Retrospective Study
Copyright ©The Author(s) 2019.
World J Gastroenterol. Nov 21, 2019; 25(43): 6451-6464
Published online Nov 21, 2019. doi: 10.3748/wjg.v25.i43.6451
Figure 3
Figure 3 Subgroup survival curves of the preoperative artificial neural network. A: The survival curve of each subgroup of the preoperative artificial neural network model showed good discrimination in the training and testing sets (P < 0.05). B: The survival analysis of the training set showed that the substages of the TNM system were well differentiated (P < 0.05). Preope-ANN: Preoperative artificial neural network.

  • Citation: Que SJ, Chen QY, Qing-Zhong, Liu ZY, Wang JB, Lin JX, Lu J, Cao LL, Lin M, Tu RH, Huang ZN, Lin JL, Zheng HL, Li P, Zheng CH, Huang CM, Xie JW. Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer. World J Gastroenterol 2019; 25(43): 6451-6464
  • URL: https://www.wjgnet.com/1007-9327/full/v25/i43/6451.htm
  • DOI: https://dx.doi.org/10.3748/wjg.v25.i43.6451