• 시스템엔지니어링학술지
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• 제17권2호
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• International Journal of Computer Science & Network Security
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• 제23권1호
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• pp.89-95
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• 2023

Analyzing breast cancer patient files is becoming an exciting area of medical information analysis, especially with the increasing number of patient files. In this paper, breast cancer data is collected from Khartoum state hospital, and the dataset is classified into recurrence and no recurrence. The data is imbalanced, meaning that one of the two classes have more sample than the other. Many pre-processing techniques are applied to classify this imbalanced data, resampling, attribute selection, and handling missing values, and then different classifiers models are built. In the first experiment, five classifiers (ANN, REP TREE, SVM, and J48) are used, and in the second experiment, meta-learning algorithms (Bagging, Boosting, and Random subspace). Finally, the ensemble model is used. The best result was obtained from the ensemble model (Boosting with J48) with the highest accuracy 95.2797% among all the algorithms, followed by Bagging with J48(90.559%) and random subspace with J48(84.2657%). The breast cancer imbalanced dataset was classified into recurrence, and no recurrence with different classified algorithms and the best result was obtained from the ensemble model.

• Archives of Plastic Surgery
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• 제40권3호
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• pp.203-208
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• 2013

Background Preoperative volume assessment is useful in breast reconstruction. Magnetic resonance imaging (MRI) and mammography are commonly available to reconstructive surgeons in the care of a patient with breast cancer. This study aimed to verify the accuracy of breast volume measured by MRI, and to identify any factor affecting the relationship between measured breast volume and actual breast weight to derive a new model for accurate breast volume estimation. Methods From January 2012 to January 2013, a retrospective review was performed on a total of 101 breasts from 99 patients who had undergone total mastectomy. The mastectomy specimen weight was obtained for each breast. Mammographic and MRI data were used to estimate the volume and density. A standard statistical analysis was performed. Results The mean mastectomy specimen weight was 340.8 g (range, 95 to 795 g). The mean MRI-estimated volume was $322.2mL^3$. When divided into three groups by the "difference percentage value", the underestimated group showed a significantly higher fibroglandular volume, higher percent density, and included significantly more Breast Imaging, Reporting and Data System mammographic density grade 4 breasts than the other groups. We derived a new model considering both fibroglandular tissue volume and fat tissue volume for accurate breast volume estimation. Conclusions MRI-based breast volume assessment showed a significant correlation with actual breast weight; however, in the case of dense breasts, the reconstructive surgeon should note that the mastectomy specimen weight tends to overestimate the volume. We suggested a new model for accurate breast volume assessment considering fibroglandular and fat tissue volume.

• Archives of Plastic Surgery
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• 제42권2호
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• pp.131-142
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• 2015

Literature indicates an increased risk of suicide among women who have had cosmetic breast implants. An explanatory model for this association has not been established. Some studies conclude that women with cosmetic breast implants demonstrate some characteristics that are associated with increased suicide risk while others support that the breast augmentation protects from suicide. A systematic review including data collection from January 1961 up to February 2014 was conducted. The results were incorporated to pre-existing suicide risk models of the general population. A modified suicide risk model was created for the female cosmetic augmentation mammaplasty candidate. A 2-3 times increased suicide risk among women that undergo cosmetic breast augmentation has been identified. Breast augmentation patients show some characteristics that are associated with increased suicide risk. The majority of women reported high postoperative satisfaction. Recent research indicates that the Autoimmune syndrome induced by adjuvants and fibromyalgia syndrome are associated with silicone implantation. A thorough surgical, medical and psycho-social (psychiatric, family, reproductive, and occupational) history should be included in the preoperative assessment of women seeking to undergo cosmetic breast augmentation. Breast augmentation surgery can stimulate a systematic stress response and increase the risk of suicide. Each risk factor of suicide has poor predictive value when considered independently and can result in prediction errors. A clinical management model has been proposed considering the overlapping risk factors of women that undergo cosmetic breast augmentation with suicide.

• Asian Pacific Journal of Cancer Prevention
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• 제13권5호
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• pp.2419-2423
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• 2012

Context: Genuine community participation does not denote taking part in an action planned by health care professionals in a medical or top-down approach. Further, community participation and health education on breast cancer prevention are not similar to other activities incorporated in primary health care services in Iran. Objective: To propose a model that provides a methodological tool to increase women's participation in the decision making process towards breast cancer prevention. To address this, an evaluation framework was developed that includes a typology of community participation approaches (models) in health, as well as five levels of participation in health programs proposed by Rifkin (1985&1991). Method: This model explains the community participation approaches in breast cancer prevention in Iran. In a 'medical approach', participation occurs in the form of women's adherence to mammography recommendations. As a 'health services approach', women get the benefits of a health project or participate in the available program activities related to breast cancer prevention. The model provides the five levels of participation in health programs along with the 'health services approach' and explains how to implement those levels for women's participation in available breast cancer prevention programs at the local level. Conclusion: It is hoped that a focus on the 'medical approach' (top-down) and the 'health services approach' (top-down) will bring sustainable changes in breast cancer prevention and will consequently produce the 'community development approach' (bottom-up). This could be achieved using a comprehensive approach to breast cancer prevention by combining the individual and community strategies in designing an intervention program for breast cancer prevention.

