• Radiation Oncology Journal
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• v.33 no.2
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• pp.126-133
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• 2015

Purpose: To evaluate the outcomes of adjuvant radiotherapy (RT) and to analyze prognostic factors of survival in the International Federation of Gynecology and Obstetrics (FIGO) IB-IIA uterine cervical cancer. Materials and Methods: We retrospectively reviewed the medical records of 148 patients with FIGO IB-IIA uterine cervical cancer who underwent surgery followed by adjuvant RT at the Yonsei Cancer Center between June 1997 and December 2011. Adjuvant radiotherapy was delivered to the whole pelvis or an extended field with or without brachytherapy. Among all patients, 57 (38.5%) received adjuvant chemotherapy either concurrently or sequentially. To analyze prognostic factors, we assessed clinicopathologic variables and metabolic parameters measured on preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). To evaluate the predictive performance of metabolic parameters, receiver operating characteristic curve analysis was used. Overall survival (OS) and disease-free survival (DFS) were analyzed by the Kaplan-Meier method. Results: The median follow-up period was 63.2 months (range, 2.7 to 206.8 months). Locoregional recurrence alone occurred in 6 patients, while distant metastasis was present in 16 patients, including 2 patients with simultaneous regional failure. The 5-year and 10-year OSs were 87.0% and 85.4%, respectively. The 5-year and 10-year DFSs were 83.8% and 82.5%, respectively. In multivariate analysis, pathologic type and tumor size were shown to be significant prognostic factors associated with both DFS and OS. In subset analysis of 40 patients who underwent preoperative PET/CT, total lesion glycolysis was shown to be the most significant prognostic factor among the clinicopathologic variables and metabolic parameters for DFS. Conclusion: Our results demonstrated that adjuvant RT following hysterectomy effectively improves local control. From the subset analysis of preoperative PET/CT, we can consider that metabolic parameters may hold prognostic significance in early uterine cervical cancer patients. More effective systemic treatments might be needed to reduce distant metastasis in these patients.

• Journal of Chest Surgery
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• v.38 no.10 s.255
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• pp.705-709
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• 2005

Background: To assess the outcome of pulmonary resection in the management of hemoptysis caused by benign inflammatory lung disease. Material and Method: A longitudinal cohort study of 45 consecutive patients who were presented with hemoptysis and were treated with pulmonary resection from January 1995 to May 2004. The predictive preoperative risk factors of morbidity and recurrence of hemoptysis were analyzed. The mean age of the patients was 47.1 years. The mean follow-up was $35\pm34$ months. Result: The overall hospital mortality rate was $4.4\%(2/45)$. Postoperative complications occurred in 8 patients $(18.6\%)$. Complications were more common in patients who received blood transfusion than non-transfused patients (p=0.002). Patients with tuberculous destroyed lung disease had more amount of preoperative hemoptysis (p=0.002), more probability of transfusion (p=0.001), more probability of undergoing pneumonectomy (p=0.039) and more probability of postoperative morbidity. Patients of undergoing pneumonectomy had more probability of reoperation due to postoperative bleeding (p=0.047). Hemoptysis recurred in five patients but three had been subsided and two sustained during follow-up. A latter two patients had been prescribed with antituberculosis medication due to relapse of tuberculosis. Conclusion: A tuberculous destroyed lung disease has a higher rate of postoperative morbidity than other inflammatory lung diseases. A pneumonectomy in patients of inflammatory lung disease should be performed with great caution especially because of postoperative bleeding. Future study with longer and larger follow-up might show the reasons of recurrence of hemoptysis.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1620-1620
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• 2001

Forages, both grazed and conserved, provide the basis of ruminant production systems throughout the world. More than 90 per cent of the feed energy consumed by herbivorous animals world - wide were provided by forages. With such world - wide dependence on forages, the economic and nutritional necessity of been able to characterize them in a meaningful way is vital. The characterization of forages for productive animals is becoming important for several reasons. Relative to conventional laboratory procedures, Near Infrared Reflectance Spectroscopy (NIRS) offers advantages of simplicity, speed, reduced chemical waste, and more cost-effective prediction of product functionality. NIR spectroscopy represents a radical departure from conventional analytical methods, in that entire sample of forage is characterized in terms of its absorption properties in the near infrared region, rather than separate subsamples being treated with various chemicals to isolate specific components. This forces the analyst to abandon his/her traditional narrow focus on the sample (one analyte at a time) and to take a broader view of the relationship between components within the sample and between the sample and the population from which it comes. forage is usually analysed by NIRS in dry and ground presentation. Initial success of NIRS analysis of coarse forages suggest a need to better understand the potential for analysis of minimally processed samples. Preparation costs and possible compositional alterations could be reduced by samples presented to the instrument in undried and unground conditions. NIRS has gained widespread acceptance for the analysis of forage quality constituents on dry material, however little attention has been given to the use of NIRS for chemical determinations on undried and unground forages. Relatively few works reported the use of NIRS to determine quality parameters on undried materials, most of them on both grass and corn silage. Only two works have been found on the determination of quality parameters on fresh forages. The objectives of this paper were (1) to evaluate the use of NIRS for determination of nitrogen and moisture on undried and unground forage samples and (2) to explore two mathematical treatments and two NIR regions to predict chemical parameters on fresh forage. Four hundred forage samples (n: 400) were analysed in a NIRS 6500 instrument (NIR Systems, PA, USA) in reflectance mode. Two mathematical treatments were applied: 1,4,4,1 and 2,5,5,2. Predictive equations were developed using modified partial least squares (MPLS) with internal cross - validation. Coefficient of determination in calibration (${R^2}_{CAL}$) and standard error in cross-validation (SECV) for moisture were 0.92 (12.4) and 0.92 (12.4) for 1,4,4,1 and 2,5,5,2 respectively, on g $kg^{-1}$ dry weight. For crude protein NIRS calibration statistics yield a (${R^2}_{CAL}$) and (SECV) of 0.85 (19.8) and 0.85 (19.6) for 1,4,4,1 and 2,5,5,2 respectively, on a dry weight. It was concluded that NIRS is a suitable method to predict moisture and nitrogen on fresh forage without samples preparation.

