Copyright
©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jul 6, 2019; 7(13): 1611-1622
Published online Jul 6, 2019. doi: 10.12998/wjcc.v7.i13.1611
Published online Jul 6, 2019. doi: 10.12998/wjcc.v7.i13.1611
Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers
Rui-Quan Zhou, Rong Liu, School of Medicine, Nankai University, Tianjin 300071, China
Hong-Chen Ji, Qu Liu, Chun-Yu Zhu, Rong Liu, The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing 100853, China
Author contributions: Zhou RQ, Ji HC and Liu Q contributed equally to this study; Zhou RQ, Ji HC and Liu R contributed to study conception and design; Ji HC, Liu Q and Zhu CY contributed to data acquisition, analysis, and interpretation; Zhou RQ contributed to writing of the article; Liu Q and Liu R contributed to the editing and final approval of the article.
Supported by “Miaopu” Innovation Foundation of the Chinese PLA General Hospital , No. 17KMM07 .
Institutional review board statement: The present study was approved by the Ethics Committee of the PLA General Hospital, China and adhered to the tenets of the Declaration of Helsinki.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: None.
Data sharing statement: No additional data are available.
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/
Corresponding author: Rong Liu, MD, PhD, Professor, School of Medicine, Nankai University; The Second Department of Hepatobiliary Surgery, Chinese PLA General Hospital, No. 28, Fuxing Road, Beijing 100853, China. liurong301@126.com
Telephone: +86-10-66937591 Fax: +86-10-66937591
Received: March 28, 2019
Peer-review started: March 28, 2019
First decision: May 15, 2019
Revised: May 15, 2019
Accepted: May 16, 2019
Article in press: May 17, 2019
Published online: July 6, 2019
Processing time: 101 Days and 0.2 Hours
Peer-review started: March 28, 2019
First decision: May 15, 2019
Revised: May 15, 2019
Accepted: May 16, 2019
Article in press: May 17, 2019
Published online: July 6, 2019
Processing time: 101 Days and 0.2 Hours
Core Tip
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 made sure that the trained models gave 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.