Song WY, Zhang X, Zhang Q, Zhang PJ, Zhang R. Clinical value evaluation of serum markers for early diagnosis of colorectal cancer. World J Gastrointest Oncol 2020; 12(2): 219-227 [PMID: 32104552 DOI: 10.4251/wjgo.v12.i2.219]
Corresponding Author of This Article
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
Research Domain of This Article
Oncology
Article-Type of This Article
Observational Study
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Song WY, Zhang X, Zhang Q, Zhang PJ, Zhang R. Clinical value evaluation of serum markers for early diagnosis of colorectal cancer. World J Gastrointest Oncol 2020; 12(2): 219-227 [PMID: 32104552 DOI: 10.4251/wjgo.v12.i2.219]
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 byNational 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.
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 Processing time: 59 Days and 2.2 Hours
Abstract
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.
AIM
To develop a new examination method based on the serum biomarker panel for the early detection of CRC.
METHODS
Three hundred and fifty cases of CRC, 300 cases of colorectal polyps and 360 cases of normal controls. Combined with the results of area under curve (AUC) and correlation analysis, the binary Logistic regression analysis of the remaining indexes which is in accordance with the requirements was carried out, and discriminant analysis, classification tree and artificial neural network analysis were used to analyze the remaining indexes at the same time.
RESULTS
By comparison of these methods, we obtained the ability to distinguish CRC from healthy control group, malignant disease group and benign disease group. Artificial neural network had the best diagnostic value when compared with binary logistic regression, discriminant analysis, and classification tree. The AUC of CRC and the control group was 0.992 (0.987, 0.997), sensitivity and specificity were 98.9% and 95.6%. The AUC of the malignant disease group and benign group was 0.996 (0.992, 0.999), sensitivity and specificity were 97.4% and 96.7%.
CONCLUSION
Artificial neural network diagnosis method can improve the sensitivity and specificity of the diagnosis of CRC, and a novel assistant diagnostic method was built for the early detection of CRC.
Core tip: We aimed to combine the serum index together by several multiparameter method, such as, the binary logistic regression, discriminant analysis, classification tree and artificial neural network analysis. Finally, a multiparameter diagnostic model based on artificial neural network which showed better diagnostic value was built for the early detection of colorectal cancer.
