• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• 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.

• Korean Journal of Poultry Science
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• v.7 no.2
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• pp.54-76
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• 1980

A survey was conducted to determine the status of health and productivity of poultry farms in Korea. Area included Was Kyunggido where exist nearly 50% of national poultry population. From this area, 41 layer and 34 broiler farms covering 21 Countries were selected randomly for the survey. When farms were divided in the operation size, 95.1% of layer and 82.3% of broiler farms were classified as business or industrial level while the rest were managed in a small scale as part time job. Generally layer farms had been established much earlier than broiler farms. Geographically 10.7% of layer farms were sited near the housing area such as field foreast and rice field. No farms were located near the seashore. The distance from one farm from the other was very close, being 80% of the farms within the distance of 1km and as many as 28% of the farms within loom. This concentrated poultry farming in a certain area created serious problems for the sanitation and preventive measures, especially in case of outbreak of infectious diseases. Average farm size was 5,016${\times}$3.3㎡ for layers and 1,037${\times}$3.3㎡ for broilers. 89.5% of layer ana 70.6% of broiler farms owned the land for farming while the rest were on lease. In 60% of layer farms welters were employed for farming while in the rest their own labour was used. Majority of farms were equipped poorly for taking necessary practice of hygiene and sanitation. The amount of disinfectant used by farms was considerably low. As many as 97.6% of lave. farms were practised with Newcastle(ND) and fowl pox(F$.$pox) vaccine, whereas only 43.6% and 5.1% of broiler farms were practised with ND and F$.$pox vaccine, respectively. In 17-32.7% of farms ND vaccine was used less than twice until 60 days of age and in only 14.6% of farms adult birds were vaccinated every 4months. Monthly expense for preventive measures was over 200,000W in 32% of farms. Only 4.9-2.7% of vaccine users were soaking advice from veterinarians before practising vaccination, 85% of the users trusted the efficacy of the vaccines. Selection of medicine was generally determined by the farm owner rather than by veterinarans on whom 33.3% of farms were dependant. When diseases outbroke, 49.3% of farms called for veterinary hospital and the rest were handled by their own veterinarians, salesmen or professionals. Approximately 70% of farms were satisfied with the diagnosis made by the veterinarians. Frequency of disease outbreaks varied according to the age and type of birds. The livabilities of layers during the period of brooding, rearing ana adultwere 90.5, 98.9 and 75.2%, respectively while the livalibility of broilers until marketing was 92.2%. In layers, average culling age, was 533.3 day and hen housed eggs were 232.7. Average feed conversion rates of layers and broilers were 3.30 and 2.48, respectively. Those figures were considerably higher than anticipated but still far lower than those in developed countries.

• Korean Journal of Poultry Science
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• v.17 no.3
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• pp.179-191
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• 1990

In order to study the effects of supplementation of live yeast(Saccharomyces cerevisiae) on the performance of laying hens, five experiments were conducted. Two experiment were conducted during summer period, one with 37 wk old Dekalb-Delta strain laying birds(Exp. 1) and the other one with 100 wk old molted Nick Chick Brown laying birds(Esp.2) . In each experiment, 240 birds were divided into 12 groups of 20 birds each and randomly distributed. Each of the two experimental diets(Control 71 and 0.05% live yeast supplemented 72) was fed to 6 groups for 4 wks in Exp.1 and 3 wks in Exp.2. Three experiments were conducted during winter period, Exp.2 with 54 wk old Hy-Line strain laying birds, Exp.4 with 52 wk old Hy-Line strain laying birds, and Exp.5 with 36 wk old broiler breeder(Indian River strain). In each experiment, 540 birds were divided into 18 groups of 30 birds each and randomly distributed. Each of the 3 experimental diets(Control：$T_1$0.05% live yeast supplemented：$T_2$ and 0.1% live yeast supplemented ： T$_3$) was fed to 9 groups for 6 wks in Exp.3, 9 wks in Exp.4 and 4 wks in Exp.5. In Exp.4, Latin Square design was employed to determine the effects of switching feeds at 3 wk intervals. All hens were housed in cages of commercial farm and experimental diets were made with commercial layer feeds. In Experiment 1, egg production was significantly(P＜0.05) higher in $T_2$. Feed intake was significantly (P＜higher in 72 at 1st wk but 4 wk average was not significantly different. Feed efficiency was significantly(P＜0.01) better in 72 at End wk but 4 wk average was not significantly different. Other parameters, such as weight, soft egg production, cracked egg production and mortality were not significantly different. In Experiment 2, egg production was significantly(P＜0.05) higher in $T_2$. Feed efficiency was significantly (P＜0.05 and P＜0.01) better at End wk and 3rd wk but 3 wk average was not significantly different. Soft egg production was significantly(P＜0.05) higher in 72. Other parameters were not significantly different. In Experiment 3, egg productions were significantly(P＜0.05) different among treatments ： $T_3$ was higher than $T_1$ and $T_2$ was higher than $T_1$. Egg weight of $T_1$ and $T_2$was significantly(P＜0.05) heavier than $T_3$. Feed intake of $T_2$ and $T_3$ was significantly(P＜0.05) higher than $T_1$ at 6th wk but overall average was not significantly different. Soft egg production were significantly(P＜0.01) different among treatments：$T_1$ was higher than $T_3$ was higher than $T_2$. Feed efficiency cracked e99 Production and mortality were not signifcantly different. In Experiment 4, egg production tended to increase as the level of live yeast supplementation increased but they were not statistically different. In Experiment 5, egg production of broiler breeders of $T_3$ was significantly(P＜0.01) higher than $T_1$. Feed intake of $T_3$ was significantly(P＜0.05) greater than $T_1$ and $T_2$ at 3rd wk but overall average was not significantly different. Fertility and hatchability tended to be higher in the supplemented groups than in the control.

