• Radiation Oncology Journal
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• v.34 no.2
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• pp.96-105
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• 2016

Purpose: The standard radiation dose for patients with locally rectal cancer treated with preoperative chemoradiotherapy is 45-50 Gy in 25-28 fractions. We aimed to assess whether a difference exists within this dose fractionation range. Materials and Methods: A retrospective analysis was performed to compare three dose fractionation schedules. Patients received 50 Gy in 25 fractions (group A), 50.4 Gy in 28 fractions (group B), or 45 Gy in 25 fractions (group C) to the whole pelvis, as well as concurrent 5-fluorouracil. Radical resection was scheduled for 8 weeks after concurrent chemoradiotherapy. Results: Between September 2010 and August 2013, 175 patients were treated with preoperative chemoradiotherapy at our institution. Among those patients, 154 were eligible for analysis (55, 50, and 49 patients in groups A, B, and C, respectively). After the median follow-up period of 29 months (range, 5 to 48 months), no differences were found between the 3 groups regarding pathologic complete remission rate, tumor regression grade, treatment-related toxicity, 2-year locoregional recurrence-free survival, distant metastasis-free survival, disease-free survival, or overall survival. The circumferential resection margin width was a prognostic factor for 2-year locoregional recurrence-free survival, whereas ypN category was associated with distant metastasis-free survival, disease-free survival, and overall survival. High tumor regression grading score was correlated with 2-year distant metastasis-free survival and disease-free survival in univariate analysis. Conclusion: Three different radiation dose fractionation schedules, within the dose range recommended by the National Comprehensive Cancer Network, had no impact on pathologic tumor regression and early clinical outcome for locally advanced rectal cancer.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.405-411
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• 2004

In the analysis of cancer data, it is important to make inferences of survival function and to assess the effects of covariates. Cox's proportional hazard model(PHM) and Beran's nonparametric method are generally used to estimate the survival function with covariates. We adjusted the incomplete survival time using the Buckley and James's(1979) pseudo random variables, and then proposed the estimator for the conditional survival function. Also, we carried out the simulation studies to compare the performances of the proposed method.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8331-8335
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• 2014

Background: Previous studies accessing the association of CYP2C19 with outcomes of patients using tamoxifen for breast cancer have yielded conflicting results. The aim of this meta-analysis is to obtain a more precise estimate of effects of CYP2C19 polymorphisms and to clarify their effects on survival of the breast cancer patients using tamoxifen. Materials and Methods: A systematic search of PubMed and Embase was performed, comparing patients with or without $CYP2C19^*2$ and $CYP2C19^*17$, relevant articles searched for. The following outcomes were included from the eligible studies: disease-free survival (DFS) and overall survival (OS), expressed by hazard ratios (HR) with corresponding 95% confidence interval (CI). Subgroup analysis by genotypes was also performed. Pooled estimates were calculated using random-effect model in accordance to the heterogeneity. Results: Six studies met the inclusion criteria. The integrated OR on the association between CYP2C19 and DFS, calculated by the random-effect model, was 0.54 (95%CI=0.34-0.84, p=0.013). Subgroup analysis showed that both $CYP2C19^*2$ and $CYP2C19^*17$ were associated with increased survival. The pooled results of two studies for OS were OR=0.46 (95%CI=0.21-1.01, p=0.233). Conclusions: This meta-analysis suggests that the $CYP2C19^*2$ and $CYP2C19^*17$ genotypes are associated with increased survival in breast cancer patients using tamoxifen.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.103-108
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• 2019

The purpose of this study is to identify the factors that may affect the 5-year survival of colon cancer through network model and to use it as a clinical decision supporting system for colorectal cancer patients. This study was conducted using data from 2,540 patients who underwent colorectal cancer surgery from 1996 to 2018. Eleven factors related to survival of colorectal cancer were selected by consulting medical experts and previous studies. Analysis was proceeded from the data sorted out into 1,839 patients excluding missing values and outliers. Logistic regression analysis showed that age, BMI, and heart disease were statistically significant in order to identify factors affecting 5-year survival of colorectal cancer. Additionally, a correlation analysis was carried out age, BMI, heart disease, diabetes, and other diseases were correlated with 5-year survival of colorectal cancer. Sex was related with BMI, lung disease, and liver disease. Age was associated with heart disease, heart disease, hypertension, diabetes, and other diseases, and BMI with hypertension, diabetes, and other diseases. Heart disease was associated with hypertension, diabetes, hypertension, diabetes, and other diseases. In addition, diabetes and kidney disease were associated. In the correlation analysis, the network model was constructed with the Network Correlation Coefficient less than p <0.001 as the weight. The network model showed that factors directly affecting survival were age, BMI levels, heart disease, and indirectly influencing factors were diabetes, high blood pressure, liver disease and other diseases. If the network model is used as an assistant indicator for the treatment of colorectal cancer, it could contribute to increasing the survival rate of patients.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.891-907
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• 2014

This article is concerned with one of the most important prior distributions for Bayesian analysis of survival and event history data, called Beta processes, proposed in Hjort (1990). We review the current state of the art of beta processes and their application to survival analysis. Relevant methodological and practical areas of research that we touch on relate to constructions, posterior distributions, large-sample properties, Bayesian computations, and mixtures of Beta processes.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7359-7363
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• 2015

