• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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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.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.556-561
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• 2010

This paper proposes a three-dimensional haptotaxis model to simulate the migration of the population of cancer cells. The invasion of the cancer cells relates with the hapto- and the effect of the energy between cells and (ECM). The diffuse interface model is employed, which incorporates haptotaxis mechanism and interface energies. The semi-implicit Fourier spectral scheme is adopted for efficient complications. The simulation results reveal rich dynamics of cancer cells migration.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.709-710
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• 2002

Cancer angiogenesis is simulated using a two dimensional model. Governing equation of angiogenesis is a TAE (Tumor angiogenesis factor) conservation equation in time and space. A stochastic process model is utilized to simulate vessel formation, proliferation, and migration to a cancer pellet. Numerical results are presented especially in case of growing cancer.

• Korean Journal of Health Education and Promotion
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• v.23 no.4
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• pp.29-45
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• 2006

Objectives: The aim of this study was to determine the distribution of stages of adoption in stomach cancer screening and elucidate differences among stages. Methods: A randomly selected sample of 712 Korean males and females aged 40 years or over were interviewed. Stomach cancer screening intention and behavior, sociodemographic characteristics, beliefs, self-efficacy and reinforcing characteristics were assessed. Results: The majority of participants were not on-schedule screening(unaware 3.2%, unengaged 20.8%, deciding about acting 24.0%, decided not to act 9.6%, decided to act 14.5%, acting 9.7%, maintenance 18.3%). Perceived susceptibility, perceived barriers, self-efficacy, other cancer screening experiences were significantly associated with higher compared to lower Precaution Adoption Process Model(PAPM) stages. Conclusions: This study appears to be applicable of the Precaution Adoption Process Model to understanding stomach cancer screening behavior. Our results suggest that it is needed to develop the tailored message for adherence of stomach cancer screening.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5669-5673
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• 2015

Background: Breast cancer is the most prevalent kind of cancer among women in Iran. Regarding the importance of cancer prevention and considerable variation of breast cancer incidence in different parts of the country, it is necessary to recognize regions with high incidence of breast cancer and evaluate the role of potential risk factors by use of advanced statistical models. The present study focussed on incidence of breast cancer in Iran at the province level and also explored the impact of some prominent covariates using Bayesian models. Materials and Methods: All patients diagnosed with breast cancer in Iran from 2005 to 2008 were included in the study. Smoking, fruit and vegetable intake, physical activity, obesity and the Human Development Index (HDI), measured at the province level, were considered as potential modulating factors. Gamma-Poisson, log normal and BYM models were used to estimate the relative risk of breast cancer in this ecological investigation with and without adjustment for the covariates. Results: The unadjusted BYM model had the best fit among applied models. Without adjustment, Isfahan, Yazd, and Tehran had the highest incidences and Sistan- Baluchestan and Chaharmahal-Bakhtiari had the lowest. With the adjusted model, Khorasan-Razavi, Lorestan and Hamedan had the highest and Ardebil and Kohgiluyeh-Boyerahmad the lowest incidences. A significantly direct association was found between breast cancer incidence and HDI. Conclusions: BYM model has better fit, because it contains parameters that allow including effects from neighbors. Since HDI is a significant variable, it is also recommended that HDI should be considered in future investigations. This study showed that Yazd, Isfahan and Tehran provinces feature the highest crude incidences of breast cancer.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5107-5111
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• 2016

Background: Epidemiological studies have indicated an increasing incidence of radiation induced secondary cancer (SC) in breast cancer patients after radiotherapy (RT), most commonly in the contra-lateral breast (CLB). The present study was conducted to estimate the SC risk in the CLB following 3D conformal radiotherapy techniques (3DCRT) including wedge field and forward intensity modulated radiotherapy (fIMRT) based on the organ equivalent dose (OED). Material and Methods: RT plans treating the chest wall with conformal wedge field and fIMRT plans were created for 30 breast cancer patients. The risks of radiation induced cancer were estimated for the CLB using dose-response models: a linear model, a linear-plateau model and a bell-shaped model with full dose response accounting for fractionated RT on the basis of OED. Results: The plans were found to be ranked quite differently according to the choice of model; calculations based on a linear dose response model fIMRT predict statistically significant lower risk compared to the enhanced dynamic wedge (EDW) technique (p-0.0089) and a non-significant difference between fIMRT and physical wedge (PW) techniques (p-0.054). The widely used plateau dose response model based estimation showed significantly lower SC risk associated with fIMRT technique compared to both wedge field techniques (fIMRT vs EDW p-0.013, fIMRT vs PW p-0.04). The full dose response model showed a non-significant difference between all three techniques in the view of second CLB cancer. Finally the bell shaped model predicted interestingly that PW is associated with significantly higher risk compared to both fIMRT and EDW techniques (fIMRT vs PW p-0.0003, EDW vs PW p-0.0032). Conclusion: In conclusion, the SC risk estimations of the CLB revealed that there is a clear relation between risk associated with wedge field and fIMRT technique depending on the choice of model selected for risk comparison.

• Journal of Radiation Protection and Research
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• v.42 no.2
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• pp.130-140
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• 2017

Background: With the need for a domestic level 3 probabilistic safety assessment (PSA), it is essential to develop a Korea-specific code. Health effect assessments study radiation-induced impacts; in particular, long-term health effects are evaluated in terms of cancer risk. The objective of this study was to analyze the latest cancer risk models developed by foreign organizations and to compare the methodology of how they were developed. This paper also provides suggestions regarding the development of Korean cancer risk models. Materials and Methods: A review of cancer risk models was carried out targeting the latest models: the NUREG model (1993), the BEIR VII model (2006), the UNSCEAR model (2006), the ICRP 103 model (2007), and the U.S. EPA model (2011). The methodology of how each model was developed is explained, and the cancer sites, dose and dose rate effectiveness factor (DDREF) and mathematical models are also described in the sections presenting differences among the models. Results and Discussion: The NUREG model was developed by assuming that the risk was proportional to the risk coefficient and dose, while the BEIR VII, UNSCEAR, ICRP, and U.S. EPA models were derived from epidemiological data, principally from Japanese atomic bomb survivors. The risk coefficient does not consider individual characteristics, as the values were calculated in terms of population-averaged cancer risk per unit dose. However, the models derived by epidemiological data are a function of sex, exposure age, and attained age of the exposed individual. Moreover, the methodologies can be used to apply the latest epidemiological data. Therefore, methodologies using epidemiological data should be considered first for developing a Korean cancer risk model, and the cancer sites and DDREF should also be determined based on Korea-specific studies.

• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.29-34
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• 2005

DNA microarray becomes a major tool for the investigation of global gene expression in all aspects of cancer and biomedical research. DNA microarray experiment generates enormous amounts of data and they are meaningful only in the context of a detailed description of microarrays, biomaterials, and conditions under which they were generated. MicroArray Gene Expression Data (MGED) society has established microarray standard for structured management of these diverse and large amount data. MGED MAGE-OM (MicroArray Gene Expression Object Model) is an object oriented data model, which attempts to define standard objects for gene expression. To assess the relevance of DNA microarray analysis of cancer research it is required to combine clinical and genomics data. MAGE-OM, however, does not have an appropriate structure to describe clinical information of cancer. For systematic integration of gene expression and clinical data, we create a new model, Cancer Genomics Object Model.