DISCUSSION

To predict the postoperative complication after gastric cancer surgery, multivariate analysis like the logistic regression analysis model has been usually used in previous study(18-20).

This model assumes that the several variables selected contribute to postoperative complication as linear pattern. In clinical and biological system, however, nonlinear complex interactions among the variables have been widely known. Because this predictive system was made based on another institute, the results would show the difference with our data. In addition, the incidence of early gastric cancer was increased, the surgical environment has been changed, and minimally invasive surgery was adopted. Considering this multi-dimensional relationship, statistical predicting models like ANNs are supposed to be better than the linear model like POSSUM score. In our study, ANN that was developed by the training and test process with data from single center could show that it was a meaningful tool for predicting severe postoperative complication after gastric cancer surgery.

ANN has been used to make a prediction and decision in the various fields such as investments, science, engineering, marketing, and even gambling. Prediction system in these areas requires the consideration the complicated communications and self-learning process that means the development of new model by studying the past. The model using the system like human brain that is composed of a number of neurons, each connected to communicate, is suitable. ANN can process, analyze and learn the relationships in a lot of data quickly like human brain. In biologic and clinical system, the correlations between the variables are complicated as much as other fields. Therefore, several studies in the medical field have

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demonstrated the success of the ANN for prediction the outcomes(12,21). The major limitation of ANN is no satisfactory explanation of their behavior has been offered. This is a limitation if ANN model interpretability is important to clinician. However, medical variables have non-linear relationships in real world. Much of multivariate analysis models (like regression model) were assuming linear relationships between the independent and dependent variables. ANN has capability of approximation non-linear function of their variables.

Gastric cancer surgery is one of the most common surgeries in the worldwide. The multicenter randomized controlled clinical trial about standard surgical procedure including D2 lymphadenectomy for gastric cancer progressed in Western society has been showed morbidity and mortality rates of 43 - 46 percent and 10 - 13 percent, respectively(22,23).

However, result of large scale in Korea showed the lower morbidity and mortality rate(19,24), findings similar to the results of our study. Several reports described that it was caused by the difference of incidence, experience of surgeons, cancer characteristics including tumor location, and so on(25,26). We supposed that predicting system developed in Western countries would be inappropriate to apply to the patients in Eastern countries. In present study, only 6 (systolic blood presser, pulse rate, white cell count, urea, potassium, and hemoglobin) of 12 physiologic factors in POSSUM system were included in developing the 2010 ANN model using information gain ranking method (Table 3). As the merged data ANN model shown, there were small numbers of physiologic factors in POSSUM system identified to be significantly associated with the severe postoperative complication, such as systolic blood presser and pulse rate (p<0.001) (Table 7). As the Table 7 shown, the patients occurred diabetes, hypertension, kidney disease could increase the severe postoperative

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complications, the patients are going to perform D2 lymphadenectomy or total gastrectomy.

Therefore, we suggest that to prevent severe postoperative complications, it must pay more attention to these patients.

To assess the prediction accuracy of 2010 ANN model, we input the 2011 to 2015 data into the pre-established 2010 ANN model and the validation test for each year was conducted. As the results shown in Table 5, we identified the diagnostic value were decreased. Year by year, this could be caused by the initial modeling sample size was small, and collected data for a particular period of time. We assume that every year regularly updated date could improve diagnostic value. Therefore, we merged the data and remodeling the ANN. After training and learning performed the interval validation test again, as the merged data includes more recent years, the accuracy, sensitivity, specificity of the several years tended to improve slightly.

Therefore, the ANN model requires on-going learning is necessary. As the Table 7 shown, the merged data was getting bigger, more factors were identified to be significantly associated with the severe postoperative complication. Therefore, it suggests that to propose a high predicted rate, it must be large data set(27).

This study had several limitations. Firstly, in spite of there were no significant differences in severe postoperative complications between the modeling data set and validation data set, several clinical characteristics were different each year. For instance, the proportion of open surgery (48.4%) in modeling data was higher than that of validation data set (p<0.001), and the operation time was significantly longer in modeling data set (p<0.001). The D2 lymphadenectomy was more frequently performed in validation data set (p<0.001). The reason mainly due to the surgical environment such has been changed year by year. The laparoscopic technique has been evolved from laparoscopy-assisted to totally laparoscopic,

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and the surgical instruments such as three-dimensional laparoscope, robotic surgery system, ultrasonically activated shears, and endo-staplers have been also improved as well. Because of there were many clinical studies providing the feasibility of D2 lymphadenectomy with minimally invasive surgery(28,29), we adopted and experienced minimally invasive surgery in our hospital. Secondly, the ANN model needs to perform external validation, to assess the prediction accuracy. However, the ANN is a fair tool to predict severe postoperative complication after gastric cancer surgery. Therefore, prospective big data research is needed to prove advantage of ANN in gastric cancer surgery.

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Ⅴ. CONCLUSION

The ANN model for predicting of severe postoperative complication after gastric cancer surgery is a fair tool with a high level of accuracy and diagnostic value compared to that of linear regression analysis. However, its diagnostic value was not reproduced in the interval validation using the recent year data. Therefore, continuous learning and training of the ANN model to reflect surgical variance seem to be needed for the application in the clinical practice.

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REFERENCES

1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65(2):87-108.

