• Radiation Oncology Journal
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• v.15 no.2
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• pp.129-136
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• 1997

Purpose : We analyzed the survival and failure patterns of cervix cancer patients treated with irradiation alone to evaluate our treatment method and to compare with the others Methods and Materials : Two hundred and twenty cervical cancer patients, Stage IB, II A, and II B who completed the planned treatment between Mar 1987 and December 1991 were analyzed retrospectively. The Stage IB patients were restaged to the Stage IB1 and IB2 by the recently revised FIGO classification, Patients were treated with a combination of external irradiation and the intracavitary brachytherapy Determination of the tumor control was done at the time of 6 months Postirradiation. The follow-up time was ranged from 3 to 115 months and the mean was 62 months and the follow-up rate was $93.6\%$(206/220) Results : The overall 5-year survival rate of Stage IB1 (N=50), IB2(N: 15). II A(N=58), and II B(N=97) was $94\%,\;87\%,\;69\%,\;and\;56\%$. respectively. In the univariate analysis of prognostic factors, stage(0.00), initial Hg level (P=0.00), initial TA-4(tumor-associated) antigen level(p= 0.02), initial CEA level(p=0.02), barrel-shaped tumor(p=0.02), whole cervical involvement (0.00), pelvic tyrnphadenopathy(LAP) in CT(p=0.04), and Post-irradiation adiuvant chemotherapy(P=0.00) were statistically significant in survival analysis. In a while multivariate analysis showed that the stage was the most powerful Prognostic indicator and the Post-irradiation chemotherapy factor also showed the statistical significance. The overall local control rate was $81\%$ and by the stage, $100\%$ in Stage IBI, $86.7\%$ in Stage IBS, $84.5\%$ in Stage IIA, and $68.1\%$ in Stage IIB, respectively The overall tumor recurrence rate was $15.5\%$(27/174) and by the stage, $8\%$(4/50) in Stage IB1, $0\%$(0/l3) in Stage IB2, $22.4\%$(l1/49) in Stage II A, and $19.4\%$(12/62) in Stage II B, respectively. Conclusions : We obtained the similar treatment results to the other's ones in early stage cervical cancer patients. But in Stage II B, the local control rate was lower than that of the other institutes and also the survival was poorer. So it seems to be necessary to reevaluate the treatment method in advanced cervical cancer patients.

• Radiation Oncology Journal
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• v.27 no.4
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• pp.189-193
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• 2009

Purpose: To report on the changes in the patterns of care and survival over time for esthesioneuroblastoma. Materials and Methods: We retrospectively analyzed 42 previously untreated and histologically confirmed esthesioneuroblastoma patients seen between March 1989 and June 2007. According to Kadish's classification, 3 patients (7%) were stage A, 6 (14%) at stage B, and 33 (79%) at stage C. Of the 33 Kadish C patients, 19 and 14 patients were treated from 1989 through 2000 and from 2001 through 2007, respectively. Treatment included surgical resection, radiotherapy, chemotherapy, or a combination of these methods. Chemotherapy was administered to 8 of 19 patients (42%) seen from 1989 through 2000, whereas all of the 14 patients seen from 2001 through 2007 received chemotherapy (p<0.001). No patient was treated by three-dimensional conformal radiotherapy (3D-CRT) from 1989 through 2000, however 8 of 14 patients (67%) seen from 2001 through 2007 underwent 3D-CRT (p<0.001). The median follow-up time for surviving patients was 6.5 years (range, 2.2~15.8 years). Results: The 5-year overall survival (OS) and progression-free survival (PFS) rates for the entire cohort were 53% and 39%, respectively. The 5-year OS was 100% for Kadish stages A or B and 39% for stage C (p=0.007). For patients with stage C disease who were treated from 1989 to 2000 and from 2001 to 2007, the 5-year OS rate was 26% and 59% (p=0.029), respectively and the corresponding 5-year PFS rate was 16% and 46% (p=0.001), respectively. Intraorbital extension and treatment era (1989~2000 vs. 2001~2007) were found as independent factors for OS and PFS in a multivariate analyses. Conclusion: The results of this study suggest that treatment era, which features a distinction in treatment modality and technique with the introduction of 3D-CRT, may be the cause of improved OS and PFS in Kadish stage C patients. To achieve better outcomes for patients with Kadish stage C, combined chemoradiotherapy, especially 3D-CRT, is recommended in addition to surgery.

• Tuberculosis and Respiratory Diseases
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• v.53 no.3
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• pp.309-318
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• 2002

Background : Usual interstitial pneumonia (UIP) is a fatal progressive fibrotic disorder of the lung with unknown etiology and characterized by a poor response to conventional immunosuppressive therapy. The histologic hallmark of UIP is parchy distribution of subpleural fibrosis and fibroblastic foci(FBF) with interposed normal appearing lung. Because FBF is a collection of actively proliferating myofibroblasts, it can be a marker of activity and prognosis of UIP. However, there were contradictory reports about the correlation between the degree of FBF and survival. Therefore we performed this study to investigate the value of FBF as prognostic marker of UIP. Methods : This was a retrospective study on the 46 patients(M:F=33:13, mean age:$59{\pm}12$ years) with UIP diagnosed by the surgical lung biopsy at the Asan Medical Center, Seoul, Korea between 1990 and 2000 and had follow-up of more than a year. All the biopsy specimens were reevaluated and diagnosed as UIP according to the ATS/ERS classification. Semiquantitative grading of FBF(absent, 0; mild 1; moderate 2; marked 3) by the experienced pathologists who did not know the clinical findings were compared to the clinical data and the follow up course. Results : Thirteen patients(28.2%) died of UIP progression during the study period. The median survival time of all the subjects was 26 months after the biopsy. At the univariate analysis, FVC, $D_Lco$, smoking history and the grade of FBF were significantly related to the survial. The survival was longer in subjects with lesser degrees of FBF, higher DLco, higher FVC and history of smoking. However the multivariate analysis with Cox regression test showed the extent of FBF was the only independent prognostic marker of UIP. Conclusion : These data suggested that the extent of FBF on the surgical lung biopsy can be used as a prognostic marker of UIP.

• Radiation Oncology Journal
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• v.19 no.3
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• pp.224-229
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• 2001

Purpose : The aim of this study is to analyze the treatment failure patterns and the risk factors for locoregional or distant failure of uterine cervical carcinoma treated with radiation therapy. Materials and methods . A retrospective analysis was undertaken of 154 patients treated with curative radiation therapy in Gyeongsang National University Hospital from April 1989 through December 1997. According to FIGO classification, 12 patients were stage IB, 24 were IIA, 98 were IIB, 1 were IIIA, 17 were IIIB, 2 were IVA. Results : Overall treatment failure rate was $42.1\%$ (65/154), and that of complete responder was $31.5\%$ (41/130). Among 65 failures, 25 failed locoregionally, another 25 failed distantly, and 15 failed locoregionally and distantly. Multivariate analysis confirmed tumor size (>4 cm) as risk factor for locoregional failure, and tumor size (>4 cm), pelvic lymph node involvement as risk factors for distant failure. Conclusion : On the basis of results of our study and recent published data of prospective randomized study for locally advanced uterine cervical carcinoma, we concluded that uterine cervical carcinoma with size more than 4 cm or pelvic lymph node involvement should be treated with concurrent chemoradiation.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.