• Asian Pacific Journal of Cancer Prevention
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• 제15권14호
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• pp.5571-5575
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• 2014

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

• Asian Pacific Journal of Cancer Prevention
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• 제16권17호
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• pp.7785-7788
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• 2015

Utilization of high energy photons (>10MV) with an optimal weight using a mixed energy technique is a practical way to generate a homogenous dose distribution while maintaining adequate target coverage in intact breast radiotherapy. This study represents a model for estimation of this optimal weight for day to day clinical usage. For this purpose, treatment planning computed tomography scans of thirty-three consecutive early stage breast cancer patients following breast conservation surgery were analyzed. After delineation of the breast clinical target volume (CTV) and placing opposed wedge paired isocenteric tangential portals, dosimeteric calculations were conducted and dose volume histograms (DVHs) were generated, first with pure 6MV photons and then these calculations were repeated ten times with incorporating 18MV photons (ten percent increase in weight per step) in each individual patient. For each calculation two indexes including maximum dose in the breast CTV ($D_{max}$) and the volume of CTV which covered with 95% Isodose line ($V_{CTV,95%IDL}$) were measured according to the DVH data and then normalized values were plotted in a graph. The optimal weight of 18MV photons was defined as the intersection point of $D_{max}$ and $V_{CTV,95%IDL}$ graphs. For creating a model to predict this optimal weight multiple linear regression analysis was used based on some of the breast and tangential field parameters. The best fitting model for prediction of 18MV photons optimal weight in breast radiotherapy using mixed energy technique, incorporated chest wall separation plus central lung distance (Adjusted R2=0.776). In conclusion, this study represents a model for the estimation of optimal beam weighting in breast radiotherapy using mixed photon energy technique for routine day to day clinical usage.

• Asian Pacific Journal of Cancer Prevention
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• 제15권4호
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• pp.1557-1561
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• 2014

Background: Breast cancer is the most frequently diagnosed cancer in women and estimating its relative risks and trends of incidence at the area-level is helpful for health policy makers. However, traditional methods of estimation which do not take spatial heterogeneity into account suffer from drawbacks and their results may be misleading, as the estimated maps of incidence vary dramatically in neighboring areas. Spatial methods have been proposed to overcome drawbacks of traditional methods by including spatial sources of variation in the model to produce smoother maps. Materials and Methods: In this study we analyzed the breast cancer data in Iran during 2004-2008. We used a method proposed to cover spatial and temporal effects simultaneously and their interactions to study trends of breast cancer incidence in Iran. Results: The results agree with previous studies but provide new information about two main issues regarding the trend of breast cancer in provinces of Iran. First, this model discovered provinces with high relative risks of breast cancer during the 5 years of the study. Second, new information was provided with respect to overall trend trends o. East-Azerbaijan, Golestan, North-Khorasan, and Khorasan-Razavi had the highest increases in rates of breast cancer incidence whilst Tehran, Isfahan, and Yazd had the highest incidence rates during 2004-2008. Conclusions: Using spatial methods can provide more accurate and detailed information about the incidence or prevalence of a disease. These models can specify provinces with different health priorities in terms of needs for therapy and drugs or demands for efficient education, screening, and preventive policy into action.

• Asian Pacific Journal of Cancer Prevention
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• 제15권21호
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• pp.9453-9458
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• 2014

Background: Breast cancer is the second leading cause of cancer death for women in the United States. Differences in survival of breast cancer have been noted among racial and ethnic groups, but the reasons for these disparities remain unclear. This study presents the characteristics and the survival curve of two racial and ethnic groups and evaluates the effects of race on survival times by measuring the lifetime data-based half-normal model. Materials and Methods: The distributions among racial and ethnic groups are compared using female breast cancer patients from nine states in the country all taken from the National Cancer Institute's Surveillance, Epidemiology, and End Results cancer registry. The main end points observed are: age at diagnosis, survival time in months, and marital status. The right skewed half-normal statistical probability model is used to show the differences in the survival times between black Hispanic (BH) and black non-Hispanic (BNH) female breast cancer patients. The Kaplan-Meier and Cox proportional hazard ratio are used to estimate and compare the relative risk of death in two minority groups, BH and BNH. Results: A probability random sample method was used to select representative samples from BNH and BH female breast cancer patients, who were diagnosed during the years of 1973-2009 in the United States. The sample contained 1,000 BNH and 298 BH female breast cancer patients. The median age at diagnosis was 57.75 years among BNH and 54.11 years among BH. The results of the half-normal model showed that the survival times formed positive skewed models with higher variability in BNH compared with BH. The Kaplan-Meir estimate was used to plot the survival curves for cancer patients; this test was positively skewed. The Kaplan-Meier and Cox proportional hazard ratio for survival analysis showed that BNH had a significantly longer survival time as compared to BH which is consistent with the results of the half-normal model. Conclusions: The findings with the proposed model strategy will assist in the healthcare field to measure future outcomes for BH and BNH, given their past history and conditions. These findings may provide an enhanced and improved outlook for the diagnosis and treatment of breast cancer patients in the United States.

