Observational Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Feb 15, 2020; 12(2): 219-227
Published online Feb 15, 2020. doi: 10.4251/wjgo.v12.i2.219
Clinical value evaluation of serum markers for early diagnosis of colorectal cancer
Wen-Yue Song, Xin Zhang, Qi Zhang, Peng-Jun Zhang, Rong Zhang
Wen-Yue Song, Qi Zhang, Rong Zhang, School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang 110016, Liaoning Province, China
Wen-Yue Song, Xin Zhang, Qi Zhang, 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: Song WY, Zhang PJ, and Zhang R designed the study; Song WY, Zhang X, and Zhang Q performed the research; Song WY, Zhang PJ , and Zhang R analyzed the date; Song WY wrote the paper; Zhang PJ and Zhang R revised the manuscript for final submission; Song WY and Zhang X contributed equally to this study; Zhang PJ and Zhang R are the co-corresponding authors.
Supported by National Key R and D Program of China, No. 2016YFC0106604; National Natural Science Foundation of China, No. 81502591.
Institutional review board statement: The study was reviewed and approved by the Peking University Cancer Hospital and Institute 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: Rong Zhang, PhD, Professor, Teacher, School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103 Wenhua Road, Shenyang 110016, Liaoning Province, China. zhangrong7110@163.com
Received: December 18, 2019
Peer-review started: December 18, 2019
First decision: January 13, 2020
Revised: January 17, 2020
Accepted: February 8, 2020
Article in press: February 8, 2020
Published online: February 15, 2020
Research background

Early screening for colorectal cancer (CRC) is important in clinical practice. However, the currently methods are inadequate because of high cost and low diagnostic value.

Research motivation

Blood-based screening method attract the public because of their noninvasive, and multiparameter method was demonstrated to increase the diagnostic value.

Research objectives

We aimed to conduct a retrospective analysis. By multiparameter methods combined detection were used to determine the tumor marker diagnostic value for detection of CRC.

Research methods

350 CRC, 300 colorectal polyps and 360 normal controls were enrolled. Combined with the results of area under curve, the binary Logistic regression analysis, and discriminant analysis, classification tree and artificial neural network were used to analyze the diagnostic value.

Research results

For distinguishing CRC from healthy control group, malignant disease group and benign disease group. Artificial neural network had the best diagnostic value when compared with the other methods. The area under the curve of CRC and the control group was 0.992 (0.987, 0.997), sensitivity and specificity were 98.9% and 95.6%. The area under the curve of the malignant disease group and benign group was 0.996 (0.992, 0.999), sensitivity and specificity were 97.4% and 96.7%.

Research conclusions

Artificial neural network diagnosis method can provide a novel assistant diagnostic method was built for the early detection of CRC.

Research perspectives

Although we have built a multiparameter diagnostic model, the sample size was relatively small, and the diagnostic model has not been validated. Multi-center and larger sample size to validate its diagnostic value.