• International journal of advanced smart convergence
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• 제13권3호
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• pp.80-88
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• 2024

Breast cancer remains a significant global health burden, necessitating accurate and timely detection for improved patient outcomes. Machine learning techniques have demonstrated remarkable potential in assisting breast cancer diagnosis by learning complex patterns from multi-modal patient data. This study comprehensively evaluates several popular machine learning models, including logistic regression, decision trees, random forests, support vector machines (SVMs), naive Bayes, k-nearest neighbors (KNN), XGBoost, and ensemble methods for breast cancer prediction using the Wisconsin Breast Cancer Dataset (WBCD). Through rigorous benchmarking across metrics like accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), we identify the naive Bayes classifier as the top-performing model, achieving an accuracy of 0.974, F1-score of 0.979, and highest AUC of 0.988. Other strong performers include logistic regression, random forests, and XGBoost, with AUC values exceeding 0.95. Our findings showcase the significant potential of machine learning, particularly the robust naive Bayes algorithm, to provide highly accurate and reliable breast cancer screening from fine needle aspirate (FNA) samples, ultimately enabling earlier intervention and optimized treatment strategies.

• 대한의용생체공학회:의공학회지
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• 제7권2호
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• pp.159-168
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• 1986

As a fundamental study of Near IR tomography system, transmission parameters of the mongolian breast tissue at 904 nm wavelengh were measured. A analytical model to determin the light intensity in the breast was developed and the measurement, and gheoretical calculations were obtained.

• 대한간호학회지
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• 제46권1호
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• pp.128-139
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• 2016

Purpose: This study was designed to evaluate the effects of an individual breast-feeding promotion program to address breast-feeding knowledge, attitude, method and rate of practice for married immigrant women. Methods: A non-equivalent control group quasi-experimental design was used (experimental group=16, control group=17). The intervention consisted of 3 phases: (1) Within 2 hours of delivery - individual breast-feeding training through video/verbal/practical training education and demonstration (2) After 1~2 days - group training using video, model doll, and breast models (3) After 7 days - family visit, counseling, retraining and reinforcement training. The data were analyzed using non-parametric tests with the SPSS program. Results: Married immigrant women who participated in the individual breast-feeding program scored high in knowledge, attitude, method and rate of practice compared to the control group. Conclusion: The results indicate that the individual breast-feeding program is very effective in increasing breast-feeding knowledge, attitude, method and rate of practicing breast feeding for married immigrant women. So, nurses are encouraged to aggressively utilize individual breast-feeding programs to help married immigrant women, who are exposed to vulnerability due to various situations.

• Asian Pacific Journal of Cancer Prevention
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• 제14권11호
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• pp.6857-6862
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• 2013

Background: Breast cancer is the most common cancer in women. All ages are susceptible and more than 90% of the patients can be cured with early diagnosis. Breast self-examination (BSE) and mammography can be useful for this aim. In this study we examined the components of the Champion health belief model to identify if they could predict the intentions of women to perform such screening. Materials and Methods: A total of 380 women aged 30 and above who had referred to health-care centers were assessed for use of breast cancer screening over the past year with a modified health belief model questionnaire. Logistic regression was applied to identify leading independent predictors. Results: In this study 27% of the women performed BSE in the last year but only 6.8% of them used mammography as a way of screening. There were significant differences regarding all components of the model except for perceived severity between women that underwent BSE. over the past year and those that did not. Findings were similar for mammography. Regression analysis revealed that intentions to perform BSE were predicted by perceived self-efficacy and perceived barriers to BSE while intentions to perform mammography were predicted by perceived barriers. Conclusions: This study indicated that self-efficacy can support performance of BSE while perceived barriers are important for not performing both BSE and mammography. Thus we must educate women to increase their self-efficacy and decrease their perceived barriers.