Background: As a result of significant progress made in treatment of many types of cancers during the last few decades, there have been an increased number of patients who do not experience mortality. We refer to these observations as cure or immune and models for survival data which include cure fraction are known as cure rate models or long-term survival models. Materials and Methods: In this study we used the data collected from 438 female patients with breast cancer registered in the Cancer Research Center in Shahid Beheshti University of Medical Sciences, Tehran, Iran. The patients had been diagnosed from 1992 to 2012 and were followed up until October 2014. We had to exclude some because of incomplete information. Phone calls were made to confirm whether the patients were still alive or not. Deaths due to breast cancer were regarded as failure. To identify clinical, pathological, and biological characteristics of patients that might have had an effect on survival of the patients we used a non-mixture cure rate model; in addition, a Weibull distribution was proposed for the survival time. Analyses were performed using STATA version 14. The significance level was set at $P{\leq}0.05$. Results: A total of 75 patients (17.1%) died due to breast cancer during the study, up to the last follow-up. Numbers of metastatic lymph nodes and histologic grade were significant factors. The cure fraction was estimated to be 58%. Conclusions: When a cure fraction is not available, the analysis will be changed to standard approaches of survival analysis; however when the data indicate that the cure fraction is available, we suggest analysis of survival data via cure models.

• Korean Journal of Artificial Intelligence
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• v.6 no.2
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• pp.11-15
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• 2018

Breast cancer is one of the leading causes of cancer related death among women. So prediction of overall survival status is important into decided in adjuvant treatment. Deep belief network is a kind of artificial intelligence (AI). We intended to construct prediction model by deep belief network using associated clinicopathologic factors. 103881 cases were found in the Korean Breast Cancer Registry. After preprocessing of data, a total of 15733 cases were enrolled in this study. The median follow-up period was 82.4 months. In univariate analysis for overall survival (OS), the patients with advanced AJCC stage showed relatively high HR (HR=1.216 95% CI: 0.011-289.331, p=0.001). Based on results of univariate and multivariate analysis, input variables for learning model included 17 variables associated with overall survival rate. output was presented in one of two states: event or cencored. Individual sensitivity of training set and test set for predicting overall survival status were 89.6% and 91.2% respectively. And specificity of that were 49.4% and 48.9% respectively. So the accuracy of our study for predicting overall survival status was 82.78%. Prediction model based on Deep belief network appears to be effective in predicting overall survival status and, in particular, is expected to be applicable to decide on adjuvant treatment after surgical treatment.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.39-42
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• 2013

Background: Breast cancer is the most frequently occurring cancer among Iranian women; however limited studies have been conducted to address survival rates. Objective: The objective was to examine survival rates in Tabriz (Northwest of Iran) and comparing with those of data reported from other cities and countries. Methods: Survival rates were calculated for one, three, five, seven and ten years for 271 breast cancer patients referred to one university clinic during 1997-2008. Results: Survival analysis demonstrated a lower survival rate compared to western countries. Conclusions: Survival rates for our patients are similar/better than other cities in Iran, but lower than certain European countries and the US. Further studies with a higher number of patients are now required.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1803-1806
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• 2013

Purpose: Carbohydrate antigen (CA) 242 is inversely related to prognosis in many cancers. However, few data regarding CA 242 in esophageal cancer (EC) are available. The aim of this study was to determine the prognostic value of CA 242 and propose an optimum cut-off point in predicting survival difference in patients with esophageal squamous cell carcinoma (ESCC). Methods: A retrospective analysis was conducted of 192 cases. A receiver operating characteristic (ROC) curve for survival prediction was plotted to verify the optimum cuf-off point. Univariate and multivariate analyses were performed to evaluate prognostic parameters for survival. Results: The positive rate for CA 242 was 7.3% (14/192). The ROC curve for survival prediction gave an optimum cut-off of 2.15 (U/ml). Patients with CA 242 ${\leq}$ 2.15 U/ml had significantly better 5-year survival than patients with CA 242 >2.15 U/ml (45.4% versus 22.6%; P=0.003). Multivariate analysis showed that differentiation (P=0.033), CA 242 (P=0.017), T grade (P=0.004) and N staging (P<0.001) were independent prognostic factors. Conclusions: Preoperative CA 242 is a predictive factor for long-term survival in ESCC, especially in nodal-negative patients. We conclude that 2.15 U/ml may be the optimum cuf-off point for CA 242 in predicting survival in ESCC.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1193-1196
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• 2016

Background: Colorectal cancer (CRC) is the commonest malignancy in the lower gastrointestinal tract in both men and women. It is the third leading cause of cancer-dependent death in the world. In Iran the incidence of colorectal cancer has increased during the last 25 years. Materials and Methods: In this article we analyzed the survival of 447 colorectal patients of Taleghani hospital in Tehran using parametric competing-risks models. The cancers of these patients were diagnosed during 1985 - 2012 and followed up to 2013. The purpose was to assess the association between survival of patients with colorectal cancer in the presence of competing-risks and prognostic factors using parametric models. The analysis was carried out using R software version 3.0.2. Results: The prognostic variables included in the model were age at diagnosis, tumour site, body mass index and sex. The effect of age at diagnosis and body mass index on survival time was statistically significant. The median survival for Iranian patients with colorectal cancer is about 20 years. Conclusions: Survival function based on Weibull model compared with Kaplan-Meier survival function is smooth. Iranian data suggest a younger age distribution compared to Western reports for CRC.