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  1. C100 医学部/医学系研究科
  2. C100b 紀要
  3. Nagoya journal of medical science
  4. 83(1)

Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma

https://doi.org/10.18999/nagjms.83.1.135
https://doi.org/10.18999/nagjms.83.1.135
93929e76-856d-46e3-9386-c0162d153b0a
名前 / ファイル ライセンス アクション
13_Tomita.pdf 13_Tomita.pdf (5.2 MB)
Item type itemtype_ver1(1)
公開日 2021-05-31
タイトル
タイトル Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma
言語 en
著者 Tomita, Hayato

× Tomita, Hayato

en Tomita, Hayato

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Yamashiro, Tsuneo

× Yamashiro, Tsuneo

en Yamashiro, Tsuneo

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Iida, Gyo

× Iida, Gyo

en Iida, Gyo

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Tsubakimoto, Maho

× Tsubakimoto, Maho

en Tsubakimoto, Maho

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Mimura, Hidefumi

× Mimura, Hidefumi

en Mimura, Hidefumi

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Murayama, Sadayuki

× Murayama, Sadayuki

en Murayama, Sadayuki

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アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
キーワード
主題Scheme Other
主題 Texture analysis
キーワード
主題Scheme Other
主題 machine learning
キーワード
主題Scheme Other
主題 nasopharyngeal cancer
キーワード
主題Scheme Other
主題 malignant lymphoma
キーワード
主題Scheme Other
主題 PET
内容記述
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.
言語 en
内容記述タイプ Abstract
内容記述
内容記述 This is an Open Access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view the details of this license, please visit (http://creativecommons.org/licenses/by-nc-nd/4.0/).
言語 en
内容記述タイプ Other
出版者
言語 en
出版者 Nagoya University Graduate School of Medicine, School of Medicine
言語
言語 eng
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ departmental bulletin paper
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
ID登録
ID登録 10.18999/nagjms.83.1.135
ID登録タイプ JaLC
関連情報
関連タイプ isVersionOf
識別子タイプ URI
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
収録物識別子タイプ PISSN
収録物識別子 0027-7622
収録物識別子
収録物識別子タイプ EISSN
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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