• Asian Pacific Journal of Cancer Prevention
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• v.13 no.10
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• pp.5219-5223
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• 2012

Osteopontin (OPN) is an integrin-binding protein, believed to be involved in a variety of physiological cellular functions. The physiology of OPN is best documented in the bone where this secreted adhesive glycoprotein appears to be involved in osteoblast differentiation and bone formation. In our study, we used semi-quantitative RT-PCR of osteopontin in calcification tissue of breast to detect breast cancer metastasis. The obtained data indicate that the expression of osteopontin is related to calcification tissue of breast, and possibly with the incidence of breast cancer. The expression strength of OPN by RT-PCR detection was related to the degree of malignancy of breast lesions, suggesting a close relationship between OPN and breast calcification tissue. The results revealed that expression of OPN mRNA is related to calcification of breast cancer tissue and to the development of breast cancer. Determination of OPN mRNA expression can be expected to be a guide to clinical therapy and prediction of the prognosis of breast cancer patients.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10787-10790
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• 2015

Background: Ovarian cancer is an important cause of mortality in women. The aim of this study was to evaluate the incidence and mortality rates and trends in the Iranian population and make predictions. Materials and Methods: National incidence from Iranian annual of National Cancer Registration report from 2003 to 2009 and National Death Statistics reported by the Ministry of Health and Medical Education from 1999 to 2004 were included in this study. A time series model (autoregressive) was used to predict the mortality for the years 2007, 2008, 2012 and 2013, with results expressed as annual mortality rates per 100,000. Results: The general mortality rate of ovarian cancer slightly increased during the years under study from 0.01 to 0.75 and reaching plateau according to the prediction model. Mortality was higher for older age. The incidence also increased during the period of the study. Conclusions: Our study indicated remarkable increasing trends in ovarian cancer mortality and incidence. Therefore, attention to high risk groups and setting awareness programs for women are needed to reduce the associated burden in the future.

• Journal of Digestive Cancer Research
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• v.3 no.2
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• pp.89-94
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• 2015

The role of adjuvant chemotherapy in patients with stage II colon cancer remains a controversial issue. Adjuvant chemotherapy aims to eliminate any micrometastatic disease that may have been missed, at the time of surgery. Although one prospective study showed a small but statistically significant benefit with respect to the overall survival for those who received adjuvant chemotherapy, multiple pooled data did not demonstrate any benefit of this therapy in patients with stage II colon cancer. Current national and international guidelines for the adjuvant treatment of stage II colon dose not advise routine implementation of adjuvant chemotherapy, but rather recommend selective use of this therapy for patients with high risk of recurrence. High risk features for recurrence include T4 disease, poorly differentiated histology, presence of lymphovascular invasion, presence of perineural invasion, inadequate retrieval of lymph nodes, bowel obstruction, localized perforation, or positive margins. More recently, prediction tools using gene expression cancer profiles are proposed to identify patients who are most likely to have recurrence and therefore may benefit from postoperative chemotherapy in stage II colon cancer. These novel methods together with conventional prognosticators, will allow us to implement more optimized personalizing adjuvant therapy in these patients.

• 대한예방의학회:학술대회논문집
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• 1998.10b
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• pp.311-312
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• 1998

• Radiation Oncology Journal
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• v.30 no.2
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• pp.70-77
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• 2012

Purpose: To evaluate the patterns of tumor shape and to compare tumor volume derived from simple diameter-based ellipsoid measurement with that derived from tracing the entire tumor contour using region of interest (ROI)-based 3D volumetry with respect to the prediction outcome in cervical cancer patients treated with concurrent chemotherapy and radiotherapy. Materials and Methods: Magnetic resonance imaging was performed in 98 patients with cervical cancer (stage IB-IIIB). The tumor shape was classified into two categories: ellipsoid and non-ellipsoid shape. ROI-based volumetry was derived from each magnetic resonance slice on the work station. For the diameter-based surrogate "ellipsoid volume," the three orthogonal diameters were measured to calculate volume as an ellipsoid. Results: The more than half of tumor (55.1%) had a non-ellipsoid configuration. The predictions for outcome were consistent between two volume groups, with overall survival of 93.6% and 87.7% for small tumor (<20 mL), 62.9% and 69.1% for intermediate-size tumor (20-39 mL), and 14.5% and 16.7% for large tumors (${\geq}$40 mL) using ROI and diameter based measurement, respectively. Disease-free survival was 93.8% and 90.6% for small tumor, 54.3% and 62.7% for intermediate-size tumor, and 13.7% and 10.3% for large tumor using ROI and diameter based method, respectively. Differences in outcome between size groups were statistically significant, and the differences in outcome predicted by the tumor volume by two different methods. Conclusion: Our data suggested that large numbers of cervical cancers are not ellipsoid. However, simple diameter-based tumor volume measurement appears to be useful in comparison with ROI-based volumetry for predicting outcome in cervical cancer patients.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4153-4158
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• 2014

