Iranian Journal of Breast Diseases
Year:2013 | Volume:6 | Issue:1 (20)|Papers Count:8

Back ground: breast cancer treatments and some extra agents using for reducing cancer treatment side effects may affect carbohydrate metabolism and glycolysis and these changes can prolonged. Lactate is the final production of glycolysis and major source of energy during exercise. Regarding to great encouragement of breast cancer patients to exercise, we aimed to investigate blood lactate changes in various exercise intensities in breast cancer patients compared to healthy controls.Materials and Methods: we recruited ten breast cancer patients who had diagnosed with stage I to III breast carcinoma, had 45±3.4 years old, completed treatment at least 5 months ago and ten healthy controls that were matched with these breast cancer patients regarding to age, physical activity, and menstrual condition. Then subjects run on a treadmill for 12 minute at two various intensities (60% and 80% of maximum heart rate). After and before each 12-minute periods, we measured blood lactate levels.Results: every 20 subjects could complete the exercise tests. We found that there were no significant differences in lactate response between breast cancer patients and healthy controls at moderate and vigorous intensity exercises.Conclusion: It seems that there are no differences in lactate responses to exercise between posttreated breast cancer patients and healthy people. However, further research is needed to investigate lactate and other metabolic biomarkers responses to exercise in these patients.

Background: Breast cancer is a widespread disease in women. Early detection of breast cancer is an important factor in cancer treatment. Mammography is the main screening tool for cancer detection and image classification and retrieval based on masses type and texture can help radiologist for better diagnosis. Accuracy in the feature extraction is an important factor in classification and image retrieval.Materials and Methods: In this paper, a breast tissue density classification model is studied. In the proposed method, a smart system classifies the similar mammographic images into the same classes. The system can help the radiologists to reduce the diagnose errors. In this system, the two-directional two-dimensional principal component analysis has been used for feature extraction and dimension reduction of the mammographic images and a support vector machine has been used for image retrieval.Results: The proposed model can be used for mammographic images analysis in the large database. The system can separate the breasts with extremely dense from the normal breasts. Thus, image analysis can be simply done in the various classes.Conclusion: The model is tested on the Mammographic Image Analysis Society (MIAS) database. The average precision rates of the model are about 90%. The proposed model results are compared with the results of other valid literature in a table for better analysis.

Background: Breast cancer is one the most prevalent cancers among the women. Evaluation of quality of life as an important subject is raised in this disease. All physical and mental symptoms of cancer affect the quality of life in breast cancer patients. Use of complementary therapies and relaxation in cancer may be effective in decreasing pain and emotional distress of these patients. One of the complementary therapies is reflexology. The objective of this study is determining the effect of reflexology on quality of life of breast cancer patients receiving chemotherapy.Materials and Methods: This study is a randomized clinical trial which has been applied on 60 patients referred to Breast Cancer Research Center for Chemotherapy, in 2012. The patients were selected randomly in three groups as intervention, control and placebo. In Intervention group, reflexology was implemented for 3 weeks and each session lasted half an hour. In placebo group only relaxation techniques were implemented for 3 weeks. Control group received the routine therapies of breast cancer. Data were collected by standard questionnaires of EORTIC QLQ-C30.V.3 and EORTIC QLQ-BR23.V.3. The questionnaires were filled before intervention and two weeks after end of study.Results: There was no significant difference in demographic characteristics or quality of life score of three groups, before intervention. Total score of quality of life was higher in interventional group compared to placebo group before and two weeks after intervention (p<0.001) Results also indicated a significant difference in total score of quality of life between three test, placebo and control groups after intervention (p<0.001). Considerable improvement was noticed in the different aspects of quality of life in test group comparing to two other placebo and control groups Conclusion: Reduction of pain, anxiety, nausea and other symptoms related to cancer in patients suffering from cancer are assumed as the important cases of nursing cares focus, and precise nursing may subside these symptoms considerably. Using reflexology in patients suffering from breast cancer may improve the quality of life, as an effective method and can be recommended to breast cancer patients.

Background: Breast cancer is the most common cancer among women. Considering the importance of prognostic factors’ effect on survival, the main object of present study was evaluation the correlated factors with survival in different stages of breast cancer.Materials and Methods: In this study, 518 breast cancer patients treated in Pars and Fayyazbakhsh hospitals in 2006-2009 were retrospectively followed up. Data were recorded based on the last status of disease (metastasis or death) in 2012.The effect of prognostic factors on outcomes indifferent stages of the disease were demonstrated by an illness-death model and their relation to death was analyzed by R software.Results: The mean age of the patients was 48.8 years (±10.8). The mean follow-up time was 5.5 years. Number of involved lymph nodes were statistically significant in death and metastasis (p<0.001) and it was found that individuals with the higher number of involved nodes had higher risk of death and metastasis in all transitions. In addition, metastatic patients with higher tumor grade, negative HER-2 and larger tumor size showed higher HR of death (p<0.05). In this study, ER and PR were not significant in death or metastasis. The age of patients was significant in death (p=0.01).Conclusion: This study showed that tumor grade, HER-2 status and tumor size in breast cancer patients with metastasis have important roles in disease survival. Patients with more involved lymph nodes and larger tumor size, have higher risk of metastasis and death.

Background: Breast cancer is one of the most common cancers among women in the world. Aim of this study is determination of predictor factors of health locus of control for breast self-examination based on Health Belief Model.Materials and Methods: In this cross-sectional study, 391 women selected through the convenience sampling from health centers. Data were collected by Champion’s HBM Scale (CHBMS), health locus of control scale and a self administered questionnaire.Results: The results showed that the perceived susceptibility (P=0.015) and health motivation (P=0.000) predict of internal health locus of control and perceived benefits (P=0.034), health motivation (P=0.026) and awareness (P=0.000) predict of external health locus of control. Perceived seriousness s (P=0.023), perceived barrier (P=0.004), health motivation (P=0.007), and awareness (P=0.034) were predictors of chance health locus of control.Conclusion: whereas health performance such as breast self examination is the most probably in person with internal health locus of control, therefore increase of health motivation and abstain of more susceptibility for creating of internal health locus of control is necessary.

Background: The prediction of breast cancer Diagnosis, survivability and Recurrence has been a challenging research problem for many researchers. Thanks to new technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently.Materials and Methods: The main objective of this manuscript is introducing some popular and well known data mining algorithms in breast cancer researches. The medical predictive models are designed to aid physicians to overcome health problems, routine classification tasks which should otherwise be referred to the specialist in a particular area of medicine by extractinghidden patterns.We present the top seven common data mining algorithms (Artificial Neural Networks/ANNs.Decision Trees, Bayes Nets, Naive Bayes, Support Vector Machine, Decision Tree with Naive Bayes) besides the most widely used statistical method (Logistic Regression model).Results: These top 7 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. Decision Trees and Support Vector Machine are optimal models to prediction, survivability and recurrence of breast cancer and trend to show high accuracy.Conclusion: These algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development. Critical success factors in data mining projects are including: accessing enough data, mining in data and reliable variables, build multiple targeted models, less missing data and other key factors depending specific data mining algorithms.