• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of Acupuncture Research
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• v.17 no.2
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• pp.187-208
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• 2000

By the activation of ovary hormone, many morphological changes occur in the epithelial cell lines and muscle cells in rat uterus. These two cells in uterus are important to the implantation of embryo, maintaining pregnancy and starting parturition. One important change associated with the morphological change of these two cells in uterus is the change on prostaglandin(PG) metabolism. Its presence and synthesis in endometriurn and myometrium in uterus affects estrous cycle and the start of embryo implantation in uterus. It also performs as an important modulator in parturition. So the abnormally weak expression of PG causes difficulty during labor and over-expression causes pre-term labor. PG biosynthesis starts from either free or liberated arachidonic acids from membrane phospholipid by phospholipase. Such arachidonic acids are converted into PG catalyzed by Cyclooxygenase. Under normal physiological condition, Cyclooxygenase-1(COX-1) having 602 units of amino acids controls the synthesis of PG. It acts as a local hormone regulating vasomodulation of blood flow, flexible muscle movement, increasing the blood permeability and contributing the protective role in preserving integrity of the stomach lining and Cyclooxygenase-2 (COX-2) is induced by the inflammation, pregnancy and increased its expression until parturition. Lipid metabolite like PG is located in uterine and expression of COX-2 increased with pregnancy. Increased expression of COX proteins in epithelial cells and myometrial cells are told to increase the muscle contractility in uterus but decreased right after the labor in rat. It is a good sign indicating that COX proteins are deeply related to the start of labor. Currently, Several studies report the use of PG and COX-2 inhibitor as medication for controlled abortion or to prevent pre-term labor but they entail various side-effects. Our study proposed to suggest use of acupuncture as an another mediator to control abortion or pre-term labor without causing unnecessary side-effects by those medicines. Two acupuncture sites, LI-4 & SP-6 were selected due to their known efficacy. From the immunohistochemical staining of COX-2, normal expression of COX-2 protein in nonpregnant SD rat's uterus revealed that COX-2 protein was primarily detected in the lumina epithelial lining and in the epithelial cell lining contacting the stromal cells. High resolution optical microscopic scanning revealed distinguishable staining in the myometrial mucosa. LI-4 acupuncture administered nonpregnant rat's uterus showed strong expression for COX-2 in endometrium contacted with lumina epithelial lining of rat uterus and in myometrial mucosa. Stromal cells showed more staining than untreated nonpregnant rat's uterus and stronger staining in stromal cells contacting myometrial layer compared to untreated nonpregnant rat's uterus. SP-6 acupuncture administered nonpregnant rat's uterus showed weak expression for COX-2 in myometrial layers and stromal cells but no staining was visible in lumina epitheliai and glandular epithelial cells. Few stromal cells and myometrial mucosa were positively stained for COX-2. Pregnant SD rat's uterus was also immunostained for COX-2 expression after 18 days of pregnancy. Unlike to untreated nonpregnant rat's uterus, luminal epithelial cells were not positively stained for COX-2 but stronger staining for COX-2 was revealed in stromal cells. LI-4 acupunctured SD rat's uterus had very strong expression of COX-2 in luminal epithelial lining. Few stromal cells showed stronger positive COX-2 staining and myometrial layers also showed more expression than untreated pregnant rat. SP-6 acupuncture administered pregnant SD rat's uterus showed positive expression of COX-2 in epithelial cells of luminal mucosa layer but weaker than that of LI-4 acupuncture treatment's case. However, strong positive staining was revealed in stromal mucosa and myometrial layers. Virgin SD rat's uterus motility index during LI-4 acupuncture was 66.52 % (Prob〉T = 0.0197) compared to its motility before the acupuncture treatment but the motility index was slighdy elevated up to 79.58 % (Prob〉T = 0.1175) after the acupuncture. During the SP-6 acupuncture treatment for 30 minutes, uterus motility index was 90.52 % (Prob〉T = 0.1832) showing lesser decrement but consequently reached similar motility index decreasal to 79.95 % (Prob〉T = 0.0215) after the acupuncture treatment as LI-4 showed. LI-4 acupuncture tend to be a quick treatment to reducing the uterus motility in a virgin rat but eventually both two acupuncture administration created very similar reduction of uterus motility seeing the index after the both acupunctures. The uterus movement monitored during the LI-4 acupuncture administered for 30 minutes, Pregnant SD rat showed decreased motility down to 77.90 % (Prob〉 T = 0.0076) compared to uterus motility before the acupuncture and it continuously decreased down to 71.81 %(Prob〉T = 0.0214) after the removal of needle. The statistical analysis using paired t-test showed significance difference for both two motility indexs at =0.05. SP-6 acupuncture administered to pregnant SD rat also had similar pattern of decreasing uterus motility index down to 74.70 % (Prob〉T = 0.1730) during the initial 30 minutes acupuncture administration and it was continuously lowered to 71.52 % (Prob〉T = 0.0155) after the acupuncture. The paired t-test resuit for SP-6 suggest prompt response of uterus motility index to the SP-6 acupuncture treatment but consequently reached same level of inducing the motility reduction as LI-4 at =0.05 level.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.4
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• pp.569-578
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• 2004

