WEKO3
アイテム
Use of ¹⁸F-FDG PET/CT texture analysis to diagnose cardiac sarcoidosis
https://repo.qst.go.jp/records/73846
https://repo.qst.go.jp/records/73846746a030a-469a-4eb6-b0a6-826cf0e5f45e
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2018-10-23 | |||||
| タイトル | ||||||
| タイトル | Use of ¹⁸F-FDG PET/CT texture analysis to diagnose cardiac sarcoidosis | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Manabe, Osamu
× Manabe, Osamu× Ohira, Hiroshi× Hirata, Kenji× Hayashi, Souichiro× Naya, Masanao× Tsujino, Ichizo× Aikawa, Tadao× Koyanagawa, kazuhiro× Noriko, Oyama-Manabe× Tomiyama, Yuuki× Magota, Keiichi× Yoshinaga, Keiichiro× Tamaki, Nagara× Yoshinaga, Keiichiro |
|||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Purpose 18F-fluorodeoxyglocose positron emission tomography (FDG PET) plays a significant role in the diagnosis of cardiac sarcoidosis (CS). Texture analysis is a group of computational methods for evaluating the inhomogeneity among adjacent pixels or voxels.We investigated whether texture analysis applied to myocardial FDG uptake has diagnostic value in patients with CS. Methods Thirty-seven CS patients (CS group), and 52 patients who underwent FDG PET/CT to detect malignant tumors with any FDG cardiac uptake (non-CS group) were studied.A total of 36 texture features from the histogram, gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level zone size matrix (GLZSM) and neighborhood gray-level difference matrix (NGLDM), were computed using polar map images. First, the inter-operator and inter-scan reproducibility of the texture features of the CS group were evaluated. Then, texture features of the patients with CS were compared to those without CS lesions. Results Twenty-eight of the 36 texture features showed high inter-operator reproducibility with intraclass correlation coefficients (ICCs) over 0.80. In addition, 17 of the 36 showed high inter-scan reproducibility with ICCs over 0.80. The SUVmax showed no difference between the CS and non-CS group [7.36 ± 2.77 vs. 8.78 ± 4.65, p = 0.45, area under the curve (AUC) = 0.60]. By contrast, 16 of the 36 texture features could distinguish CS from non-CS grsoup with AUC > 0.80. Multivariate logistic regression analysis after hierarchical clustering concluded that long-run emphasis (LRE; P = 0.0004) and short-run low gray-level emphasis (SRLGE; P = 0.016) were significant independent factors that could distinguish between the CS and non-CS groups. Specifically, LRE was significantly higher in CS than in non-CS (30.1 ± 25.4 vs. 11.4 ± 4.6, P < 0.0001), with high diagnostic ability (AUC = 0.91), and had high inter-operator reproducibility (ICC = 0.98). Conclusions The texture analysis had high inter-operator and high inter-scan reproducibility. Some of texture features showed higher diagnostic value than SUVmax for CS diagnosis. Therefore, texture analysis may have a role in semi-automated systems for diagnosing CS. |
|||||
| 書誌情報 |
European Journal of Nuclear Medicine and Molecular Imaging 巻 46, 号 6, p. 1240-1247, 発行日 2018-10 |
|||||
| 出版者 | ||||||
| 出版者 | Springer | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1619-7070 | |||||
| PubMed番号 | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 30327855 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1007/s00259-018-4195-9 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://link.springer.com/article/10.1007%2Fs00259-018-4195-9 | |||||
