Results of Radiation Therapy for Carcinoma of the Uterine Cervix (자궁경부암의 방사선치료 성적)
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- Radiation Oncology Journal
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- 제13권4호
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- pp.359-368
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- 1995
Purpose : This is a retrospective analysis for pattern of failure, survival rate and prognostic factors of 114 patients with histologically proven invasive cancer of the uterine cervix treated with definitive irradiation. Materials and Methods : One hundred fourteen patients with invasive carcinoma of the cervix were treated with a combination of intracavitary irradiation using Fletcher-Suit applicator and external beam irradiation by 6MV X-ray at the Ewha Womans University Hospital between March 1982 and Mar 1990. The median age was 53 years(range:30-77 years). FIGO stage distribution was 19 for IB, 23 for IIA, 42 for IIB, 12 for IIIA and 18 for IIIB. Summation dose of external beam and intracavitary irradiation to point A was 80-90 Gy(median:8580 cGy) in early stage(IB-IIA) and 85-100 Gy(median:8850 cGy) in advanced stage(IIB-IIIB). Kaplan-Meier method was used to estimate the survival rate and multivariate analysis for progrostic factors was performed using the Log likelihood for Weibull Results : The pelvic failure rates by stage were
Background: The cell-mediated immunity is needed for eradicating the tubercle bacilli. Prostaglandin(PG), especially PG
This thesis looks into the interpretation of the Korean War and mystery genre in Korea in the 1970s by analyzing the special drama
The physicochemical properties, antioxidant capacities, and sensory optimization of taro (Colocasia esculenta) under different aging conditions were investigated to develop black taro. Black taro was processed in three steps (steaming:
Background: Resting pulmonary function tests(PFTs) are routinely used in the evaluation of pulmonary impairment/disability. But the significance of the cardiopulmonary exercise test(CPX) in the evaluation of pulmonary impairment is controvertible. Many experts believe that dyspnea, though a necessary part of the assessment, is not a reliable predictor of impairment. Nevertheless, oxygen requirements of an organism at rest are different from at activity or exercising, and a clear relationship between resting PFTs and exercise tolerance has not been established in patients with chronic pulmonary disease. As well, the relationship between resting PFTs and dyspnea is complex. To investigate the relationship of dyspnea, resting PFTs, and CPX, we evaluated the patients of stabilized chronic pulmonary disease with clinical dyspnea rating(baseline dyspnea index, BDI), resting PFTs, and CPX. Method: The 50 patients were divided into two groups: non-severe and severe group on basis of results of resting PFTs(by criteria of ATS), CPX(by criteria of ATS or Ortega), and dyspnea rating(by focal score of BDI). Groups were compared with respect to pulmonary function, indices of CPX, and dyspnea rating. Results: 1. According to the criteria of pulmonary impairment with resting PFTs,
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used