The purpose of this study was to clarify the palatal arch length, width and height in the primary and permanent dentition. Samples were consisted of normal occlusions both in the primary dentition(50 males and 50 females) and in the permanent dentition(50 males and 50 females). With their upper plaster casts were used and through 3-dimensional laser scanning(3D Scanner, DS4060, LDI, U.S.A.), cloud data, polygonization, section curve and loft surface, fit and horizontal plane were based to measure the palatal arch length, width and height(Surfacer 10.0, Imageware, U.S.A.). T-tests were applied for the statistical analyze of the data. The results were as follows : 1. In the measurement values, the values of the male were higher than those of the female except primary anterior palatal height. There were not only statistically significant differences in anterior palatal width(p<0.05) and posterior palatal width(p<0.01) in primary dentition but palatal width(p<0.05), anterior palatal length(p<0.01), middle and posterior palatal length(p<0.05) in permanent dentition between male and female. 2. In the indices of palate, there were statistically significant differences in height-length index(p<0.05) and width-length index(p<0.01) between male and female in primary dentition. In permanent dentition, there was statistically difference between male and female. 3. In the measurement values, posterior palatal width was increased most greatly. Posterior palatal height, anterior palatal width and anterior palatal length were followed by descending order. On the other hand, anterior palatal height and posterior palatal length were decreased. 4. In the indices of palate, the height-length index, the width-length index and posterior height-width index were increased, but the others were decreased.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.9
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• pp.1499-1512
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• 2013

The purpose of this study was to compare the coffee shop visits of Koreans and Mongolians, and to determine the quality attributes that should be managed by Importance-Performance Analysis (IPA). The survey was conducted in Seoul and the Gyeonggi Province of Korea, and at Ulaanbaatar in Mongolia from April to May 2012. The questionnaire was distributed to 380 Koreans and 380 Mongolians, with 253 and 250 responses from the Koreans and Mongolians, respectively, used for statistical analyses. From the results, Koreans visited coffee shops more frequently than Mongolians, with both groups mainly visiting a coffee shop with friends. Koreans also spent more time in a coffee shop than Mongolians. In addition, they generally used a coffee shop, regardless of time. In terms of coffee preference, Koreans preferred Americano and Mongolians preferred Espresso. The most frequently stated purpose of Koreans for visiting a coffee shop was to rest, while Mongolians typically visited to drink coffee. The general price range respondents spent on coffee was less than 4~8 thousand won for the Koreans and 2~4 thousand won for the Mongolians. Both Koreans and Mongolians obtained information about coffee shops from recommendations. According to the IPA results of 20 quality attributes of coffee shops, the selection attributes with high importance but low satisfaction were quality, price, and kindness for Koreans, but none of the attributes was found for Mongolians.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.45-67
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• 2017

