Item type |
itemtype_ver1(1) |
公開日 |
2021-05-31 |
タイトル |
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タイトル |
Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma |
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言語 |
en |
著者 |
Tomita, Hayato
Yamashiro, Tsuneo
Iida, Gyo
Tsubakimoto, Maho
Mimura, Hidefumi
Murayama, Sadayuki
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アクセス権 |
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アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
権利 |
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言語 |
en |
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権利情報Resource |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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権利情報 |
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International |
キーワード |
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主題Scheme |
Other |
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主題 |
Texture analysis |
キーワード |
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主題Scheme |
Other |
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主題 |
machine learning |
キーワード |
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主題Scheme |
Other |
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主題 |
nasopharyngeal cancer |
キーワード |
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主題Scheme |
Other |
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主題 |
malignant lymphoma |
キーワード |
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主題Scheme |
Other |
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主題 |
PET |
内容記述 |
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内容記述 |
Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of this study is to investigate the usefulness of texture features on unenhanced CT for differentiating between nasopharyngeal cancer and nasopharyngeal ML. Thirty patients with nasopharyngeal tumors, including 17 nasopharyngeal cancers and 13 nasopharyngeal MLs, were underwent 18F-FDG PET/CT. All nasopharyngeal cancers and 7 of 13 nasopharyngeal MLs were confirmed by endoscopic biopsy. On unenhanced CT, 34 texture features were analyzed following lesion segmentation in the maximum area of the target lesion. The Mann-Whitney U test and areas under the curve (AUCs) were used for analysis and to compare the maximum standardized uptake values (SUV)max, SUVmean, and 34 texture features. A support vector machine (SVM) was constructed to evaluate the diagnostic accuracy and AUCs of combinations of texture features, with 50 repetitions of 5-fold cross-validation. Differences between the SUVmax and SUVmean for nasopharyngeal cancers and nasopharyngeal MLs were not significant. Significant differences of texture features were seen, as follows: 1 histogram feature (p = 0.038), 3 gray-level co-occurrence matrix features (p < 0.05), and 1 neighborhood gray-level different matrix feature (NGLDM) (p = 0.003). Coarseness in NGLDM provided the highest diagnostic accuracy and largest AUC of 76.7% and 0.82, respectively. SVM evaluation of the combined texture features obtained the highest accuracy of 81.3%, with an AUC of 0.80. Combined texture features can provide useful information for discriminating between nasopharyngeal cancer and nasopharyngeal ML on unenhanced CT. |
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言語 |
en |
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内容記述タイプ |
Abstract |
出版者 |
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言語 |
en |
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出版者 |
Nagoya University Graduate School of Medicine, School of Medicine |
言語 |
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言語 |
eng |
資源タイプ |
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資源タイプresource |
http://purl.org/coar/resource_type/c_6501 |
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タイプ |
departmental bulletin paper |
出版タイプ |
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出版タイプ |
VoR |
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出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
ID登録 |
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ID登録 |
10.18999/nagjms.83.1.135 |
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ID登録タイプ |
JaLC |
関連情報 |
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関連タイプ |
isVersionOf |
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識別子タイプ |
URI |
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関連識別子 |
https://www.med.nagoya-u.ac.jp/medlib/nagoya_j_med_sci/831.html |
収録物識別子 |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
0027-7622 |
収録物識別子 |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2186-3326 |
書誌情報 |
en : Nagoya Journal of Medical Science
巻 83,
号 1,
p. 135-149,
発行日 2021-02
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