Performance of Artificial Neural Network in Differentiation between Malignant and Benign Nodules in the Thyroid on Ultrasonography
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概要
- 論文の詳細を見る
The Purpose of our study is to determine the utility of artificial neural network in differentiation between malignant and benign nodules in the thyroid on ultrasonography. We evaluated 109 thyroid nodules. The pathological diagnoses of malignant nodules are papillary cancer (48), follicular cancer (1) and benign nodules are nodular hyperplasia (26), adenomatous goiter (17), benign follicular tumor (4), acute thyroiditis (2), oncocytic adenoma (1) and others (10). The area under the receiver operating characteristic curve (Az) of ANN was 0.9492 (SE: 0.0195). Az of 10 year-experienced radiologist was 0.83 and Az of 4 year-experienced radiologist was 0.75.
- 社団法人電子情報通信学会の論文
- 2007-01-20
著者
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Choi Chul
Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of M
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Kim Kwang
Department Of Radiology College Of Medicine Seoul National University
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Lee Ji
Department Of Dermatology And Internal Medicine Chungbuk National University
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Jeon Yong
Department Of Radiology College Of Medicine Kangwon National University
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KIM Sam
Department of Physics, Yonsei University
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Yoon Dae
Department Of Radiology College Of Medicine Hallym University
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Chang Suk
Department Of Radiology College Of Medicine Hallym University
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Lim Kyoung
Department Of Radiology College Of Medicine Hallym University:department Of Radiology College Of Med
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Kim Kwang
Department Of Anatomy Wonkwang University
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Han Heon
Department of Radiology, College of Medicine, Kangwon National University
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Han Heon
Department Of Radiology College Of Medicine Kangwon National University
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Chang Suk
Department Of Obstetrics & Gynecology Ajou University School Of Medicine
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Choi Chul
Department Of Mechanical Engineering Korea Advanced Institute Of Science And Technology
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Lee Ji
Department Of Advanced Materials Science And Engineering Sungkyunkwan University
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Yoon Dae
Department Of Materials Science And Engineering Korea Advanced Institute Of Science And Technology
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Kim Sam
Department Of Animal Science (insti. Of Agri. & Life Sci.) Gyeongsang National University
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Choi Chul
Department Of Radiology College Of Medicine Hallym University
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Lee Ji
Department Of Radiology College Of Medicine Kangwon National University
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Kim Sam
Department Of Radiology College Of Medicine Kangwon National University
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Jeon Yong
Department Of Obstetrics And Gynecology Seoul National University Bundang Hospital
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Jeon Yong
Department Of Internal Medicine Hanyang University Kuri Hospital
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KIM Kwang
Department of Advanced Materials Engineering (Polymerics Program), Korea Advanced Institute of Science and Technology (KAIST)
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