Abstract
Background
Artificial neural networks (ANNs) have been applied to many prediction and classification problems, and could also be used to develop a prediction model of survival outcomes for cancer patients.
Objective
The aim of this study is to develop a prediction model of survival outcomes for patients with gastric cancer using an ANN.
Methods
This study enrolled 1243 patients with stage IIA–IV gastric cancer who underwent D2 gastrectomy from January 2007 to June 2010. We used a recurrent neural network (RNN) to make the survival recurrent network (SRN), and patients were randomly sorted into a training set (80%) and a test set (20%). Fivefold cross-validation was performed with the training set, and the optimized model was evaluated with the test set. Receiver operating characteristic (ROC) curves and area under the curves (AUCs) were evaluated, and we compared the survival curves of the American Joint Committee on Cancer (AJCC) 8th stage groups with those of the groups classified by the SRN-predicted survival probability.
Results
The test data showed that the ROC AUC of the SRN was 0.81 at the fifth year. The SRN-predicted survival corresponded closely with the actual survival in the calibration curve, and the survival outcome could be more discriminately classified by using the SRN than by using the AJCC staging system.
Conclusion
SRN was a more powerful tool for predicting the survival rates of gastric cancer patients than conventional TNM staging, and may also provide a more flexible and expandable method when compared with fixed prediction models such as nomograms.
Similar content being viewed by others
References
Park JY, von Karsa L, Herrero R. Prevention strategies for gastric cancer: a global perspective. Clin Endosc. 2014;47:478–489.
Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55:74–108.
Washington K. 7th edition of the AJCC cancer staging manual: stomach. Ann Surg Oncol. 2010;17:3077–3079.
Japanese Gastric Cancer Association. Japanese classification of gastric carcinoma: 3rd English edition. Gastric Cancer. 2011;14:101–112.
Chen D, Jiang B, Xing J, et al. Validation of the memorial Sloan-Kettering Cancer Center nomogram to predict disease-specific survival after R0 resection in a Chinese gastric cancer population. PLoS One. 2013;8:e76041.
Kattan MW, Karpeh MS, Mazumdar M, Brennan MF. Postoperative nomogram for disease-specific survival after an R0 resection for gastric carcinoma. J Clin Oncol. 2003;21:3647–3650.
Baxt WG. Application of artificial neural networks to clinical medicine. Lancet. 1995;346:1135–1138.
Grossi E, Mancini A, Buscema M. International experience on the use of artificial neural networks in gastroenterology. Dig Liver Dis. 2007;39:278–285.
Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 1996;49:1225–1231.
Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer. 1997;79:857–862.
Biglarian A, Hajizadeh E, Kazemnejad A, Zali MR. Application of artificial neural network in predicting the survival rate of gastric cancer patients. Iran J Public Health. 2011;40:80–86.
Zhu L, Luo W, Su M, Wei H, Wei J, Zhang X, et al. Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients. Biomed Rep. 2013;1:757–760.
Hush DR, Horne BG. Progress in supervised neural networks. IEEE Signal Process Mag. 1993;10:8–39.
Larose DT. Discovering knowledge in data: an introduction to data mining. Hoboken, NJ: Wiley, 2005:90–106.
Anguita D, Ghelardoni L, Ghio A, Oneto L, Ridella S. The ‘K’ in K-fold Cross Validation. ESANN 2012 proceedings. ESANN 2012 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 25–27 April 2012; Bruges: pp. 441–446.
Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L. The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform. 2005;38:404–15.
Peeters KC, Kattan MW, Hartgrink HH, Kranenbarg EK, Karpeh MS, Brennan MF, van de Velde CJ. Validation of a nomogram for predicting disease-specific survival after an R0 resection for gastric carcinoma. Cancer. 2005;103:702–707.
AR Novotny, C Schuhmacher, R Busch, MW Kattan, MF Brennan, JR Siewert. Predicting individual survival after gastric cancer resection: validation of a U.S.-derived nomogram at a single high-volume center in Europe. Ann Surg. 2006;243:74–81.
Strong VE, Song KY, Park CH, et al. Comparison of gastric cancer survival following R0 resection in the United States and Korea using an internationally validated nomogram. Ann Surg. 2010;251:640–646.
Ashfaq A, Kidwell JT, McGhan LJ, et al. Validation of a gastric cancer nomogram using a cancer registry. J Surg Oncol. 2015;112:377–380.
Kim JH, Kim HS, Seo WY, et al. External validation of nomogram for the prediction of recurrence after curative resection in early gastric cancer. Ann Oncol. 2012;23:361–367.
Song KY, Park YG, Jeon HM, Park CH. A nomogram for predicting individual survival of patients with gastric cancer who underwent radical surgery with extended lymph node dissection. Gastric Cancer. 2014;17:287–293.
Eom BW, Ryu KW, Nam BH, et al. Survival nomogram for curatively resected Korean gastric cancer patients: multicenter retrospective analysis with external validation. PLoS One. 2015;10:e0119671.
Brennan MF. Current status of surgery for gastric cancer: a review. Gastric Cancer. 2005;8:64–70.
Fondevila C, Metges JP, Fuster J, et al. p53 and VEGF expression are independent predictors of tumour recurrence and survival following curative resection of gastric cancer. Br J Cancer. 2004;90:206–215.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author Contributions
M-GC and SWS contributed to the concept of this study and revised it critically; SEO and SWS collected, analyzed the data and drafted the work; and TSS, JMB, and SK ensured that questions related to the accuracy or integrity of any part of the work were appropriately investigated and resolved. All authors gave approval for the final version to be published.
Disclosures
:Sung Eun Oh, Sung Wook Seo, Min-Gew Choi, Tae Sung Sohn, Jae Moon Bae, and Sung Kim report no proprietary or commercial interest in any product mentioned or concept discussed in this article.
Author information
Authors and Affiliations
Corresponding authors
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Oh, S.E., Seo, S.W., Choi, MG. et al. Prediction of Overall Survival and Novel Classification of Patients with Gastric Cancer Using the Survival Recurrent Network. Ann Surg Oncol 25, 1153–1159 (2018). https://doi.org/10.1245/s10434-018-6343-7
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1245/s10434-018-6343-7