According to the Internet Usage Research performed in 2016, the number of internet users and the internet usage have been increasing. Smartphone, compared to the computer, is taking a more dominant role as an internet access device. As the number of smart devices have been increasing, some views that the demand on high-speed internet will decrease; however, Despite the increase in smart devices, the high-speed Internet market is expected to slightly increase for a while due to the speedup of Giga Internet and the growth of the IoT market. As the broadband Internet market saturates, telecom operators are over-competing to win new customers, but if they know the cause of customer exit, it is expected to reduce marketing costs by more effective marketing. In this study, we analyzed the relationship between the cancellation rates of telecommunication products and the factors affecting them by combining the data of 3 cities, Anyang, Gunpo, and Uiwang owned by a telecommunication company with the regional data from KOSIS(Korean Statistical Information Service). Especially, we focused on the assumption that the neighboring areas affect the distribution of the cancellation rates by coupling type, so we conducted spatial cluster analysis on the 3 types of cancellation rates of each region using the spatial analysis tool, SatScan, and analyzed the various relationships between the cancellation rates and the regional data. In the analysis phase, we first summarized the characteristics of the clusters derived by combining spatial information and the cancellation data. Next, based on the results of the cluster analysis, Variance analysis, Correlation analysis, and regression analysis were used to analyze the relationship between the cancellation rates data and regional data. Based on the results of analysis, we proposed appropriate marketing methods according to the region. Unlike previous studies on regional characteristics analysis, In this study has academic differentiation in that it performs clustering based on spatial information so that the regions with similar cancellation types on adjacent regions. In addition, there have been few studies considering the regional characteristics in the previous study on the determinants of subscription to high-speed Internet services, In this study, we tried to analyze the relationship between the clusters and the regional characteristics data, assuming that there are different factors depending on the region. In this study, we tried to get more efficient marketing method considering the characteristics of each region in the new subscription and customer management in high-speed internet. As a result of analysis of variance, it was confirmed that there were significant differences in regional characteristics among the clusters, Correlation analysis shows that there is a stronger correlation the clusters than all region. and Regression analysis was used to analyze the relationship between the cancellation rate and the regional characteristics. As a result, we found that there is a difference in the cancellation rate depending on the regional characteristics, and it is possible to target differentiated marketing each region. As the biggest limitation of this study and it was difficult to obtain enough data to carry out the analyze. In particular, it is difficult to find the variables that represent the regional characteristics in the Dong unit. In other words, most of the data was disclosed to the city rather than the Dong unit, so it was limited to analyze it in detail. The data such as income, card usage information and telecommunications company policies or characteristics that could affect its cause are not available at that time. The most urgent part for a more sophisticated analysis is to obtain the Dong unit data for the regional characteristics. Direction of the next studies be target marketing based on the results. It is also meaningful to analyze the effect of marketing by comparing and analyzing the difference of results before and after target marketing. It is also effective to use clusters based on new subscription data as well as cancellation data.

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.1
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• pp.37-48
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• 2017

Purpose: The most basic conditions of radiation therapy is to prevent unnecessary exposure of normal tissue. The risk factors that are important o evaluate the dose emitted to the lung and heart from radiation therapy for breast cancer. Therefore, comparing the dose factors of a normal tissue according to the radion treatment position and Seeking an effective radiation treatment for breast cancer through the analysis of the correlation relationship. Materials and Methods: Computed tomography was conducted among 30 patients with left breast cancer in supine and prone position. Eclipse Treatment Planning System (Ver.11) was established by computerized treatment planning. Using the DVH compared the incident dose to normal tissue by position. Based on the result, Using the SPSS (ver.18) analyzed the dose in each normal tissue factors and Through the correlation analysis between variables, independent sample test examined the association. Finally The HI, CI value were compared Using the MIRADA RTx (ver. ad 1.6) in the supine, prone position Results: The results of computerized treatment planning of breast cancer in the supine position were V20, $16.5{\pm}2.6%$ and V30, $13.8{\pm}2.2%$ and Mean dose, $779.1{\pm}135.9cGy$ (absolute value). In the prone position it showed in the order $3.1{\pm}2.2%$, $1.8{\pm}1.7%$, $241.4{\pm}138.3cGy$. The prone position showed overall a lower dose. The average radiation dose 537.7 cGy less was exposured. In the case of heart, it showed that V30, $8.1{\pm}2.6%$ and $5.1{\pm}2.5%$, Mean dose, $594.9{\pm}225.3$ and $408{\pm}183.6cGy$ in the order supine, prone position. Results of statistical analysis, Cronbach's Alpha value of reliability analysis index is 0.563. The results of the correlation analysis between variables, position and dose factors of lung is about 0.89 or more, Which means a high correlation. For the heart, on the other hand it is less correlated to V30 (0.488), mean dose (0.418). Finally The results of independent samples t-test, position and dose factors of lung and heart were significantly higher in both the confidence level of 99 %. Conclusion: Radiation therapy is currently being developed state-of-the-art linear accelerator and a variety of treatment plan technology. The basic premise of the development think normal tissue protection around PTV. Of course, if you treat a breast cancer patient is in the prone position it take a lot of time and reproducibility of set-up problems. Nevertheless, As shown in the experiment results it is possible to reduce the dose to enter the lungs and the heart from the prone position. In conclusion, if a sufficient treatment time in the prone position and place correct confirmation will be more effective when the radiation treatment to patient.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Chest Surgery
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• v.38 no.2 s.247
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• pp.116-122
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• 2005