• Korean Journal of Poultry Science
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• v.32 no.4
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• pp.291-300
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• 2005

Two experiments were carried out to assess the appropriate incubation conditions namely; duration, moisture content and the ideal microbial inoculant for fermented dried food waste(EW) offered to broilers. The nutrient utilization of birds fed the FW diets at varying dietary inclusion rates was also compared with a control diet. In Experiment 1, different moisture contents(MC) of 30, 40, 50 and $60\%$ respectively were predetermined to establish the ideal duration of incubation and the microbial inoculant. A 1mL Aspergillus oryzae(AO) $(1.33\times10^5\;CFU/mL)$ was used as the seed inoculant in FW. This results indicated that the ideal MC for incubation was $40\~50\%$ while the normal incubation time was > 72 hours. Consequently, AO seeds at 0.25, 0.50, 0.75 and 1.00mL were inoculated in FW to determine its effect on AO count. The comparative AO count of FW incubated for 12 and 96 hours, respectively showed no significant differences among varying inoculant dosage rates. The FW inoculated with lower AO seeds at 0.10, 0.05 and 0.01mL were likewise incubated for 72 and 96 hours, respectively and no changes in AO count was detected(p<0.05). The above findings indicated that the incubation requirements for FW should be $%40\~50\%$ for 72 hours with an AO seed incoulant dosage rate of 0.10mL. Consequently, in Experiment II, after determining the appropriate processing condition for the FW, 20 five-week old male Hubbard strain were used in a digestibility experiment. The birds were divided into 4 groups with 5 pens(1 bird per pen). The dietary treatments were; Treatment 1 : Control(Basal diet), Treatment 2 : $60\%$ Basal+4$40\%$ FW, Treatment 3 : $60\%$ $Basal+20\%\;FW+20\%$ AFW(Aspergillus oryzae inoculate dried food-waste diet) and Treatment 4: $60\%$ Basal+$40\%$ Am. Digestibility of treatment 2 was lowed on common nutrients and amino acids compared with control(p<0.05) and on crude fat and phosphorus compared with AFW treatments(T3, T4)(plt;0.05). Digestibility of treatment 3 and 4 increased on crude fiber and crude ash compared treatment 2 (p<0.05). Digestibility of control was high on agrinine, leucine, and phenylalnine of essential amino acids compared with treatment 3 and 4(p<0.05), and diestibility of treatment 3 and 4 was improved on arginine, lysine, and threonine of essential amino acids. Finally, despite comparable nutrient utilization among treatments, birds fed the dietary treatment containing AO tended to superior nutrient digestion to those fed the $60\%$ Basa1+$40\%$ FW.

• Radiation Oncology Journal
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• v.19 no.3
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• pp.237-244
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• 2001