• Asian Pacific Journal of Cancer Prevention
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• 제15권16호
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• pp.6709-6714
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• 2014

The rs2273535 polymorphism in the AURKA gene had proven to be associated with breast carcinoma susceptibility. Nevertheless, the results of different studies remain contradictory. A meta-analysis covering 28, 789 subjects from eleven different studies was here carried out in order to investigate the association in detail. The random effects model was used to analyze the pooled odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs). A significant relationship between the rs2273535 polymorphism and breast tumors was found in an allelic genetic model (OR: 1.076, 95% CI: 1.004-1.153, p=0.040, $P_{heterogeneity}$=0.002). No significant association was detected in a homozygote model (OR: 1.186, 95% CI: 0.990-1.423, P=0.065, $P_{heterogeneity}$=0.002), a heterozygote model (OR: 1.016, 95% CI: 0.959-1.076, p=0.064, $P_{heterogeneity}$=0.000), a dominant genetic model (OR: 1.147, 95% CI: 0.992-1.325, p=0.217, $P_{heterogeneity}$=0.294) and a recessive genetic model (OR: 1.093, 95% CI: 0.878-1.361, p=0.425, $P_{heterogeneity}$=0.707). A significant relationship between the rs2273535 polymorphism in the AURKA gene and breast tumor in Asian group was found in an allelic genetic model (OR: 1.124, 95% CI: 1.003-1.29, p=0.044, $P_{heterogeneity}$=0.034), a homozygote model (OR: 1.229, 95% CI: 1.038-1.455, p=0.016, $P_{heterogeneity}$=0.266) and a recessive genetic model (OR: 1.227, 95% CI: 1.001-1.504, p=0.049, $P_{heterogeneity}$=0.006). A significant association was thus observed between the rs2273535 polymorphism in the AURKA gene and breast cancer risk. Individuals with the rs2273535 polymorphism in the AURKA gene have a higher risk of breast cancer in Asian populations, but not in Caucasians.

• Asian Pacific Journal of Cancer Prevention
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• 제17권4호
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• pp.2229-2233
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• 2016

Background: Breast cancer is one of the most common cancers among women in the world. Early detection is necessary to improve outcomes and decrease related costs. The aim of this study was to assess the predictive power of health locus of control as a modifying factor in the Health Belief Model (HBM) for prediction of breast self-examination. Materials and Methods: In this cross- sectional study, 400 women selected through the convenience sampling from health centers. Data were collected using part of the Champion's HBM scale (CHBMS), the Health Locus of Control Scale and a self administered questionnaire. For data analysis by SPSS the independent T test, Chi square test, logistic and linear regression modes were appliedl. Results: The results showed that 10.9% of the participants reported performing BSE regularly. Health locus of control did not act as a predictor of BSE as a modifying factor. In this study, perceived self-efficacy was the strongest predictor of BSE performance (Exp (B) =1.863) with direct effect, while awareness had direct and indirect influence. Conclusions: For increasing BSE, improvement of self-efficacy especially in young women and increasing knowledge about cancer is necessary.

• 대한방사선기술학회지:방사선기술과학
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• 제43권1호
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• pp.23-27
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• 2020

In South Korea, female individuals in their forties show a high rate of incidence, with approximately 13% of the patients being <40 years. This statistic is more than twice as high as that in Western countries. It is therefore necessary to identify the risk factors for breast cancer incidence by age and economic activity participation status. Women aged 30 to 59-whether breast cancer patients or those in the control group and having no breast cancer-were appraised from the sample cohort database. The data were analyzed using the statistical software R36.2. To identify the factors affecting breast cancer incidence, the degree of association was determined with HR and 95% CI by means of cox regression analysis. As for the socio-demographic variables, the older the individual, the higher the risk of breast cancer incidence becomes. As for the economic activity variables, those who were dependents (unemployed) and who had higher income (medium and high) were at higher risk of breast cancer incidence, which was statistically significant. The income-adjusted HR (model 1) for breast cancer development associated with the economic activity was 1.452 (95% CI, 1.19-1.77). The body mass index and alcohol intake-adjusted HR (model 2) was 1.431 (95% CI, 1.18-1.74). One needs to pay attention to policy plans regarding women's quality of life, as well as to the risk of breast cancer incidence by their economic activity. In other words, policies need to give post care, instead of focus on early detection and cancer treatment.

• Asian Pacific Journal of Cancer Prevention
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• 제16권18호
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• pp.8567-8571
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• 2016

Background: The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. Materials and Methods: We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. Results: On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Conclusions: Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.

• Asian Pacific Journal of Cancer Prevention
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• 제15권2호
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• pp.663-669
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• 2014

Background: With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and Methods: A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results: The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions: The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.

• 대한예방한의학회지
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• 제26권1호
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• pp.11-23
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• 2022

Objectives : The aim of this study was to develop a clinical pathway (CP) model for the integrative treatment of non-invasive breast cancer, with western medicine and Korean traditional medicine. Methods : The checklist model was composed in four types according to the target patients: DCIS inpatients, DCIS outpatients, LCIS inpatients, LCIS outpatients. The vertical axis of the pathway consists of 11 categories of actions applied to the patient. The horizontal axis was in accordance with the flow of time, comprising three periods during inpatient care and seven periods during outpatient care. In addition, CP was also composed in flow chart form. The pathway model was developed through a literature review of clinical practice guidelines, conference publications, papers, books, and websites. Results : The integrative CP model for non-invasive breast cancer was developed. Conclusions : The goal of the CP suggested in this study was to improve non-invasive breast cancer patients' quality of life and to supplement conventional treatment, by alleviating the side effects. The model developed through this study could serve as the basis when developing CPs in a real-world integrative medical environment. This could lead to a reduction in cost and time for CP development, thus bringing about efficiency in the clinical setting.