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7/6/2019 11:29:12 AM | Browse: 22 | Download: 42
Publication Name World Journal of Clinical Cases
Manuscript ID 47865
Country of Manuscript Source China
Received
2019-03-28 02:23
Peer-Review Started
2019-03-28 07:42
To Make the First Decision
2019-05-15 07:51
Return for Revision
2019-05-15 08:00
Revised
2019-05-15 12:40
Second Decision
2019-05-16 06:21
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief
2019-05-16 08:47
Articles in Press
2019-05-16 08:47
Publication Fee Transferred
Edit the Manuscript by Language Editor
2019-05-31 17:30
Typeset the Manuscript
2019-07-05 01:47
Publish the Manuscript Online
2019-07-06 11:29
ISSN 2307-8960 (online)
Open Access This 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 Non Commercial (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) 2019. 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
Specialty Oncology
Manuscript type Retrospective Study
Article Title Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumors grade using biochemical and tumor markers
Manuscript Source Unsolicited Manuscript
All Author List Rui-Quan Zhou, Hong-Chen Ji, Qu Liu, Chun-Yu Zhu and Rong Liu
ORCID
Author(s) ORCID Number
Rui-Quan Zhou http://orcid.org/0000-0002-6382-0562
Hong-Chen Ji http://orcid.org/0000-0003-3838-7354
Qu Liu http://orcid.org/0000-0001-7486-3288
Chun-Yu Zhu http://orcid.org/0000-0002-4736-3381
Rong Liu http://orcid.org/0000-0001-5170-6474
Funding Agency and Grant Number
Funding Agency Grant Number
“Miaopu” innovation foundation of the Chinese PLA General Hospital 17KMM07
Correspondence To Rong Liu, PhD, Doctor, Professor, School of Medicine, Nankai University; The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing 100853, China. liurong@301hospital.com.cn
Keywords Machine learning; Pancreatic neuroendocrine tumors; Tumor grade; Biochemical indexes; Tumor markers
Core Tip In this study, we provide a machine learning approach to predict the grade of pancreatic neuroendocrine tumors (PNETs) using combined clinical data. We design a method of minimum P for the Chi-square test to maximize differences between groups, which benefited the model’s construction. Then, we proposed four classical supervised machine learning models by using biochemical and tumor markers. After the tuning, training and testing of the models, we make sure that the trained models could give stable results. In general, the result of our study provided a non-invasive way to judge the condition of PNETs and offers a reference for treatment.
Publish Date 2019-07-06 11:29
Citation Zhou RQ, Ji HC, Liu Q, Zhu CY, Liu R. Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumors grade using biochemical and tumor markers. World J Clin Cases 2019; 7(13): 1611-1622
Url https://www.wjgnet.com/2307-8960/full/v7/i13/1611.htm
DOI https://dx.doi.org/10.12998/wjcc.v7.i13.1611
Full Article (PDF) WJCC-7-1611.pdf
Full Article (Word) WJCC-7-1611.docx
Manuscript File FP6859_de26_CE1MS_edit2.docx
Answering Reviewers 47865-Answering reviewers.pdf
Audio Core Tip 47865-Audio core tip.mp3
Biostatistics Review Certificate 47865-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form 47865-Conflict-of-interest statement.pdf
Copyright License Agreement 47865-Copyright license agreement.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s) 47865-Grant application form(s).pdf
Signed Informed Consent Form(s) or Document(s) 47865-Informed consent statement.pdf
Institutional Review Board Approval Form or Document 47865-Institutional review board statement.pdf
Non-Native Speakers of English Editing Certificate 47865-Language certificate.pdf
Peer-review Report 47865-Peer-review(s).pdf
Scientific Misconduct Check 47865-Scientific misconduct check.pdf
Scientific Editor Work List 47865-Scientific editor work list.pdf