• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.361-367
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• 2016

We have reported a high prevalence of breast cancer in light-polluted areas in Korea. However, it is necessary to analyze the spatial effects of light polluted areas on breast cancer because light pollution levels are correlated with region proximity to central urbanized areas in studied cities. In this study, we applied a spatial regression method (an intrinsic conditional autoregressive [iCAR] model) to analyze the relationship between the incidence of breast cancer and artificial light at night (ALAN) levels in 25 regions including central city, urbanized, and rural areas. By Poisson regression analysis, there was a significant correlation between ALAN, alcohol consumption rates, and the incidence of breast cancer. We also found significant spatial effects between ALAN and the incidence of breast cancer, with an increase in the deviance information criterion (DIC) from 374.3 to 348.6 and an increase in $R^2$ from 0.574 to 0.667. Therefore, spatial analysis (an iCAR model) is more appropriate for assessing ALAN effects on breast cancer. To our knowledge, this study is the first to show spatial effects of light pollution on breast cancer, despite the limitations of an ecological study. We suggest that a decrease in ALAN could reduce breast cancer more than expected because of spatial effects.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.442-450
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• 2014

This paper is a study of the propagation characteristic of matching liquids in the skin-covered breast model. In order to evaluate the matching liquids, we investigated six kinds of matching liquids applied to proposed 1-D breast model from frequency range of 3~6 GHz. A uniform plane wave is projected / transmitted inside the multi-layered breast model. Then the propagation characteristics inside the model and the transmission loss of each matching liquids were analyzed. The studying method presented in the paper can be used in the breast cancer detection system, the field of cancer detection using human tissue and the field of other medical devices. This paper was applied to the breast cancer detection system. Consequently, these studies could be used to determine the suitable type of matching liquids for breast cancer detection system and to apply useful for performance analysis.

• Journal of Preventive Medicine and Public Health
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• v.49 no.6
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• pp.367-375
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• 2016

Objectives: The established theory that breast density is an independent predictor of breast cancer risk is based on studies targeting white women in the West. More Asian women than Western women have dense breasts, but the incidence of breast cancer is lower among Asian women. This meta-analysis investigated the association between breast density in mammography and breast cancer risk in Asian women. Methods: PubMed and Scopus were searched, and the final date of publication was set as December 31, 2015. The effect size in each article was calculated using the interval-collapse method. Summary effect sizes (sESs) and 95% confidence intervals (CIs) were calculated by conducting a meta-analysis applying a random effect model. To investigate the dose-response relationship, random effect dose-response meta-regression (RE-DRMR) was conducted. Results: Six analytical epidemiology studies in total were selected, including one cohort study and five case-control studies. A total of 17 datasets were constructed by type of breast density index and menopausal status. In analyzing the subgroups of premenopausal vs. postmenopausal women, the percent density (PD) index was confirmed to be associated with a significantly elevated risk for breast cancer (sES, 2.21; 95% CI, 1.52 to 3.21; $I^2=50.0%$). The RE-DRMR results showed that the risk of breast cancer increased 1.73 times for each 25% increase in PD in postmenopausal women (95% CI, 1.20 to 2.47). Conclusions: In Asian women, breast cancer risk increased with breast density measured using the PD index, regardless of menopausal status. We propose the further development of a breast cancer risk prediction model based on the application of PD in Asian women.

• 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.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1363-1369
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• 2018

Breast cancer is one of the most common cancers in women worldwide. In Korea, breast cancer is most common cancer in women followed by thyroid cancer. The purpose of this study is to evaluate the possibility of using deep - run model for segmentation of breast masses and to identify the best deep-run model for breast mass segmentation. In this study, data of patients with breast masses were collected at Asan Medical Center. We used 596 images of mammography and 596 images of gold standard. In the area of interest of the medical image, it was cut into a rectangular shape with a margin of about 10% up and down, and then converted into an 8-bit image by adjusting the window width and level. Also, the size of the image was resampled to $150{\times}150$. In Deconvolution net, the average accuracy is 91.78%. In U-net, the average accuracy is 90.09%. Deconvolution net showed slightly better performance than U-net in this study, so it is expected that deconvolution net will be better for breast mass segmentation. However, because of few cases, there are a few images that are not accurately segmented. Therefore, more research is needed with various training data.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5133-5138
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• 2016

