manuscript File	54872-Review.docx
Answering Reviewers	54872-Answering reviewers.pdf
Audio Core Tip	54872-Audio core tip.m4a
Biostatistics Review Certificate	54872-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	54872-Conflict-of-interest statement.pdf
Copyright License Agreement	54872-Copyright license agreement.pdf
Institutional Review Board Approval Form or Document	54872-Institutional review board statement.pdf
Non-Native Speakers of English Editing Certificate	54872-Language certificate.pdf
Peer-review Report	54872-Peer-review(s).pdf
Scientific Misconduct Check	54872-Scientific misconduct check.pdf
Scientific Editor Work List	54872-Scientific editor work list.pdf