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"nBackground: The aim of this study was to predict the survival rate of Iranian gastric cancer patients using the Cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients."nMethods: In this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal cancers), Tehran, Iran, to predict the survival time using Cox proportional hazard and artificial neural network techniques. "nResults: The estimated one-year, two-year, three-year, four-year and five-year survival rates of the patients were 77.9%, 53.1%, 40.8%, 32.0%, and 17.4%, respectively. The Cox regression analysis revealed that the age at diag-nosis, high-risk behaviors, extent of wall penetration, distant metastasis and tumor stage were significantly associated with the survival rate of the patients. The true prediction of neural network was 83.1%, and for Cox regression model, 75.0%."nConclusion: The present study shows that neural network model is a more powerful statistical tool in predicting the survival rate of the gastric cancer patients compared to Cox proportional hazard regression model. Therefore, this model recommended for the predicting the survival rate of these patients.

Introduction: Cox regression model is one of the statistical methods in survival analysis.Proportionality of hazard rate is an assumption of this model. In the recent decades, artificial neuralnetwork (ANN) model has increasingly used in survival prediction. This study aimed to predict thesurvival probability of Gastric cancer patients using Cox regression and ANN models.Materials and Methods: In this historical-cohort study, information of total of 436 gastric cancerpatients with adenocarcinomas pathology who underwent surgery at the Taleghani hospital of Tehranbetween 2002 and 2007 were included. Data were divided to training and testing (or validation) groups,randomly. The Cox regression model (semi-parametric model) and a three layer ANN model were usedfor analyzing of database. Furthermore, the area under receiver operating characteristic curve (AUROC)and classification accuracy were used to compare these models.Results: Prediction accuracy of ANN and Cox regression models were 81.51% and 72.60%,respectively. In addition, AUROC of ANN and Cox regression models were 0.826 and 0.754,respectively.Conclusions: ANN was better than Cox regression model in terms of AUROC and accuracy ofprediction. Therefore, ANN model is recommended for prediction of survival probability. These findingare very important in health research, particularly in allocation of medical resources for patients whopredicted as high-risks.

Introduction: Gastric cancer is one of the most common cancers in the world. The classical methodssuch as Cox regression and parametric models are used in most medical researches that their aims is thesurvival distribution survey, although the Bayes models have some advantages in compared with theclassical models. The present study was performed to analyze the survival rate of patients who had gastriccancer and were under treatment in the gastroenterology ward of Taleghani hospital, in Tehran usingBayes models. Weibull distribution was used for modeling in the study .Material and Methods: This study was a cohort study and performed in the gastroenterology ward ofTaleghani hospital by using gastric cancer patient's data from January 2003 to December 2007.178patients were enrolled to the study and their information was collected through telephone contacts. Thesurvival rate of patients were analyzed using Bayes Weibull models by considering variables such as ageof diagnosis, gender, tumor size, metastasis of other lymph. For determining of the risk factors on thesurvival of patients, was used Weibull model in the case that interval censoring. Data analysis was carriedout using Winbugs software and significant levels were considered 0.05 .Results: The results showed survival rate are dependent on the age of diagnosis and tumor size. Thosepatients who had early diagnosis, the rate of survival was greater. In addition, he patients who had smallertumor size, their survival rate was greater .Conclusion: Considering to classical models are based on normal approximation and applicable for bigsamples, Bayes methods are emphasized for small to medium samples. The results of this study showedthat the Bayesian Weibull model is a suitable model. This study also showed that age of diagnosis andtumor size of patients is important factors in regard to the survival rate of these patients. As a result, ifgastric cancer is diagnosed early, the relative risk of death would reduce.

Background & Objective: Using parametric models is common approach in survival analysis. In the recent years, artificial neural network (ANN) models have increasingly used in survival prediction. The aim of this study was to predict of survival rate of patients with gastric cancer by using a parametric regression and ANN models and compare these methods. Methods: We used the data of 436 gastric cancer patients from a cancer registry in Tehran between 2002-2007. All patients had a confirmed diagnosis. Data were randomly divided into two groups: training and testing (or validation) set. For analysis of data we used a parametric model (exponential, Weibull, normal, lognormal, logistic and log-logistic models) and a three layer ANN model. In order to compare of the prediction of two models, we used the area under receiver operating characteristic (AUROC) curve, classification table and concordance index. Results: The prediction accuracy of the ANN and the parametric (Weibull) models were 79.45% and 73.97% respectively. The AUROC for the ANN and the Weibull models were 0.815 and 0.748 respectively. Conclusions: The ANN had a better predictions than the Weibull model. Thus it is suggested to use of the ANN model survival prediction in field of cancer.