Background: This study was aimed to establish a novel method to simultaneously detect expression of four genes, ribonucleotide reductase subunit M1(RRM1), X-ray repair cross-complementing gene 1 (XRCC1), thymidylate synthase (TS) and class III ${\beta}$-tubulin (TUBB3), and to assess their application in the clinic for prediction of response of non-small cell lung cancer (NSCLC) to chemoradiotherapy. Materials and Methods: We have designed four gene molecular beacon (MB) probes for multiplex quantitative real-time polymerase chain reactions to examine RRM1, XRCC1, TUBB3 and TS mRNA expression in paraffin-embedded specimens from 50 patients with advanced or metastatic carcinomas. Twenty one NSCLC patients receiving cisplatin-based first-line treatment were analyzed. Results: These molecular beacon probes could specially bind to their target genes in homogeneous solutions. Patients with low RRM1 and XRCC1 mRNA levels were found to have apparently higher response rates to chemoradiotherapy compared with those with high levels of RRM1 and XRCC1 expression (p<0.05). The TS gene expression level was not significantly associated with chemotherapy response (p>0.05). Conclusions: A method of simultaneously detecting four molecular markers was successfully established and applied for evaluation of chemoradiotherapy response. It may be a useful tool in personalized cancer therapy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3527-3531
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• 2015

Background: Serbia is one of the countries with highest incidence and mortality rates for cervical cancer in Central and South Eastern Europe. Introducing a risk index could provide a powerful means for targeting groups at high likelihood of having an abnormal cervical smear and increase efficiency of screening. The aim of the present study was to create and assess validity ofa index for prediction of an abnormal Pap test result. Materials and Methods: The study population was drawn from patients attending Departments for Women's Health in two primary health care centers in Serbia. Out of 525 respondents 350 were randomly selected and data obtained from them were used as the index creation dataset. Data obtained from the remaining 175 were used as an index validation data set. Results: Age at first intercourse under 18, more than 4 sexual partners, history of STD and multiparity were attributed statistical weights 16, 15, 14 and 13, respectively. The distribution of index scores in index-creation data set showed that most respondents had a score 0 (54.9%). In the index-creation dataset mean index score was 10.3 (SD-13.8), and in the validation dataset the mean was 9.1 (SD=13.2). Conclusions: The advantage of such scoring system is that it is simple, consisting of only four elements, so it could be applied to identify women with high risk for cervical cancer that would be referred for further examination.

• Environmental Analysis Health and Toxicology
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• v.11 no.1_2
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• pp.1-10
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• 1996

A decrease in stratospheric ozone probably caused by chloroflurocarbons (CFCs) emissions, has been observed large parts of-the globe. It is generally accepted that if ozone levels in the stratosphere are depleted, greater amounts of shortwave ultraviolet radiationB (UVB) will reach the earth's surface, resulting in increased incidence of nonmelanoma skin cancer. In this study, we evaluated several mathematical models, such as a power and an exponential model, and a geometric model considering the surface area of a human body part and ages for the prediction of Skin cancer incidence caused by exposure to the UVB radiation. These models basically estimated the risk of skin cancer based on those measurements of the local ozone in stratosphere and UVB. Both were measured at a part of Seoul with a Dobson ozone spectrometer and Robertson-Berger UV Biometer for 1995. As a result, we calculated the point estimation applying a biological amplification factor (BAF), UVB radiation and other factors. We used a Monte-Carlo simulation technique with assumption on the distribution of each considered factor. The sensitivity analysis of model by there components conducted using Gaussian sensitivity method. The annual integral of UVB radiation was 2275 MED (minimal erythema dose)/yr. Also, an estimate of the annual amount of UVB reaching the earth's surface at a korea's latitude and altitude was 3328 MED/yr. The values of the radiation amplification factor (RAF) were ranged from 0.9 to 1.5 in Seoul. To give the effective factors required to model the prediction of skin cancer incidence caused by exposure to the UVB radiation in Korea, we studied the pros and cons of above mentioned models with the application of those parameters measured in Seoul, Korea.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.