Purpose : To assess the tolerance, complete response rate, bladder preservation rate and survival rate in patients with muscle-invading bladder cancer treated with selective bladder preservation protocol. Method and Materials : From October 1990 to June 1998, twenty six patients with muscle-invading bladder cancer (clinical stage T2-4, N0-3, M0) were enrolled for the treatment protocol of bladder preservation. They were treated with maximal TURBT (transurethral resection of bladder tumor) and 2 cycles of MCV chemotherapy (methotrexate, crisplatin, and vinblastine) followed by $39.6\~45\;Gy$ pelvic irradiation with concomitant cisplatin. After complete urologic evaluation (biopsy or cytology), the patients who achieved complete response were planed for bladder preservation treatment and treated with consolidation cisplatin and radiotherapy (19.8 Gy). The patients who had incomplete response were planed to immediate radical cystectomy. If they refused radical cystectomy, they were treated either with TURBT followed by MCV or cisplatin chemotherapy and radiotherapy. The median follow-up duration is 49.5 months. Results : The Patients with stage T2-3a and T3b-4a underwent complete removal of tumor or gross tumor removal by TURBT, respectively. Twenty one out of 26 patients $(81\%)$ successfully completed the protocol of the planned chemo-radiotherapy. Seven patients had documented complete response. Six of them were treated with additional consolidation cisplatin and radiotherapy. One patient was treated with 2 cycles of MCV chemotherapy due to refusal of chemo-radiotherapy. Five of 7 complete responders had functioning tumor-free bladder. Fourteen patients of incomplete responders were further treated with one of the followings : radical cystectomy (1 patient), or TURBT and 2 cycles of MCV chemotherapy (3 patients), or cisplatin and radiotherapy (10 patients). Thirteen patients of them were not treated with planned radical cystectomy due to patients' refusal (9 patients) or underlying medical problems (4 patients). Among twenty one patients, 12 patients $(58\%)$ were alive with their preserved bladder, 8 patients died with the disease, 1 patient died of intercurrent disease. The 5 years actuarial survival rates according to CR and PR after MCV chemotherapy and cisplatin chemoradiotherapy were $80\%\;and\;14\%$, respectively (u=0.001). Conclusion : In selected patients with muscle-invading bladder cancer, the bladder preservation could be achieved by MCV chemotherapy and cisplatin chemo-radiotherapy. All patients tolerated well this bladder preservation protoco. The availability of complete TURBT and the responsibility of neoadjuvant chemotherapy and chemoradiotherapy were important predictors for bladder preservation and survival. The patients who had not achieved complete response after neoadjuvant chemotherapy and chemoradiotherapy should be immediate radical cystectomy. A randomized prospective trial might be essential to determine more accurate indications between cystectomy or bladder preservation.

• Radiation Oncology Journal
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• v.16 no.4
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• pp.389-398
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• 1998

Purpose : To evaluate the role of conventional postoperative adjuvant radiotherapy in the management of supratentorial malignant glioma and to determine favorable prognostic factors affecting survival. Materials and Methods : From Sep. 1985 to Mar. 1997, the number of eligible patients who received postoperative radiotherapy completely was 69. They ranged in age from 7 to 66 years (median, 47). Forty-two (61$\%$) patients were glioblastoma multiforme and the other 27 (39$\%$) were anaplastic astrocytoma. Twenty patients (29$\%$) had Karnofsky score equal or more than 80 preoperatively. Forty-three patients (62$\%$) had symptom duration equal or less than 3 months. Twenty-four patients (35$\%$) had gross total resection and forty patients(58$\%$) had partial resection, the remaining five patients (7$\%$) had biopsy only. Radiotherapy dose ranged from 50.4 Gy to 61.2 Gy (median, 55.8; mode, 59.4) with fraction size of 1 8 Gy-2.0 Gy for 33-83 days(median, 48) except three patients delivered 33, 36, 39 Gr, respectively with fraction size of 3.0 Gy due to poor postoperative performance status. Follow-up rate was 93$\%$ and median follow-up period was 14 months. Results : Overall survival rate at 2 and 3 years and median survival were 38$\%$, 20$\%$, and 16 months for entire patients; 67$\%$, 44$\%$, and 34 months for anaplastic astrocytoma; 18$\%$, 4$\%$, and 14 months for glioblastoma multiforme, respectively (p=0.0001). According to the extent of surgery, 3-year overall survival for gross total resection, partial resection, and biopsy only was 38$\%$, 11$\%$, and 0$\%$, respectively (p=0.02) The 3-year overall survival rates for patients age 40>, 40-59, and 60< were 52$\%$, 8$\%$, and 0$\%$, respectively (p=0.0007). For the variate of performance score 80< vs 80>, the 3-year survival rates were 53$\%$ and 9$\%$, respectively (p=0.008). On multivariate analysis including covariates of three surgical and age subgroups as above, pathology, extent of surgery and age were significant prognostic factors affecting overall survival. On another multivariate analysis with covariates of two surgical (total resection vs others) and two a9e (50> vs 50<) subgroups, then, pathology, extent of surgery and performance status were significant factors instead of age and 3-year cumulative survival rate for the five patients with these three favorable factors was 100$\%$ without serious sequela. Conclusion : We confirmed the role of postoperative conventional radiotherapy in the management of supratentorial malignant glioma by improving survival as compared with historical data of surgery only. Patients with anaplastic astrocytoma, good performance score, gross total resection and/or young age survived longest. Maximum surgical resection with acceptable preservation of neurologic function should be attempted in glioblastoma patients, especially in younger patients. But the survival of most globlastoma patients without favorable factors is still poor, so other active adjuvant treatment modalities should be tried or added rather than conventional radiation treatment alone in this subgroup.