• Asian Pacific Journal of Cancer Prevention
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• 제13권4호
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• pp.1533-1537
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• 2012

In general, breast cancer is the most common malignancy among women in developed as well as some developing countries, often being the second leading cause of cancer mortality after lung cancer. Using a parametric log-logistic model to consider the effects of prognostic factors, the present study focused on the 5-year survival of women with the diagnosis of breast cancer in Southern Iran. A total of 1,148 women who were diagnosed with primary invasive breast cancer from January 2001 to January 2005 were included and divided into three prognosis groups: poor, medium, and good. The survival times as well as the hazard rates of the three different groups were compared. The log-logistic model was employed as the best parametric model which could explain survival times. The hazard rates of the poor and the medium prognosis groups were respectively 13 and 3 times greater than in the good prognosis group. Also, the difference between the overall survival rates of the poor and the medium prognosis groups was highly significant in comparison to the good prognosis group. Use of the parametric log-logistic model - also a proportional odds model - allowed assessment of the natural process of the disease based on hazard and identification of trends.

• Asian Pacific Journal of Cancer Prevention
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• 제17권8호
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• pp.3759-3762
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• 2016

Background: Breast cancer risk assessment is a helpful method for estimating development of breast cancer at the population level. Materials and Methods: In this cross-sectional study, participants consisted of a group of 3,847 volunteers ($mean{\pm}SD$ age: $463{\pm}7.59$ years) in a convenience sample of women referred to health centers affiliated to Tehran University of Medical Sciences in Tehran, Iran. The risk of breast cancer was estimated by applying the National Cancer Institute's online version of the Gail Risk Assessment Tool. Results: Some 24.9% of women reported having one first-degree female relative with breast cancer, with 8.05% of them having two or more first-degree relatives with breast cancer. The mean five-year risk of breast cancer for all participants was $1.61{\pm}0.73%$, and 9.36% of them had a five-year risk of breast cancer >1.66%. The mean lifetime risk of breast cancer was $11.7{\pm}3.91%$. Conclusions: The Gail model is useful for assessing probability of breast cancer in Iranian women. Based on the their breast cancer risk, women may decide to accept further screening services.

• Asian Pacific Journal of Cancer Prevention
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• 제17권sup3호
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• pp.5-10
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• 2016

Breast cancer, the second cause of cancer-related death after lung cancer and the most common cancer in women after skin cancer, is curable if detected in early stages of clinical presentation. Knowledge as to any age cut-off points which might have significance for prognostic groups is important in screening and treatment planning. Therefore, determining a change-point could improve resource allocation. This study aimed to determine if a change point for survival might exist in the age of breast cancer diagnosis. This study included 568 cases of breast cancer that were registered in Breast Cancer Research Center, Tehran, Iran, during the period 1986-2006 and were followed up to 2012. In the presence of curable cases of breast cancer, a change point in the age of breast cancer diagnosis was estimated using a mixture survival cure model. The data were analyzed using SPSS (versions 20) and R (version 2.15.0) software. The results revealed that a change point in the age of breast cancer diagnosis was at 50 years age. Based on our estimation, 35% of the patients diagnosed with breast cancer at age less than or equal to 50 years of age were cured while the figure was 57% for those diagnosed after 50 years of age. Those in the older age group had better survival compared to their younger counterparts during 12 years of follow up. Our results suggest that it is better to estimate change points in age for cancers which are curable in early stages using survival cure models, and that the cure rate would increase with timely screening for breast cancer.

• 한국축산식품학회지
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• 제43권2호
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• pp.305-318
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• 2023

This study investigated the protein digestibility of chicken breast and thigh in an in vitro digestion model to determine the better protein sources for the elderly in terms of bioavailability. For this purpose, the biochemical traits of raw muscles and the structural properties of myofibrillar proteins were monitored. The thigh had higher pH, 10% trichloroacetic acid-soluble α-amino groups, and protein carbonyl content than the breast (p<0.05). In the proximate composition, the thigh had higher crude fat and lower crude protein content than the breast (p<0.05). Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) of myofibrillar proteins showed noticeable differences in the band intensities of tropomyosin α-chain and myosin light chain-3 between the thigh and breast. The intrinsic tryptophan fluorescence intensity of myosin was lower in the thigh than in the breast (p<0.05). Moreover, circular dichroism spectroscopy of myosin revealed that the thigh had higher α-helical and lower β-sheet structures than the breast (p<0.05). The cooked muscles were then chopped and digested in the elderly digestion model. The thigh had more α-amino groups than the breast after both gastric and gastrointestinal digestion (p<0.05). SDS-PAGE analysis of the gastric digesta showed that more bands remained in the digesta of the breast than that of the thigh. The content of proteins less than 3 kDa in the gastrointestinal digesta was also higher in the thigh than in the breast (p<0.05). These results reveal that chicken thigh with higher in vitro protein digestibility is a more appropriate protein source for the elderly than chicken breast.

• Asian Pacific Journal of Cancer Prevention
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• 제15권9호
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• pp.4049-4054
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• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.