• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.2
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• pp.25-28
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• 2019

Purpose In patients with unusual kidney position after $^{99m}Tc-DTPA$ renal dynamic imaging study, the GFR(Glomerular Filtration Rate) values are significantly different according to the depth of the kidney. Thus, we tried to compare the difference of the GFR values between the depth measurement methods and in-vitro test. 30 adult patients who were subjected to renal study. 27 patients were in usual position and 3 patients were in unusual. $555{\pm}37MBq$ of $^{99m}Tc-DTPA$ was administrated to all patients. GE infinia gamma camera was used. GFR values were obtained in-vivo(gates method) and in-vitro(blood). The kidney depth in-vivo was calculated by three methods(tonnensen, manual, taylor). In-vitro, GFR was performed by blood test. Differences in the mean values of GFR and correlation between depth and GFR values were evaluated using the SPSS 12.0 statistical program. The GFR values for 27 patients with kidney in the usual position are as follows(1.tonnensen 2.manual 3.taylor 4.invitro); $69.3{\pm}4.2$, $88.2{\pm}5.6$, $77.8{\pm}4.3$, $82.2{\pm}5.8ml/min$. The three unusual cases are as follows, first(congenital renal anomaly): 66.4, 101.24, 69.07, 94.8 ml/min. second(transplantation kidney): 12.22, 29.99, 19.36, 23.5 ml/min. third(horseshoe kidney): 37.37, 93.54, 35.9, 92.5 ml/min. There was a difference between tonnensen and manual in the usual position of the kidney(p<0.05). There was no significant difference between the other methods. However, there was a significant difference in case of the unusual position of the kidneys. Correlation analysis between both kidney depth and GFR value shows person correlation as follows; Rt kidney: 0.298, Lt kidney: 0.322. When compared with the GFR values in-vitro test, it was useful to calculate the GFR value by measuring the kidney depth using a manual formula in the unusual position of the kidneys. GFR values and kidney depth were significantly related.

• The Journal of Korean Society for Radiation Therapy
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• v.34
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• pp.51-60
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• 2022

Objectives: The purpose is to evaluate dosimetric performance and delivery efficiency of VMAT with Halcyon LINAC for double target spine SBRT Materials and Methods: 12 patients with spine oligometastases were retrospectively studied. Single-isocenter spine SBRT plans was established using Halcyon® with Dual Layer MLC and Truebeam® with High Definition MLC. All patients' plans were created in Eclipse TPS through the identical conditions and optimization. C.I, H.I, G.I (Gradient Index), maximal and volumetric doses to spinal cord and low dose area were evaluated for comparison of both plans. Also, total MU and BOT(Beam On Time) were evaluated. Results: Halcyon plans was no Statistical differences in C.I and H.I. However, the average of G.I was 4.64 for Halcyon, which decreased to 5.5% compared to Truebeam (P<0.001). Halcyon plans demonstrated statistically significant reduced G.I. The average of 50% and 25% isodose volume was 487.56 cc (-3.82%, P<0.001), 1859.45 cc (-4.75%, P<0.001) in Halcyon, respectively. Significantly reduced low dose spill were observed in Halcyon plans. In the evaluation of the spinal cord, the average of Dmean and V10 of Halcyon plans in the sample group with an overlap volume of less than 1 cc was 6.802 Gy (-3.504%, P=0.067), 5.766±1.683 cc (-8.199%, P=0.002), respectively. Halcyon plans demonstrated statistically significant reduced D_mean and V10. For delivery efficiency, MU and BOT(maximum dose rate for each machine), on average, increased in Halcyon plans. However, the average of BOT(800MU/min for each machine) was 648.33 sec for Halcyon (-1.74%, P<0.001). Conclusion: Halcyon plan for double-target spine SBRT demonstrated advantages in the low dose area with a steep dose gradient, while having dosimetrically equivalent target dose distribution and spinal cord protective effect. As a result, Halcyon LINAC produced a dosimetrically improved plan for double-target spine SBRT.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.

• Radiation Oncology Journal
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• v.21 no.4
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• pp.299-305
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• 2003

Purpose: In order to develop the national guide-lines for the standardization of radiotherapy we are planning to establish a web-based, on-line data-base system for laryngeal cancer. As a first step this study was performed to accumulate the basic clinical information of laryngeal cancer and to determine the items needed for the data-base system. Materials and Methods: We analyzed the clinical data on patients who were treated under the diagnosis of laryngeal cancer from January 1998 through December 1999 In the South-west area of Korea. Eligiblity criteria of the patients are as follows: 18 years or older, currently diagnosed with primary epithelial carcinoma of larynx, and no history of previous treatments for another cancers and the other laryngeal diseases. The items were developed and filled out by radiation oncologlst who are members of forean Southwest Radiation Oncology Group. SPSS vl0.0 software was used for statistical analysis. Results: Data of forty-five patients were collected. Age distribution of patients ranged from 28 to 88 years(median, 61). Laryngeal cancer occurred predominantly In males (10 : 1 sex ratio). Twenty-eight patients (62$\%$) had primary cancers in the glottis and 17 (38$\%$) in the supraglottis. Most of them were diagnosed pathologically as squamous cell carcinoma (44/45, 98$\%$). Twenty-four of 28 glottic cancer patients (86$\%$) had AJCC (American Joint Committee on Cancer) stage I/II, but 50$\%$ (8/16) had In supraglottic cancer patients (p=0.02). Most patients(89$\%$) had the symptom of hoarseness. indirect laryngoscopy was done in all patients and direct laryngoscopy was peformed in 43 (98$\%$) patients. Twenty-one of 28 (75$\%$) glottic cancer cases and 6 of 17 (35$\%$) supraglottic cancer cases were treated with radiation alone, respectively. The combined treatment of surgery and radiation was used in 5 (18$\%$) glottic and 8 (47$\%$) supraglottic patients. Chemotherapy and radiation was used in 2 (7$\%$) glottic and 3 (18$\%$) supraglottic patients. There was no statistically significant difference in the use of combined modality treatments between glottic and supraglottic cancers (p=0.20). In all patients, 6 MV X-ray was used with conventional fractionation. The iraction size was 2 Gy In 80$\%$ of glottic cancer patients compared with 1.8 Gy in 59$\%$ of the patients with supraglottic cancers. The mean total dose delivered to primary lesions were 65.98 ey and 70.15 Gy in glottic and supraglottic patients treated, respectively, with radiation alone. Based on the collected data, 12 modules with 90 items were developed or the study of the patterns of care In laryngeal cancer. Conclusion: The study Items for laryngeal cancer were developed. In the near future, a web system will be established based on the Items Investigated, and then a nation-wide analysis on laryngeal cancer will be processed for the standardization and optimization of radlotherapy.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.