• The Journal of the Korean bone and joint tumor society
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• v.2 no.1
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• pp.1-7
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• 1996

PURPOSE : The purpose of this study is to know the ability of detecting malignant tumor tissue by Tl-201 scan, and to compare with that of Tc-99m-MIBI and Tc-99m-MDP scan. MATERIAL AND METHODS : Between February 1994 and December 1995,38 unselected patients with various bone pathologies were studied prospectively. Eighteen had malignant bone and soft tissue pathologies, while twenty had benign. All patients were studied with Tl-201, Tc-99mMIBI and Tc-99m-MDP scan prior to surgical biopsy. PICKER Prism 2000 gamma camera with high resolution parallel hole collimator was used for scanning. To avoid the interaction of isotope, the early(30min.) and delayed phase(3hrs.) of Tl-20l scan was performed first and Tc-99m-MIBI scan was performed after 30 minutes, and then Tc-99m-MDP scan 48 hours later. The scan images were visually evaluated by a blinded nuclear medicine physician. We could find true positive, true negative, false positive and false negative by the comparison of results with those of biopsy. We calculated positive and negative predictive value(%), sensitivity(%), specificity(%) and diagnostic accuracy(%) of each scan. RESULT : The results of each scan were 85.7, 100, 100, 85, 92.1% in Tl-201, 81, 94.1, 94.4, 80, 86.8% in Tc-99m-MIBI and 50, 66.7, 88.9, 20, 52.6% in Tc-99m-MDP scan. As a conclusion, Tl-201 scan was the most specific and accurate method for detecting malignant tumor tissue. Tc-99m-MIBI scan was also good for malignant tumor searching. CONCLUSION : With our results, we can use Tl-201 scan to differentiate benign from malignant tumor, and to evaluate the response of preoperative chemotherapy or radiotherapy, and to determine the residual tumor or local recurrence. For the better result, we need to have a more detail information about false positive cases and a more objective and quantitative reading technique.

• Journal of Chest Surgery
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• v.29 no.8
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• pp.897-904
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• 1996

A preoperative prediction of postoperative pulmonary function after the pulmonary resection should be made to prevent serious complications and postoperative mortality. There are several methods to predict postoperative lung function but the 99m7c-MAA perfusion lung scan is known as simple, inexpensive and easily tolerated method for patients. We studied the accuracy of the perfusion lung scan in predicting postoperative lung function on 34 patients who received either the resection of one lobe(17 patients) or 2 lobes(2 patients) or pneumonictomy(15 patients). We performed pulmonary function test and lung scan immediately before the operation and calculated the postoperative lung function by substracting the regional lung function which will be rejected. We compared this predictive value to the observed pulmonary function which was done 20 days after the surgery. We also compared the data achieved from 12 patients ho received open thoracotomy due to intrathoracic disease that are not confined in the lung. The correlation coefficient between the predicted value and observed value of FEVI .0 was 0.423, FVC was 0.557 in the pneumonectomy group and FEVI . 0 was 0.693, FVC was 0.591 in the lobectomy group. The correlation coefnclent between the'postoperative value and preoperative value of FEVI .0 was 0.528, FVC was 0.502 in the resectional group and FEVI .0 was 0.871, FVC was 0.896 in the comparatives. We concluded that the perfusion lung scan is accllrate in predicting post-resectional pulmonary function.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.99-106
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• 2020

The purpose of this study is to find out whether image similarity between celebrities and brands on social network service be a determinant to predict advertising effectiveness. To this end, an advertising effect prediction model for celebrity endorsed advertising was created and its validity was verified through a machine learning method which is a big data analysis technique. Firstly, the celebrity-brand image similarity, which was used as an independent variable, was quantified by the association network theory with social big data, and secondly a multiple regression model which used data representing advertising effects as a dependent variable was repeatedly conducted to generate an advertising effect prediction model. The accuracy of the prediction model was decided by comparing the prediction results with the survey outcomes. As for a result, it was proved that the validity of the predictive modeling of advertising effects was secured since the classification accuracy of 75%, which is a criterion for judging validity, was shown. This study suggested a new methodological alternative and direction for big data-based modeling research through celebrity-brand image similarity structure based on social network theory, and effect prediction modeling by machine learning.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.18 no.2
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• pp.75-84
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• 2014