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5081-5084
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• 2015

Background: Breast cancer is a malignant tumor that starts from cells of the breast and is seen mainly in women. It's the most common cancer in women worldwide and is a major threat to health. The purpose of this study was to fit a Cox proportional hazards model for prediction and determination of years of survival in Iranian patients. Materials and Methods: A total of 366 patients with breast cancer in the Cancer Research Center were included in the study. A Cox proportional hazard model was used with variables such as tumor grade, number of removed positive lymph nodes, human epidermal growth factor receptor 2 (HER2) expression and several other variables. Kaplan-Meier curves were plotted and multi-years of survival were evaluated. Results: The mean age of patients was 48.1 years. Consumption of fatty foods (p=0.033), recurrence (p<0.001), tumor grade (p=0.046) and age (p=0.017) were significant variables. The overall 1- year, 3-year and 5-year survival rates were found to be 93%, 75% and 52%. Conclusions: Use of covariates and the Cox proportional hazard model are effective in predicting the survival of individuals and this model distinguished 4 effective factors in the survival of patients.

• 대한간호학회지
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• 제49권4호
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• pp.375-385
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• 2019

Purpose: This study aimed to develop and test a structural model for chemotherapy-related cognitive impairment of breast cancer patients based on a literature review and Hess and Insel's chemotherapy-related cognitive change model. Methods: The Participants consisted of 250 patients who were ${\geq}19$ years of age. The assessment tools included the Menopause Rating Scale, Symptom Experience Scale, Hospital Anxiety and Depression Scale, Everyday Cognition, and Functional Assessment of Cancer Therapy-Breast Cancer. Data were analyzed using the SPSS 21.0 and AMOS 21.0 programs. Results: The modified model was a good fit for the data. The model fit indices were ${\chi}^2=423.18$ (p<.001), ${\chi}^2/df=3.38$, CFI=.91, NFI=.91, TLI=.89, SRMR=.05, RMSEA=.09, and AIC=515.18. Chemotherapy-related cognitive impairment was directly influenced by menopausal symptoms (${\beta}=.38$, p=.002), depression and anxiety (${\beta}=.25$, p=.002), and symptom experiences (${\beta}=.19$, p=.012). These predictors explained 47.7% of the variance in chemotherapy-related cognitive impairment. Depression and anxiety mediated the relations among menopausal symptoms, symptom experiences, and with chemotherapy related cognitive impairment. Depression and anxiety (${\beta}=-.51$, p=.001), symptom experiences (${\beta}=-.27$, p=.001), menopausal symptoms (${\beta}=-.22$, p=.008), and chemotherapy-related cognitive impairment (${\beta}=-.15$, p=.024) had direct effects on the quality of life and these variables explained 91.3%. Conclusion: These results suggest that chemotherapy-related toxicity is highly associated with cognitive decline and quality of life in women with breast cancer. Depression and anxiety increased vulnerability to cognitive impairment after chemotherapy. Nursing intervention is needed to relieve chemotherapy-related toxicity and psychological factor as well as cognitive decline for quality of life in patients undergoing chemotherapy.

• Asian Pacific Journal of Cancer Prevention
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• 제15권16호
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• pp.6811-6817
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• 2014

Background: Breast cancer risk prediction models are widely used in clinical practice. They should be useful in identifying high risk women for screening in limited-resource countries. However, previous models showed poor performance in derived and validated settings. Therefore, we aimed to develop and validate a breast cancer risk prediction model for Thai women. Materials and Methods: This cross-sectional study consisted of derived and validation phases. Data collected at Ramathibodi and other two hospitals were used for deriving and externally validating models, respectively. Multiple logistic regression was applied to construct the model. Calibration and discrimination performances were assessed using the observed/expected ratio and concordance statistic (C-statistic), respectively. A bootstrap with 200 repetitions was applied for internal validation. Results: Age, menopausal status, body mass index, and use of oral contraceptives were significantly associated with breast cancer and were included in the model. Observed/expected ratio and C-statistic were 1.00 (95% CI: 0.82, 1.21) and 0.651 (95% CI: 0.595, 0.707), respectively. Internal validation showed good performance with a bias of 0.010 (95% CI: 0.002, 0.018) and C-statistic of 0.646(95% CI: 0.642, 0.650). The observed/expected ratio and C-statistic from external validation were 0.97 (95% CI: 0.68, 1.35) and 0.609 (95% CI: 0.511, 0.706), respectively. Risk scores were created and was stratified as low (0-0.86), low-intermediate (0.87-1.14), intermediate-high (1.15-1.52), and high-risk (1.53-3.40) groups. Conclusions: A Thai breast cancer risk prediction model was created with good calibration and fair discrimination performance. Risk stratification should aid to prioritize high risk women to receive an organized breast cancer screening program in Thailand and other limited-resource countries.

