فصلنامه بیماری های پستان ایران
سال یازدهم شماره 3 (پیاپی 42، پاییز 1397)

Breast cancer is one of the most common cancers among women. The purpose of this study was to investigate the effectiveness of compassion focused therapy on body image and marital satisfaction in women with breast cancer. This research utilized a pre-test and post-test semi-experimental design with a control group. A total of 30 subjects were selected from women with breast cancer presenting to Imam Khomeini Hospital in Tehran in 2017. They were then randomly assigned to experimental and control groups (n=15 for each group). The experimental group received a compassion focused therapy for eight sessions of 90 minutes, but the control group did not receive any intervention. The research instruments were body image questionnaire of Cash (2000) and Enrich marital inventory of Fowlers and Olson (1989). Data were analyzed by multivariate analysis of covariance. The mean post-test scores of the experimental group in the components of the body image were apparent evaluation (P<0.043), apparent tendency (P<0.006), fit assessment (P<0.001), tendency to fit (P<0.001), appearance assessment (P<0/030), health tendency (P<0.001), tendency to disease (P<0.001), satisfaction of body parts (P<0.001), mental retardation with overweight (P<0.001) ), Self-classification of weight (P<0.003) is less than the mean post-test scores of the control group (P<0.05). Also, the experimental and control groups have a significant difference in terms of marital satisfaction components (P<0.05), personality issues (P<0.001), marital relationship (P<0.007), solving in contractual response components (P<0.02), satisfaction (P<0.001), financial management (P<0.001), time and leisure (P<0.002), sexual relations (P<0/01), parenting (P<0.001), relatives and relatives (P<0.001)), Male and female equality (P<0.014), ideological orientation (P<0.007), the experimental group was significantly more than marital satisfaction in the control group (P=0.001). The results of this study showed that compassion focused therapy can affect the body image and marital satisfaction in women with breast cancer.

Stress due to cancer diagnosis leads to marital conflict and negatively affects couple interactions, relationship satisfaction, and marital adjustment. Studies have demonstrated the effective role of dyadic coping in the association between stress and marital adjustment. The purpose of this study was to investigate the predictive and mediating role of supportive dyadic coping in the relationship between stress and marital adjustment in women undergoing treatment for breast cancer. The study population consisted of women undergoing treatment for breast cancer in Shiraz and Mashhad. The sample included 186 volunteers who participated in this study. In this study, the Perceived Stress Scale, Dyadic Adjustment Scale, and Dyadic Coping Inventory were used for collecting data. Data were analyzed using multivariate regression and structural equations. The results showed that supportive dyadic coping by oneself and by the partner could positively and significantly predict marital adjustment. Additionally, supportive dyadic coping by oneself and by the partner completely mediated the relationship between stress and marital adjustment. Supportive dyadic coping by oneself and by the partner plays a very important role in the relationship between stress and marital adjustment in women. Therefore, supportive dyadic coping not only reduces the negative effects of stress on marital adjustment and quality, but it can improve marital adjustment.

Today, cancer is the second leading cause of death, and breast cancer is the most common cancer, among women. The levels of apoptotic factors, such as caspases, decrease in tumor, leading to tumor growth. The present study investigated the effect of aerobic exercise on the regulation of caspase-3 and -9 in breast cancer. After familiarization, 20 BALB/c mice received transplants of estrogen receptor–positive MC4-L2 tumor cell line to develop cancer. The animals were then randomized into two groups: control (n = 10) and exercise (n = 10). The exercise group performed incremental endurance training (at 18-22 m/s, equivalent to 55% to 70% of aerobic capacity in mice) for 6 weeks, 5 days per week, while the control group did not exercise. Tumor volume was measured in on a weekly basis with a digital caliper. At the end of the study, mice were sacrificed, and tumor tissues were removed and frozen in liquid nitrogen and stored at -70°C. Tissue levels of caspase-3 and caspase-9 were measured using ELISA kits (catalogue number: SEA626Mu and E03C0551, respectively). The expression of caspase-3 (p = 0.001) and caspase-9 (p = 0.013), and the ratio of heart weight to body weight (p = 0.001) were significantly greater in the exercise group than in controls. On the other hand, the exercise group had a significantly lower rate of tumor growth (p = 0.001) and tumor weight (p = 0.001) compared with the controls. Aerobic exercise can increase the expression of caspase-3 and caspase-9 and reduce tumor growth rate and tumor weight in breast cancer. Therefore, it can be claimed that exercise training can reduce tumor volume, and thus improve the condition of the mice with cancer, by increasing the levels of apoptotic factors.

