Showing 8 results for Gastric Cancer
B Moghimi Dehkordi, A Rajaeefard, Hr Tabatabaee, B Zeighami, A Safaee, Z Tabeie,
Volume 3, Issue 1 (9-2007)
Abstract
Background & Objectives: Cancer has been traditionally regarded as a fatal disease it is a major public health problem in many countries throughout the world. In recent years, cancer morbidity and mortality has increased in our country and notably stomach cancer now ranks second or third among all cancers types with regard to morbidity.
Methods: Our study included all gastric cancer patients registered in the cancer registry of Fars province. The patients' survival status was followed using phone calls and death records from hospitals, other medical centers, and the city's cemetery. Data analysis involved the use of the nonparametric Kaplan-Meier and Cox proportional hazards models and was performed with the software package SPSS V.13.
Results: Of the 442 patients with gastric cancer, 303 cases (68.6 percents) were male, and the mean age of patients was 58.41 years (SD=14.46). In univariate analysis with the KM method, a statistically significant association was found between survival rates and the following factors: age at diagnosis (P<0.001), tumor grade (P=0.009), presence of metastases (P<0.001), and type of the initial treatment (P=<0.001). Factors without a significant relationship with the survival rate included sex, ethnicity, weight, BMI, tobacco use, history of cancer in close or distant relatives, place of residence, number of children, marital status, occupation, and income. In Cox regression, only age at diagnosis, tumor grade, and the presence of metastases showed a significant association with survival rates.
Conclusions: Our results imply that early detection of cancer at a lower age and in lower tumor grades could be important for increasing the patients' life expectancy.
Ar Baghestani, E Hajizadeh, Sr Fatemi,
Volume 6, Issue 3 (12-2010)
Abstract
Background & Objectives: The Cox proportional-hazards regression and other parametric models model have achieved widespread use in the analysis of time-to-event data with censoring and covariates. However employing Bayesian method has not been widely used or discussed. The aim of this study was to evaluate the prognostic factors in using Bayesian interval censoring analysis.
Methods: This cohort study was based on 178 patients with gastric cancer from January 2003 to December 2007 admitted to Taleghani teaching hospital in Tehran. Known prognostic risk factors were entered into the analysis using Bayesian Weibull and Exponential models. The term DIC was employed to find best model.
Results: The results were showed survival rate depended on age of diagnosis and tumor size. Those patients who had early diagnosis and/or had smaller tumor size were in lower risk of death.
Conclusion: The age of diagnosis and tumor size of patients are important prognostic factors related to survival of patients with gastric cancer. Based on DIC, Bayesian analysis of the Weilbull model performed better than the Exponential model. As a result, if this cancer has been diagnosed early, the relative risk of death would reduce.
A Biglarian, E Hajizadeh, A Kazemnejad,
Volume 6, Issue 3 (12-2010)
Abstract
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.
H Noorkojuri, E Hajizadeh, Ar Baghestani, Ma Pourhoseingholi,
Volume 9, Issue 2 (10-2013)
Abstract
Background & Objectives: Cox regression model is one of the statistical methods in survival analysis. The use of smoothing techniques in Cox model makes the more accurate estimates for the parameters. Fractional polynomial is one of these techniques in Cox model. The aim of this study was to assess the effects of prognostic factors on survival of patients with gastric cancer using the fractional polynomial in Cox model and Cox proportional hazards.
Methods: Information of total of 216 patients with gastric cancer who underwent surgery in the gastroenterology ward of Taleghani Hospital in Tehran between 2003 and 2008 were included in this retrospective study. In this research, fractional polynomial in Cox model and Cox proportional hazards model were utilized for determining the effects of prognostic factors on patients’ survival time with gastric cancer. The SPSS version 18.0 and R version 2.14.1 were used for data analysis. These models were compared with Akaike information criterion.
Results: The analysis of Cox proportional hazards and fractional polynomial models resulted in age at diagnosis and tumor size as prognostic factors on survival time of patients with gastric cancer independently (P<0.05). Also, Akaike information criterion was equal in both models.
Conclusion: In the present study, the Cox proportional hazards and fractional polynomial models led to similar results with equal Akaike information criterions. Using of smoothing methods helped us eliminate non-linear effects but it seemed more appropriate to use Cox proportional hazards model in medical data because of its’ ease of interpretation and capability of modeling in both continuous and discrete covariates.
M Khodadost, P Yavari, Ss Hashemi Nazari , M Babaei, A Abadi, F Sarvi,
Volume 10, Issue 4 (3-2015)
Abstract
Background and Objectives : Awareness of the cancer incidence is essential for cancer prevention and control programs. Capture-recapture methods have been recommended for reducing bias and increasing the accuracy of cancer incidence estimation. This study aimed to estimate the incidence of gastric cancer by the capture-recapture method based on Ardabil population-based cancer registry data.
Methods: All new cases of gastric cancer reported by three sources, i.e. pathology reports, death certificates, and medical records, reported to Ardabil population-based cancer registry between 2006 and 2008 were enrolled in the study. The duplicate cases based on the similarity of the first name, surname, and father's name were identified among sources. The estimated incidence was calculated by the log-linear method using the Stata 12 software.