2. Yoo HM, Lee HH, Shim JH, Jeon HM, Park CH, Kim JG, et al. Long-term outcomes and survival after laparoscopy-assisted distal gastrectomy for gastric cancer: three-year survival analysis of a single-center experience in Korea. J Surg Oncol.

2011;104(5):511-5.

3. Sano T, Sasako M, Mizusawa J, Yamamoto S, Katai H, Yoshikawa T, et al.

Randomized Controlled Trial to Evaluate Splenectomy in Total Gastrectomy for Proximal Gastric Carcinoma. Ann Surg. 2016.

4. Hu Y, Huang C, Sun Y, Su X, Cao H, Hu J, et al. Morbidity and Mortality of Laparoscopic Versus Open D2 Distal Gastrectomy for Advanced Gastric Cancer: A Randomized Controlled Trial. J Clin Oncol. 2016;34(12):1350-7.

5. Information Committee of Korean Gastric Cancer A. Korean Gastric Cancer Association Nationwide Survey on Gastric Cancer in 2014. J Gastric Cancer.

2016;16(3):131-40.

6. Copeland GP, Jones D, Walters M. POSSUM: a scoring system for surgical audit.

Br J Surg. 1991;78(3):355-60.

7. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-29.

- 26 -

8. Tekkis PP, McCulloch P, Poloniecki DR, Prytherch DR, Kessaris N, Steger AC.

Risk-adjusted prediction of operative mortality in oesophagoastric surgery with O-POSSUM.

Br J Surg. 2004;91(3):288-95.

9. Eftekhar B, Mohammad K, Ardebili HE, Ghodsi M, Ketabchi E. Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med Inform Decis Mak. 2005;5:3.

10. Vukicevic AM, Stojadinovic M, Radovic M, Djordjevic M, Cirkovic BA, Pejovic T, et al. Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery. Comput Biol Med. 2016;75:80-9.

11. Aleksic M, Luebke T, Heckenkamp J, Gawenda M, Reichert V, Brunkwall J.

Implementation of an artificial neuronal network to predict shunt necessity in carotid surgery.

Ann Vasc Surg. 2008;22(5):635-42.

12. Baxt WG, Skora J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet. 1996;347(8993):12-5.

13. Francis NK, Luther A, Salib E, Allanby L, Messenger D, Allison AS, et al. The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic colorectal cancer surgery. Tech Coloproctol.

2015;19(7):419-28.

14. Dindo D, Demartines N, Clavien P-A. Classification of Surgical Complications.

Annals of Surgery. 2004;240(2):205-13.

15. N. Cristianini, J. Shawe-Taylor, An Introduction to support Vector Machines: and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge UK, 2000.

- 27 -

16. G. Stiglic, S. Kocbek, I. Pernek, P. Kokol, Comprehensive decision tree models in bioinformatics, PLoS One 2012;7 (3):e33812.

17. JV T. advantages and Disadvantages of Using Artificial Neural Networks versus Logistic Regression for Predicting Medical Outcomes. J clin epideminol. 1996;1996(49):6.

18. Kunisaki C, Makino H, Takagawa R, Sato K, Kawamata M, Kanazawa A, et al.

Predictive factors for surgical complications of laparoscopy-assisted distal gastrectomy for gastric cancer. Surg Endosc. 2009;23(9):2085-93.

19. Park DJ, Lee HJ, Kim HH, Yang HK, Lee KU, Choe KJ. Predictors of operative morbidity and mortality in gastric cancer surgery. Br J Surg. 2005;92(9):1099-102.

20. Persiani R, Antonacci V, Biondi A, Rausei S, La Greca A, Zoccali M, et al.

Postoperative morbidity and mortality after D1 and D2 resections for gastric cancer:

preliminary results of the MRC randomised controlled surgical trial. The Surgical Cooperative Group. Lancet. 1996;347(9007):995-9.

24. Kim W, Kim HH, Han SU, Kim MC, Hyung WJ, Ryu SW, et al. Decreased Morbidity of Laparoscopic Distal Gastrectomy Compared With Open Distal Gastrectomy for

- 28 -

Stage I Gastric Cancer: Short-term Outcomes From a Multicenter Randomized Controlled Trial (KLASS-01). Ann Surg. 2015.

25. Strong VE, Song KY, Park CH, Jacks LM, Gonen M, Shah M, 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(4):640-6.

26. Sano T. Gastric cancer: Asia and the world. Gastric Cancer. 2017;20(Suppl 1):1-2.

27. Beleites C, Neugebauer U, Bocklitz T, Krafft C, Popp J. Sample size planning for classification models. Anal Chim Acta. 2013;760:25-33.

28. Hong SS, Son SY, Shin HJ, Cui LH, Hur H, Han SU. Can Robotic Gastrectomy Surpass Laparoscopic Gastrectomy by Acquiring Long-Term Experience? A Propensity Score Analysis of a 7-Year Experience at a Single Institution. J Gastric Cancer.

2016;16(4):240-6.

29. Hur H, Lee HY, Lee HJ, Kim MC, Hyung WJ, Park YK, et al. Efficacy of laparoscopic subtotal gastrectomy with D2 lymphadenectomy for locally advanced gastric cancer: the protocol of the KLASS-02 multicenter randomized controlled clinical trial. BMC Cancer. 2015;15:355.