Background: Breast cancer is one of the most common cancers and a major public health problem in developing countries. However, early detection and treatment may be achieved by breast self-examination (BSE). Despite the importance of BSE in reducing the incidence of breast cancer and esultant deaths, the disease continues to be the most common cause of cancer death among women in Iran.This study aimed to determine the effects of self-care education on performance of BSE among women referring to health centers in our country. Materials and Methods: This quasi-experimental interventional study with pretest/posttest control group design was conducted on 168 women referred to health centers. The data were collected using a validated researcher-made questionnaire including demographic variables and trans-theoretical model constructs as well as a checklist assessing BSE behavior. The instruments were administered to groups with and without self-care education before, a week after, and 10 weeks after the intervention. Then, the data were entered into the SPSS statistical software (version 19) and analyzed using independent sample t-tests, paired sample t-test, repeated measures ANOVA, Chi-square, and Friedman tests (p<0.05). Results: The results showed an increase in the intervention group's mean scores of trans-theoretical model constructs (stages of change, self-efficacy, decisional balance, and processes of change) and BSE behavior compared to the control group (p<0.001). Conclusion: The study confirmed the effectiveness of aneducational intervention based ona trans-theoretical model in performing BSE. Therefore, designing educational interventions based on this model is recommended to improve women's health and reduce deaths due to breast cancer.

• Korean Journal of Adult Nursing
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• v.21 no.6
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• pp.666-677
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• 2009

Purpose: This study was to test a theoretical model examining the relationships among social support, illness demands, marital adjustment, family coping and family functioning in couples more than three years after breast cancer diagnosis. Methods: A causal modeling methodology was used to test the specified relationships in the recursive theoretical model. A total of 60 couples with breast cancer were recruited from January to April 2005. Five standardized questionnaires were used to measure the theoretical concepts: social support (ISSB), illness demands (DOII), marital adjustment (DAS), family coping (F-COPES), and family functioning (FACESII). Results: Path analysis results from the wives and the husbands revealed different patterns. Three hypotheses were supported in the wife model as predicted: social support and family coping, family coping and family functioning, and social support and marital adjustment (trend). Five hypotheses were supported in the husband model as predicted: social support and illness demands, also social support and marital adjustment, illness demands and marital adjustment, marital adjustment and family coping, and family coping and family functioning. Conclusion: This study provides valuable information for developing various interventions with social support for improving family functioning of breast cancer couples in the middle adaption stage (more than three years after diagnosis).

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.20
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• pp.8719-8724
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• 2014

Background: Breast cancer incidence is increasing among South Asian migrants to the United States (US). However, their utilization of cancer screening services is poor. This study characterizes attitudes of South Asians towards breast health and screening in a community sample. Materials and Methods: A cross-sectional survey based on the Health Belief Model (HBM) was conducted among South Asians (n=124) in New Jersey and Chicago. The following beliefs and attitudes towards breast cancer screening were assessed-health motivation, breast self-examination confidence, breast cancer susceptibility and fear, and mammogram benefits and barriers. Descriptive statistics and Spearman rank correlation coefficients were computed for HBM subscales. Findings: Mean age of participants was 36 years with an average 10 years stay in the US. Most women strived to care for their health ($3.82{\pm}1.18$) and perceived high benefits of screening mammography ($3.94{\pm}0.95$). However, they perceived lower susceptibility to breast cancer in the future ($2.30{\pm}0.94$). Conclusions: Increasing awareness of breast cancer risk for South Asian women may have a beneficial effect on cancer incidence because of their positive attitudes towards health and breast cancer screening. This is especially relevant because South Asians now constitute one of the largest minority populations in the US and their incidence of breast cancer is steadily increasing.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.9643-9647
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• 2014

Background: Several studies have been performed to investigate the association of the HER2 Ile655Val polymorphism and breast cancer risk. However, the results were inconsistent. To understand the precise relationship, a meta-analysis was here conducted. Materials and Methods: A search of PubMed conducted to investigate links between the HER2 Ile655Val polymorphism and breast cancer, identified a total of 32 studies, of which 29, including 14,926 cases and 15,768 controls, with odds ratios (ORs) with 95% confidence intervals were used to assess any association. Results: In the overall analysis, the HER2 Ile655Val polymorphism was associated with breast cancer in an additive genetic model (OR=1.136, 95% CI 1.043-1.239, p=0.004) and in a dominant genetic (OR=1.118, 95% CI 1.020-1.227, p=0.018), while no association was found in a recessive genetic model. On subgroup analysis, an association with breast cancer was noted in the additive genetic model (OR=1.111, 95% CI: 1.004-1.230, p=0.042) for the Caucasian subgroup. No significant associations were observed in Asians and Africans in any of the genetic models. Conclusions: In summary, our meta-analysis findings suggest that the HER2 Ile655Val polymorphism is marginally associated with breast cancer susceptibility in worldwide populations with additive and dominant models, but not a recessive model.