Results : A total 857 new cases of gastric cancer were reported from three sources. After removing duplicates, the reported incidence rate was 35.3 and 32.5 per 100,000 population for the years 2006 and 2008, respectively. The estimated incidence rate calculated by the log-linear method for these years was 96.2 and 90.4 per 100,000 population, respectively.
Conclusion: The results showed that none of the sources of pathology reports, death certificates, and medical records, individually or collectively, fully covered the incidence of gastric cancer. We can obtain more accurate estimates of the incidence rate using the capture-recapture method.
E Akhondzadeh, P Yavari, Y Mehrabi, A Kabir,
Volume 11, Issue 1 (6-2015)
Abstract
Background and Objectives : Various studies have reported different survival rates of patients with gastric cancer in Iran, and there is no overall estimate of the survival rate. The aim of this study was to conduct a meta-analysis of one, three, and five-year survival rate of patients with gastric cancer in Iran.
Methods: In this study, all of the national databases including Iran Medex, Magiran, SID, and Medlib and the English databases including Google Scholar and PubMed were searched by using the keywords “stomach cancer”, “survival rate” and other Persian and English synonymous keywords, in the period 1392-1339 . Then, all articles with inclusion criteria and acceptable quality were investigated. Der Simonian and Laird random effects models were used to combine the results of all studies. Other analyses including subgroup analysis, sensitivity analysis, and assessment of publication bias were performed by using the funnel plot, and Beg’s and Egger’s tests. Finally, the data was analyzed using STATA software.
Results: Of the 235 articles found in the initial search, nine studies were eligible for this study. According to these studies, one, three and five-years survival rate of patients with gastric cancer was 0.57 (95% CI: 0.45-0.70), 0.29 (95% CI: 0.22-0.37), 0.17 (95% CI: 0.13-0.21), respectively.
Conclusion : Researches conducted in different parts of Iran are limited and there are no exact statistics on the survival rate in other parts of Iran. Therefore, further studies in the whole country are required to obtain more precise estimates of the survival and factors affecting it.
H Jamali, N Khanjani, M Fararouei, Z Parisae, M Chorami,
Volume 11, Issue 1 (6-2015)
Abstract
Background & Objectives : Gastric cancer has a low survival and remains a serious threat to the health of human life, especially in developing countries such as Iran. The present study was performed to estimate the main effective factors in the survival rate of patients with gastric cancer in the Province of Kohgilouyeh & Boyerahmad.
Methods: All cases of gastric cancer in Kohgiloyeh and Boyerahmad recorded in Provinces of Fars and Kohgiloyeh and Boyerahmad cancer registry were enrolled in this study. The impact of the independent variables on the survival was estimated by single and multivariate Cox regression controlled for the probable confounding variables. Survival analysis was performed using Kaplan Meier curves, the log-rank test, and Wilcoxon test to compare the results. Analysis of the data was performed by SPSS 19, and P-values less than 0.05 were considered significant.
Results: Among the 348 studied patients, 75.6% were male and the rest (24.4%) were female. In general, in this study, 1, 2, 3, 4, and 5-year survival rate of the patients was 37, 27, 20, 19, and 18%, respectively. By combining these end variables in regression models, three risk groups were identified. In the high risk group, the cumulative survival rate was 0% at the end of the fifth year.
Conclusion: Execution of the down-staging program through public education, considering the low survival rate in this province seems essential especially for high-risk groups such as farmers, ranchers and regional nomadic populations.
M Safari, M Abbasi, F Gohari Ensaf , Z Berangi, Gh Roshanaei,
Volume 15, Issue 4 (1-2020)
Abstract
Background and Objectives: In survival analysis, using the Cox model to determine the effective factors requires the assumptions whose failure of leads to biased results. The aim of this paper was to determine the factors affecting the survival of metastatic gastric cancer patients using the non-parametric method of Randomized Survival Forest (RSF) model and to compare its result with the Cox model.
Methods: In this retrospective cohort study, 201 patients with metastatic gastric cancer were evaluated in Hamadan Province. Patient survival was calculated from diagnosis to death or end of study. Demographic characteristics (such as gender and age) and clinical variables (including stage, tumor size, etc.) were extracted from the patient records. Factors affecting survival were determined using the Cox model and RSF. Data analysis was performed using the R3.4.3 software and RandomForestSRC and survival packages.
Results: The mean (SD) age of patients was 61.5 (12.9) years old. The Cox model showed that chemotherapy (p=0.033) was effective in survival, and the results of fitting the RSF model showed that the most important variables affecting survival were type of surgery, location of metastasis, chemotherapy, age, tumor grade, surgery, number of involved lymph nodes, sex and radiotherapy. Based on the model appropriateness, the RSF model with log-rank split rule had a better performance compared to the Cox model.
Conclusion: If the number of variables is high and there is a relationship between the variables, the RSF method identifies the important and effective variables on survival with high accuracy without requiring restrictive assumptions compared to the Cox model.
