| Project/Area Number |
09671932
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
病態科学系歯学(含放射線系歯学)
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| Research Institution | Showa University School of Dentistry |
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Principal Investigator |
ARAKI Kazuyuki Showa University, School of Dentistry, Associate Professor, 歯学部, 助教授 (50184271)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAMOTO Mika Showa University, School of Dentistry, Assistant, 歯学部, 助手 (30276604)
SAKAINO Rie Showa University, School of Dentistry, Assistant, 歯学部, 助手 (50153862)
KIMURA Yukinori Showa University, School of Dentistry, Assistant Professor, 歯学部, 講師 (20225072)
SANO Tsukasa Showa University, Achool of Dentistry, Assistant Professor, 歯学部, 講師 (40241038)
HARADA Yasuo Showa University, School of Dentistry, Associate Professor, 歯学部, 助教授 (30119250)
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| Project Period (FY) |
1997 – 1998
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| Project Status |
Completed (Fiscal Year 1998)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 1998: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1997: ¥2,500,000 (Direct Cost: ¥2,500,000)
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| Keywords | Neural network / Computer assisted diagnosis / Malignant tumor / Image diagnosis / Lymph node / Ultrasonography / Metastasis / コンピューター診断支緩 / 頚部 |
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| Research Abstract |
The presence of cervical lymph node metastases in patients with oral cancer is of great prognostic and therapeutic importance. The criteria whether the node is metastatic or reactive has not been well established. Meanwhile, artificial neural networks recently introduced in the analysis of diagnostic images may have bright prospects. The purpose of this study is to determine the accuracy indiagnosing the lymph node metastases on the ultrasonographic images using artificial neural network.
Total of 188 nodes was used to the studies, all of which were verified histologically. In first part of the study 138 nodes randomly selected were used. Included among them were 60 nodes metastatic and 78 reactive. An ultrasonographic apparatus was model PT 2600 US scanner using 7.5 MHz B-mode linear scan probe. Seven ultrasonographic features evaluated were central echogenic hilus, echogenity of peripheral parencymal zone, homogeneity of peripheral parencymal zone, margin, border, Max-Min ratio and smallest diameter. To prove the effect of ultrasonographic features on diagnostic accuracy, a variety of network structure and ultrasonographic features were used. According to the result of the first part of the study, we made reporting system using Visual C++. The residual 50 nodes were used for the study in which we examined the ability of the reporting system.
The results showed that central echogenic hilus, the echogenity of peripheral parencyma and Max-Mm ratio were most important features on diagnosis of metastasis. The neural network assisted reporting system improved the diagnostic ability of the unskilled doctors. In conclusion, the neural network may assist the diagnosis of cervical lymph node swelling.
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