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©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer
Si-Jin Que, Qi-Yue Chen, Qing-Zhong, Zhi-Yu Liu, Jia-Bin Wang, Jian-Xian Lin, Jun Lu, Long-Long Cao, Mi Lin, Ru-Hong Tu, Ze-Ning Huang, Ju-Li Lin, Hua-Long Zheng, Ping Li, Chao-Hui Zheng, Chang-Ming Huang, Jian-Wei Xie
Si-Jin Que, Qi-Yue Chen, Qing-Zhong, Zhi-Yu Liu, Jia-Bin Wang, Jian-Xian Lin, Jun Lu, Long-Long Cao, Mi Lin, Ru-Hong Tu, Ze-Ning Huang, Ju-Li Lin, Hua-Long Zheng, Ping Li, Chao-Hui Zheng, Chang-Ming Huang, Jian-Wei Xie, Department of Gastric Surgery and Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
Si-Jin Que, Qi-Yue Chen, Qing-Zhong, Zhi-Yu Liu, Jia-Bin Wang, Jian-Xian Lin, Jun Lu, Long-Long Cao, Mi Lin, Ru-Hong Tu, Ze-Ning Huang, Ju-Li Lin, Hua-Long Zheng, Ping Li, Chao-Hui Zheng, Chang-Ming Huang, Jian-Wei Xie, Key Laboratory of Ministry of Education of Gastrointestinal Cancer, Fujian Medical University, Fuzhou 350108, Fujian Province, China
Si-Jin Que, Qi-Yue Chen, Qing-Zhong, Zhi-Yu Liu, Jia-Bin Wang, Jian-Xian Lin, Jun Lu, Long-Long Cao, Mi Lin, Ru-Hong Tu, Ze-Ning Huang, Ju-Li Lin, Hua-Long Zheng, Ping Li, Chao-Hui Zheng, Chang-Ming Huang, Jian-Wei Xie, Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou 350108, Fujian Province, China
Author contributions: Que SJ and Chen QY contributed equally to this work. Huang CM, Zheng CH, Chen QY, and Que SJ designed the research; Que SJ, Chen QY, Zhong Q, Liu ZY, Xie JW, Wang JB, Lin JX, Lu J, Cao LL, Lin M, Tu RH, Huang ZN, Lin JL, Zheng HL, Li P, Zheng CH, and Huang CM performed the research; Que SJ, Chen QY, Zhong Q, and Liu ZY contributed new reagents or analytic tools; Que SJ, Chen QY, Zhong Q, Liu ZY, Huang ZN, Lin JL, Zheng HL, Zheng CH, and Huang CM, Li P analyzed the data; Que SJ, Chen QY, Zhong Q, Liu ZY, Huang CM, and Li P wrote the paper.
Supported by the Scientific and Technological Innovation Joint Capital Projects of Fujian Province, No. 2016Y9031; the Construction Project of Fujian Province Minimally Invasive Medical Center, No. [2017]171; the General Project of Miaopu Scientific Research Fund of Fujian Medical University, No. 2015MP021; the Youth Project of Fujian Provincial Health and Family Planning Commission, No. 2016-1-41; the Fujian Province Medical Innovation Project, Chinese Physicians Association Young Physician Respiratory Research Fund, No. 2015-CXB-16; and the Fujian Science and Technology Innovation Joint Fund Project, No. 2017Y9004.
Institutional review board statement: This retrospective study was approved by the Ethics Committee of Fujian Medical University Union Hospital.
Informed consent statement: The 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: All authors declare that they have no conflicts of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article which 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: Jian-Wei Xie, MD, PhD, Doctor, Professor, Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29, Xinquan Road, Fuzhou 350001, Fujian Province, China.
xjwhw2019@163.com
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Received: August 19, 2019
Peer-review started: August 19, 2019
First decision: September 10, 2019
Revised: September 17, 2019
Accepted: October 17, 2019
Article in press: October 17, 2019
Published online: November 21, 2019
Processing time: 94 Days and 6.1 Hours
ARTICLE HIGHLIGHTS
Research background
Because of the powerful abilities of self-learning and handling complex biological information, artificial neural network (ANN) models have been widely applied to disease diagnosis, imaging analysis, and prognosis prediction. However, there has been no trained preoperative ANN (preope-ANN) model to preoperatively predict the prognosis of patients with gastric cancer (GC).
Research motivation
This study combined the preoperative blood biomarkers and preoperative tumor data to establish an ANN model in order to build a reliable preoperative prediction system that can achieve the same effect as postoperative TNM staging. The aim of this study was to evaluate the prognosis of patients with GC and to provide a reasonable individualized treatment plan for patients.
Research objectives
We aimed to establish a neural network model that can predict long-term survival of GC patients before surgery to evaluate the tumor condition before the operation.
Research methods
The clinicopathological data of 1608 GC patients treated from January 2011 to April 2015 at the Department of Gastric Surgery, Fujian Medical University Union Hospital were analyzed retrospectively. Patients were randomly divided into a training set (70%) for establishing a preope-ANN model and a testing set (30%). The prognostic evaluation ability of the preope-ANN model was compared with that of the American Joint Commission on Cancer (8th edition) clinical TNM stage (cTNM) and pathological TNM stage (pTNM) through the receiver operating characteristic curve, Akaike information criterion index, Harrell's C index, and likelihood ratio chi-square.
Research results
We used the variables that were statistically significant factors for the 3-year overall survival as input-layer variables to develop a preope-ANN in the training set. The survival curves within each score of the preope-ANN had good discrimination (P < 0.05). Comparing the preope-ANN model, cTNM, and pTNM in both the training and testing sets, the preope-ANN model was superior to cTNM in predictive discrimination (C index), predictive homogeneity (likelihood ratio chi-square), and prediction accuracy (area under the curve). The prediction efficiency of the preope-ANN model was similar to that of pTNM.
Research conclusions
The preope-ANN model can accurately predict the long-term survival of GC patients, and its predictive efficiency is not inferior to pTNM staging.
Research perspectives
This study for the first time confirmed that the preope-ANN is a novel and convenient prognostic model through the use of a large sample data size, which can effectively predict the prognosis of GC patients. In the clinic, preope-ANN can be considered as part of preoperative risk stratification to guide the individualized treatment of patients with GC. The next challenge is to establish a web version of the preope-ANN model that can be dynamically adjusted for the input of different sample data; with this approach, the model accuracy would be closer to the real value and more flexibly applied to the evaluation of clinical patients.