• 한국환경성돌연변이발암원학회:학술대회논문집
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• 한국환경성돌연변이발암원학회 2002년도 Current Trends in Toxicological Sciences
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• pp.75-75
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• 2002

• Asian Pacific Journal of Cancer Prevention
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• 제14권12호
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• pp.7149-7154
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• 2013

Objective: Increasing scientific evidence suggests that common variants in the PALB2 gene may confer susceptibility to breast cancer, but many studies have yielded inconclusive results. This meta-analysis aimed to derive a more precise estimation of the relationship between PALB2 genetic variants and breast cancer risk. Methods: An extensive literary search for relevant studies was conducted in PubMed, Embase, Web of Science, Cochrane Library, CISCOM, CINAHL, Google Scholar, CNKI and CBM databases from their inception through September 1st, 2013. A meta-analysis was performed using the STATA 12.0 software and crude odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: Six case-control studies were included with a total of 4,499 breast cancer cases and 6,369 healthy controls. Our meta-analysis reveals that PALB2 genetic variants may increase the risk of breast cancer (allele model: OR>1.36, 95%CI: 1.20~1.52, P < 0.001; dominant model: OR>1.64, 95%CI: 1.42~1.91, P < 0.001; respectively). Subgroup analyses by ethnicity indicated PALB2 genetic variants were associated with an increased risk of breast cancer among both Caucasian and Asian populations (all P < 0.05). No publication bias was detected in this meta-analysis (all P > 0.05). Conclusion: The current meta-analysis indicates that PALB2 genetic variants may increase the risk of breast cancer. Thus, detection of PALB2 genetic variants may be a promising biomarker approach.

• Asian Pacific Journal of Cancer Prevention
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• 제14권1호
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• pp.63-68
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• 2013

Background: Tumor angiogenesis correlates with recurrence and appears to be a prognostic factor for both breast and prostate cancers. In the present study, we aimed to investigate the correlation of microvessel density (MVD), a measure of angiogenesis, with nuclear pleomorphism, mitotic count, and vascular invasion in breast and prostate cancers at preclinical and clinical levels. Methods: Samples from xenograft tumors of luminal B breast cancer and prostate adenocarcinoma, established by BT-474 and PC-3 cell lines, respectively, and commensurate human paraffin-embedded blocks were obtained. To determine MVD, specimens were immunostained for CD-34. Nuclear pleomorphism, mitotic count, and vascular invasion were determined using hematoxylin and eosin (H&E)-stained slides. Results: MVD showed significant correlations with nuclear pleomorphism (r=0.68, P=0.03) and vascular invasion (r=0.77, P=0.009) in breast cancer. In prostate cancer, MVD was significantly correlated with nuclear pleomorphism (r=0.75, P=0.013) and mitotic count (r=0.75, P=0.012). In the breast cancer xenograft model, a significant correlation was observed between MVD and vascular invasion (r=0.87, P=0.011). In the prostate cancer xenograft model, MVD was significantly correlated with all three parameters (nuclear pleomorphism, r=0.95, P=0.001; mitotic count, r=0.91, P=0.001; and vascular invasion, r=0.79, P=0.017; respectively). Conclusions: Our results demonstrate that MVD is correlated with nuclear pleomorphism, mitotic count, and vascular invasion at both preclinical and clinical levels. This study therefore supports the predictive value of MVD in breast and prostate cancers.