Objectives Eyes, one of visual inspection regions, present important clues to pathological patterns including deficiency and excess patterns to the clinicians. The purpose of this study was to develop Eyes Inspection Questionnaire (EIQ) and to examine which items among the EIQ were more predictive of clinicians' determination for the deficiency and excess patterns. Methods Nine questionnaire items for Visual Inspection of Eyes were extracted through the literature review. These items were presented to the 4 Korean medical doctors who are specialized in visual inspection to conduct the Delphi method. The Korean medical doctors were asked to rate the importance of each items for the corresponding Visual Inspection of Eyes, using a Likert 5-point scale(the 3 points of importance as a cut-off point). Then, out of 75 photographs submitted to the Society of HyungSang Medicine in 2009, 30 portrait pictures were selected as samples. The samples were copied to make 60 sample pictures, and then randomly assigned to 4 clinicians. The 4 clinicians evaluated the 60 samples for excess and deficiency of the eyes and were asked to check the 6 questionnaire items. The results were recorded as 5-points-scale, and their average and standard deviations were calculated. Intra- class reliability test and multi regression test were performed using SPSS 13. Results Intra-class correlation coefficient (ICC) was between 0.750 to 0.841 (P<0.05). Indices for visual inspection of the eyes were: endowment of the bone structure around the eyes; brightness of the eyes; upward deviation of the eyes; eye shapes; and definition of iris. 76.92% of deficiency symptom patterns and 86.42% of the excess symptom patterns matched the patterns predicted by the visual inspection of the eyes, according to the frequency analysis. According to the multiple regression analysis, were significantly related to the excessive symptoms, and to the deficiency symptoms. Conclusion This study is the first attempt of development for checklist of excess and deficiency of Visual Inspection of Eyes and quantitative measurement of excess and deficiency using the Visual Inspection of Eyes by the visual inspection experts. Still, additional studies are needed regarding the relationship visual inspection methods have with existing standards of diagnosis.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.179-189
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• 2016

Recently, the convergence of knowledge based society and information telecommunication technologies has a rapid impact on politics, economics and various fields. Meta-analysis is a statistical integration method that delivers an opportunity to overview the entire result of integrating and analyzing many quantitative research results. Meta-analysis, can see the direction and size of the relationship between variables using the concept of the effect size. The factor determining behavioral intention of consumer in e-commerce can say that critically dependent variable. In a predictive factor determining behavioral intention is typical that perceived value and perceived risk. We conducted a meta-analysis and review of between perceived value, perceived risk and behavioral intention on e-commerce researches. This study focused a total of 33 research papers that established causal relationships in between perceived value, risk and behavioral intention on e-commerce published in Korea academic journals during 2000 and 2016. The result of the meta-analysis might be summarized that the effect size in the path from the perceived value to the behavioral intention with the effect size (r = .526), listed an explanatory power of 28%. In addition, it showed that the effect size in the path from perceived risk to the behavioral intention with the effect size (r = -.220), listed a negative explanatory power of 5%. Based on these findings, several theoretical and practical implications were suggested and discussed with the difference from previous researches.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.480-488
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• 2018

The development of information and communication technology has been carried out actively in the field of agriculture to generate valuable information from large amounts of data and apply big data technology to utilize it. Crops and their varieties are determined by the influence of the natural environment such as temperature, precipitation, and sunshine hours. This paper derives the climatic factors affecting the production of crops using the garlic growth process and daily meteorological variables. A prediction model was also developed for the production of garlic per unit area. A big data analysis technique considering the growth stage of garlic was used. In the exploratory data analysis process, various agricultural production data, such as the production volume, wholesale market load, and growth data were provided from the National Statistical Office, the Rural Development Administration, and Korea Rural Economic Institute. Various meteorological data, such as AWS, ASOS, and special status data, were collected and utilized from the Korea Meteorological Agency. The correlation analysis process was designed by comparing the prediction power of the models and fitness of models derived from the variable selection, candidate model derivation, model diagnosis, and scenario prediction. Numerous weather factor variables were selected as descriptive variables by factor analysis to reduce the dimensions. Using this method, it was possible to effectively control the multicollinearity and low degree of freedom that can occur in regression analysis and improve the fitness and predictive power of regression analysis.

• The Journal of Korean Medicine
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• v.41 no.2
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• pp.58-79
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• 2020

Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics. Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting. Results: Prediction accuracy was increased from 1^st to 2^nd and 3^rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1^st (54.69%), 2^nd (73.52%), and 3^rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1^st (0.640), 2^nd (0.816), and 3^rd (0.939) prediction analysis based on reduced weight. Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.