Observational Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Apr 15, 2020; 12(4): 483-491
Published online Apr 15, 2020. doi: 10.4251/wjgo.v12.i4.483
Evaluation of the value of multiparameter combined analysis of serum markers in the early diagnosis of gastric cancer
Zhi-Guo Zhang, Liang Xu, Peng-Jun Zhang, Lei Han
Zhi-Guo Zhang, Lei Han, Department of Oncology, Beijing Daxing District People’s Hospital, Beijing 102600, China
Liang Xu, Peng-Jun Zhang, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Interventional Therapy Department, Peking University Cancer Hospital and Institute, Beijing 100142, China
Author contributions: Zhang ZG, Zhang PJ, and Han L designed the study; Zhang ZG and Xu L performed the research; Zhang ZG, Zhang PJ, and Han L analyzed the date; Zhang ZG wrote the paper; Zhang PJ and Han L revised the manuscript for final submission; Zhang ZG and Xu L contributed equally to this study; Zhang PJ and Han L are the co-corresponding authors.
Supported by the National Key R& D Program of China, No. 2016YFC0106604; and National Natural Science Foundation of China, No. 81502591.
Institutional review board statement: The study was reviewed and approved by the Beijing Daxing District People’s Hospital review board.
Informed consent statement: All study participants or their legal guardian provided written informed consent prior to study enrollment.
Conflict-of-interest statement: We declare that we have no financial or personal relationships with other individuals or organizations that can inappropriately influence our work and that there is no professional or other personal interest of any nature in any product, service and/or company that could be construed as influencing the position presented in or the review of the manuscript.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement – checklist of items, and the manuscript was prepared and revised according to the STROBE Statement – checklist of items.
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/
Corresponding author: Lei Han, MD, Professor, Department of Oncology, Beijing Daxing District People’s Hospital, No. 26 Huangcun West Street, Beijing 102600, China. zlk60283168@163.com
Received: December 21, 2019
Peer-review started: December 21, 2019
First decision: January 19, 2020
Revised: February 5, 2020
Accepted: March 22, 2020
Article in press: March 22, 2020
Published online: April 15, 2020
ARTICLE HIGHLIGHTS
Research background

Tumor markers are increased in the blood in early gastric cancer (GC). The levels of these markers have been used as important indexes for GC screening, early diagnosis and prognostic evaluation.

Research motivation

Specific tumor markers have not yet been discovered. Diagnosis based on a single tumor marker has limited significance. The detection rate of GC is still very low.

Research objectives

In this study, we aimed to improve the diagnostic value of blood markers for GC.

Research methods

In this study, to distinguish between healthy controls (Ctrls) vs GC, gastric polyp (GP) and GC, we analyzed the routine blood detection indexes of GC diagnosis by using binary logistic regression, discriminant analysis, classification tree and artificial neural network.

Research results

By analyzing the data, there are 27 indexes in the final Ctrls vs GC with P values < 0.01, the area under the curve (AUC) of albumin is the largest in Ctrls vs GC, and the AUC was 0.907. For 30 indexes in GP vs GC have P values < 0.01. Among them, the D-dimer showed an AUC of 0.729. The 27 indexes in Ctrls vs GC and 30 indexes in GP vs GC were used for binary logistic regression, discriminant analysis, classification tree analysis and artificial neural network analysis model. The overall prediction accuracy was 92.9%, and the AUC was 0.992 (0.980, 1.000).

Research conclusions

The diagnostic effect of multi-parameter joint artificial neural networks analysis is significantly better than the single-index test diagnosis, and it may provide an assistant method for the detection of GC.

Research perspectives

We propose that the artificial neural network analysis method has good prospects for the multi-index joint detection of tumors, and further research in this area should be carried out in the future.