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Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer

Authors
  • Que, Si-Jin
  • Chen, Qi-Yue
  • Qing-Zhong,
  • Liu, Zhi-Yu
  • Wang, Jia-Bin
  • Lin, Jian-Xian
  • Lu, Jun
  • Cao, Long-Long
  • Lin, Mi
  • Tu, Ru-Hong
  • Huang, Ze-Ning
  • Lin, Ju-Li
  • Zheng, Hua-Long
  • Li, Ping
  • Zheng, Chao-Hui
  • Huang, Chang-Ming
  • Xie, Jian-Wei
Type
Published Article
Journal
World Journal of Gastroenterology
Publisher
Baishideng Publishing Group Inc
Publication Date
Nov 21, 2019
Volume
25
Issue
43
Pages
6451–6464
Identifiers
DOI: 10.3748/wjg.v25.i43.6451
PMID: 31798281
PMCID: PMC6881508
Source
PubMed Central
Keywords
License
Green

Abstract

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). AIM 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. 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. The 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 (cTNM) and pathological TNM (pTNM) staging through the receiver operating characteristic curve, Akaike information criterion index, Harrell's C index, and likelihood ratio chi-square. 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 is similar to that of pTNM. CONCLUSION The preope-ANN model can accurately predict the long-term survival of GC patients, and its predictive efficiency is not inferior to that of pTNM stage.

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