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
Processing time: 116 Days and 3.8 Hours
Abstract
BACKGROUND

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

AIM

To improve the diagnostic value of blood markers for GC.

METHODS

We used a multiparameter joint analysis of 77 indexes of malignant GC and gastric polyp (GP), 64 indexes of GC and healthy controls (Ctrls).

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. 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. For the ability to distinguish between Ctrls vs GC, GP vs GC, artificial neural networks had better diagnostic value when compared with classification tree, binary logistic regression, and discriminant analysis. When compared Ctrl and GC, the overall prediction accuracy was 92.9%, and the AUC was 0.992 (0.980, 1.000). When compared GP and GC, the overall prediction accuracy was 77.9%, and the AUC was 0.969 (0.948, 0.990).

CONCLUSION

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.

Keywords: Gastric cancer; Gastric polyp; Serum; Artificial neural network; Detection

Core tip: In this study, we aimed to improve the diagnostic value of blood markers for gastric cancer. By comparing the binary logistic regression, discriminant analysis, classification tree and artificial neural network analysis, finally, artificial neural networks had better diagnostic value. When compared healthy control and gastric cancer, gastric polyp and gastric cancer, the area under the curve was 0.992 (0.980, 1.000) and 0.969 (0.948, 0.990), respectively. Based on artificial neural network and serum index, a novel diagnostic model for gastric cancer may be provided for clinical practice.