Cachexia is one of the main cancer-related complications. Changes in levels of cachexia-related factors such as myostatin play an important role in accelerating or muscle wasting. The aim of present study was to investigate the effect of six weeks of endurance training on myostatin levels in female mice with breast cancer. Twenty-four female BALB/c mice, aged 6-8 weeks, were assigned to three groups of 8 mice each: a healthy control, tumor control, and an endurance training + tumor group. The endurance training program was performed for six weeks, five sessions per week, at 14-18 m/min on a rodent treadmill. Forty-eight hours after the last exercise session, gastrocnemius muscle tissue was removed and myostatin levels were determined with ELISA method. Tumor volume was measured with a digital caliper. Results indicated significant decreases in myostatin levels in the training + tumor group when compared with the tumor control group (p < 0.001). However, myostatin levels in the control group were significantly low compared with the tumor control and training + tumor groups (p < 0.05). Moreover, significant increases in the gastrocnemius muscle weight (p = 0.015) and decreases in tumor volume (p < 0.001) were observed in the training + tumor group. It seems that decreased myostatin levels due to endurance training have an important role in the prevention of muscle wasting. According to our findings, endurance training, in addition to suppressing tumor growth, can be considered an effective strategy for combating muscle loss in cancer patients.

Staging is one of the most important factors determining the survival of a patient suffering from breast cancer and plays a key role in choosing treatment modalities. The method of choice for cancer staging is surgery followed by histological evaluation. However, finding a predictive algorithm to replace surgery in the staging of breast cancer is both cost- and time-saving and helps physicians to provide appropriate therapeutic techniques. The present paper introduces a strong predictive model for breast cancer staging using data mining techniques. We suggested a mechanized model based on the TNM staging system and the ant colony algorithm. This method would reduce the patient’s mental stress and financial costs because it does not need a surgical operation. The SEER international dataset and a local data set of 1148 women with breast cancer were used to evaluate the system performance, and model accuracy and the area under the ROC curve were calculated for different classifications. Using the TNM system, the accuracy rates were 99.93% and 99.91% for the SEER international dataset and the local dataset, respectively. The accuracy rates were 99.43% and 98.95% for the SEER international dataset and local dataset, respectively, when the ant colony algorithm was applied. Our results indicated that in addition to commonly used features in the TNM system, other features such as vascular invasion, age, blood group, number of children, birthplace, histology, CS Extension, positive regional node, morphology, and Site-Specific Factors 2, 3, and 6 can be used as important factors for breast cancer staging. Based on the results obtained, the two Logistic and Multi-Class Classifiers have the best accuracies for the SEER and local datasets in this study.

The identification of factors influencing the incidence of breast cancer bears great importance. The wide range of symptoms of the disease makes the diagnosis difficult for doctors. Preventing breast cancer could be achieved via a knowledge of the factors affecting the incidence of the disease. The purpose of this paper was to identify variables related to dietary habits, cultural factors, and laboratory results that could contribute to the effective prediction of breast cancer. For this purpose, an optimal model based on genetic bee colony (GBC) algorithm was developed to increase machine learning accuracy. In this study, patient information was collected from the database of Mortaaz subspecialty hospital in Yazd. Medical records of 711 breast cancer patients were screened for 63 variables. Patients had been followed up for at least two years. Variables most affecting the incidence of breast cancer were identified using the GBC algorithm and backup vector machine. Among diet- and culture-related factors, smoking and hookah use, physical inactivity, nighttime employment, and being single and cultural factors related to breast cancer were selected. Also, based on the proposed model, factors such as comparing the results of previous and current mammograms, the duration of taking contraceptive pills, hysterectomy, hormone replacement therapy, nipple retraction and pain, type of discharge, and detecting a mass in mammograms were effective in detecting the disease. No significant relationship was found between breast cancer and factors such as using the microwave in the kitchen, or the type of rice or oil consumed. Using dietary habits and cultural factors in the prediction of breast cancer, the proposed model has the least error rate and the most accuracy compared with other models such as fuzzy and neural networks.