Osamu Manabe1, Hiroshi Ohira2, Kenji Hirata3, Souichiro Hayashi1, Masanao Naya4, Ichizo Tsujino2, Tadao Aikawa4, Kazuhiro Koyanagawa4, Noriko Oyama-Manabe5, Yuuki Tomiyama1, Keiichi Magota1, Keiichiro Yoshinaga6, Nagara Tamaki7. 1. Department of Nuclear Medicine, Hokkaido University Graduate School of Medicine, N15 W7, Kita-Ku, Sapporo, Hokkaido, 0608638, Japan. 2. First Department of Medicine, Hokkaido University Hospital, Sapporo, Japan. 3. Department of Nuclear Medicine, Hokkaido University Graduate School of Medicine, N15 W7, Kita-Ku, Sapporo, Hokkaido, 0608638, Japan. khirata@med.hokudai.ac.jp. 4. Department of Cardiovascular Medicine, Hokkaido University Hospital, Sapporo, Japan. 5. Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan. 6. Diagnostic and Therapeutic Nuclear Medicine, National Institute of Radiological Science, Chiba, Japan. 7. Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Abstract
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.
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 CSpatients (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.
Authors: Brian A Mc Ardle; David H Birnie; Ran Klein; Rob A de Kemp; Eugene Leung; Jennifer Renaud; Jean DaSilva; George A Wells; Rob S Beanlands; Pablo B Nery Journal: Circ Cardiovasc Imaging Date: 2013-07-24 Impact factor: 7.792
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Authors: Nicholas Josselyn; Matthew T MacLean; Christopher Jean; Ben Fuchs; Brianna F Moon; Eileen Hwuang; Srikant Kamesh Iyer; Harold Litt; Yuchi Han; Fatemeh Kaghazchi; Paco E Bravo; Walter R Witschey Journal: Radiol Artif Intell Date: 2021-03-31