Annals of Surgical Oncology

, Volume 25, Issue 5, pp 1153–1159 | Cite as

Prediction of Overall Survival and Novel Classification of Patients with Gastric Cancer Using the Survival Recurrent Network

Gastrointestinal Oncology
  • 151 Downloads

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.

Notes

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.

Supplementary material

10434_2018_6343_MOESM1_ESM.tif (18.5 mb)
Supplementary material 1 (TIFF 18952 kb)
10434_2018_6343_MOESM2_ESM.docx (16 kb)
Supplementary material 2 (DOCX 15 kb)

References

  1. 1.
    Park JY, von Karsa L, Herrero R. Prevention strategies for gastric cancer: a global perspective. Clin Endosc. 2014;47:478–489.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin. 2005;55:74–108.CrossRefPubMedGoogle Scholar
  3. 3.
    Washington K. 7th edition of the AJCC cancer staging manual: stomach. Ann Surg Oncol. 2010;17:3077–3079.CrossRefPubMedGoogle Scholar
  4. 4.
    Japanese Gastric Cancer Association. Japanese classification of gastric carcinoma: 3rd English edition. Gastric Cancer. 2011;14:101–112.Google Scholar
  5. 5.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    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.CrossRefPubMedGoogle Scholar
  7. 7.
    Baxt WG. Application of artificial neural networks to clinical medicine. Lancet. 1995;346:1135–1138.CrossRefPubMedGoogle Scholar
  8. 8.
    Grossi E, Mancini A, Buscema M. International experience on the use of artificial neural networks in gastroenterology. Dig Liver Dis. 2007;39:278–285.CrossRefPubMedGoogle Scholar
  9. 9.
    Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 1996;49:1225–1231.CrossRefPubMedGoogle Scholar
  10. 10.
    Burke HB, Goodman PH, Rosen DB, et al. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer. 1997;79:857–862.CrossRefPubMedGoogle Scholar
  11. 11.
    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.PubMedPubMedCentralGoogle Scholar
  12. 12.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Hush DR, Horne BG. Progress in supervised neural networks. IEEE Signal Process Mag. 1993;10:8–39.CrossRefGoogle Scholar
  14. 14.
    Larose DT. Discovering knowledge in data: an introduction to data mining. Hoboken, NJ: Wiley, 2005:90–106.CrossRefGoogle Scholar
  15. 15.
    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.Google Scholar
  16. 16.
    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.CrossRefPubMedGoogle Scholar
  17. 17.
    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.CrossRefPubMedGoogle Scholar
  18. 18.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    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.CrossRefPubMedGoogle Scholar
  20. 20.
    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.CrossRefPubMedGoogle Scholar
  21. 21.
    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.CrossRefPubMedGoogle Scholar
  22. 22.
    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.CrossRefPubMedGoogle Scholar
  23. 23.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Brennan MF. Current status of surgery for gastric cancer: a review. Gastric Cancer. 2005;8:64–70.CrossRefPubMedGoogle Scholar
  25. 25.
    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.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Society of Surgical Oncology 2018

Authors and Affiliations

  1. 1.Department of Surgery, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea
  2. 2.Department of Orthopedic Surgery, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulKorea

Personalised recommendations