| Project/Area Number |
10470436
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Surgical dentistry
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
NAGATA Tetsuji Kyushu University Faculty of Dentistry, Assistant, 歯学部, 助手 (60264058)
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| Co-Investigator(Kenkyū-buntansha) |
ARITA Seizaburou Kansai Medical University, Professor, 医学部, 教授 (20098601)
OZEKI Satoru Fukuoka Dental College, Professor, 歯学部, 教授 (80117077)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 1999: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 1998: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | fuzzy inference / tongue cancer / lymph node metastasis / prediction / multivariate analysis / 口腔癌 / リンパ節転移 / ファジイ推論 |
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| Research Abstract |
The purpose of this study was to predict lymph node metastasis in tongue cancer. The subjects were 137 patients with squamous cell carcinoma of the tongue who had been treated at our department during the 20-year period from 1971 through 1990. We first used multivariate analysis and then fuzzy inference to assess the accuracy of these methods for predicting lymph node metastasis. Based on the results of the chi-square test and a correlation matrix, T category, N category, keratinization, mitosis and mode of invasion were selected as parameters for multivariate analysis. Among the 137, 104 were correctly identified by this multivariated analysis. For the prediction using fuzzy inference, the patients were first divided into two groups : those treated surgically and those given radiotherapy. Three items, i.e., tumor size, keratinization and mode of invasion were used for analysis. Then, an "If-Then rule" was applied to each of the two groups. The accurate rate of the 137 cases was 78.1%, sensitivity was 69.0%, and specificity was 84.8%. These figures exceeded the accuracy obtained by the multivariate analyses. Fuzzy inference appears to be useful for predicting lymph node metastasis.
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