Background: Adverse effects of cardiopulmonary bypass can be avoided by 'Off-pump' coronary artery bypass (OPCAB) surgery. Recent studies have reported that OPCAB had the most beneficial impact on patients at highest risk by reducing bypass-related complications. The purpose of this study is to compare the outcome of OPCAB and conventional coronary artery bypass grafting (CCAB) in patients with poor left ventricular (LV) function. Material and Method: From March 1997 to February 2004, seventy five patients with left ventricular ejection fraction (LVEF) of $35\%$ or less underwent isolated coronary artery bypass grafting at our institute. Of these patients, 33 patients underwent OPCAB and 42 underwent CCAB. Preoperative risk factors, operative and postoperative outcomes, including LV functional change, were compared and analysed. Result: Patients undergoing CCAB were more likely to have unstable angina, three vessel disease and acute myocardial infarction among the preoperative factors. OPCAB group had significantly lower mean operation time, less numbers of total distal anastomoses per patient and less numbers of distal anastomoses per patient in the circumflex territory than the CCAB group. There was no difference between the groups in regard to in-hospital mortality $(OPCAB\; 9.1\%\;(n=3)\;Vs.\;CCAB\;9.5\%\;(n=4)),$ intubation time, the length of stay in intensive care unit and in hospital postoperatively. Postoperative complication occurred more in CCAB group but did not show statistical difference. On follow-up echocardiography, OPCAB group showed $9.1\%$ improvement in mean LVEF, 4.3 mm decrease in mean left ventricular end-diastolic dimension (LVEDD) and 4.2 mm decrease in mean left ventricular end-systolic dimension (LVESD). CCAB group showed $11.0\%$ improvement in mean LVEF, 5.1 mm decrease in mean LVEDD and 5.5 mm decrease in mean LVESD. But there was no statistically significant difference between the two groups. Conclusion: This study showed that LV function improves postoperatively in patients with severe ischemic LV dysfunction, but failed to show any difference in the degree of improvement between OPCAB and CCAB. In terms of operative mortality rate and LV functional recovery, the results of OPCAB were as good as those of CCAB in patients with poor LV function. But, OPCAB procedure was advantageous in shortening of operative time and in decrease of complications. We recommend OPCAB as the first surgical option for patients with severe LV dysfunction.

• Journal of Chest Surgery
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• v.38 no.2 s.247
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• pp.157-163
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• 2005

Background: Clinical outcomes of esophageal cancer have not been satisfactory in spite of the development of surgical skills and protocols of adjuvant therapy. We analyzed the results of corrective surgical patients for esophageal cancer from January 1992 to July 2002. Material and Method: Among 129 patients with esophageal cancer, this study was performed in 68 patients who received corrective surgery. The ratio of sex was 59 : 9 (male : female) and mean age was $61.07\pm7.36$ years old. Chief complaints of this patients were dysphagia, epigastric pain and weight loss, etc. The locations of esophageal cancer were 4 in upper esophagus, 36 in middle, 20 in lower, 8 in esophagogastric junction. 60 patients had squamous cell cancer and 7 had adenocarcinoma, and 1 had malignant melanoma. Five patients had neoadjuvant chemotherapy. Result: The postoperative stage I, IIA, IIB, III, IV patients were 7, 25, 12, 17 and 7, respectively. The conduit for replacement of esophagus were stomach (62 patients) and colon (6 patients). The neck anastomosis was performed in 28 patients and intrathoracic anastomosis in 40 patients. The technique of anastomosis were hand sewing method (44 patients) and stapling method (24 patients). One of the early complications was anastomosis leakage (3 patients) which had only radiologic leakage that recovered spontaneously. The anastomosis technique had no correlation with postoperative leakage, which stapling method (2 patients) and hand sewing method (1 patient). There were 3 respiratory failures, 6 pneumonia, 1 fulminant hepatitis, 1 bleeding and 1 sepsis. The 2 early postoperative deaths were fulminant hepatitis and sepsis. Among 68 patients, 23 patients had postoperative adjuvant therapy and 55 paitents were followed up. The follow up period was $23.73\pm22.18$ months ($1\~76$ month). There were 5 patients in stage I, 21 in stage 2A, 9 in stage IIB, 15 in stage III and 5 in stage IV. The 1, 3, 5 year survival rates of the patients who could be followed up completely was $58.43\pm6.5\%,\;35.48\pm7.5\%\;and\;18.81\pm7.7\%$, respectively. Statistical analysis showed that long-term survival difference was associated with a stage, T stage, and N stage (p<0.05) but not associated with histology, sex, anastomosis location, tumor location, and pre and postoperative adjuvant therapy. Conclusion: The early diagnosis, aggressive operative resection, and adequate postoperative treatment may have contributed to the observed increase in survival for esophageal cancer patients.