Japanese journal of radiology · 2021
Texture analysis of delayed contrast-enhanced computed tomography to diagnose cardiac sarcoidosis.
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Tsuneta Satonori, Oyama-Manabe Noriko, Hirata Kenji, Harada Taisuke, Aikawa Tadao, Manabe Osamu, Ohira Hiroshi, Koyanagawa Kazuhiro, Naya Masanao, Kudo Kohsuke
Аннотация
PURPOSE: To investigate the diagnostic value of texture analysis to differentiate cardiac sarcoidosis (CS) from other non-ischemic cardiomyopathies (non-CS).
MATERIALS AND METHODS: Twenty CS patients and 15 non-CS patients who had undergone myocardial CT delayed enhancement (CTDE) were included. A total of 36 texture features were calculated according to the CT attenuation of CTDE. We investigated the diagnostic value to differentiate CS from non-CS. We also assessed the intra- and inter-rater reproducibility for each feature and inter-observer agreement for visual assessment.
RESULTS: Seven extracted features had significantly higher run length non-uniformity (RLNU) values (5.4 × 102 ± 6.2 × 102 vs. 11.2 × 102 ± 4.9 × 102, p = 0.037) and significantly lower low gray-level zone emphasis (LGZE) values (7.1 × 10-3 ± 8.6 × 10-3 vs. 18.1 × 10-3 ± 16.9 × 10-3, p = 0.017) in CS than in non-CS. Intra- and inter-rater reproducibility of RLNU and LGZE were excellent (ICCs > 0.8), while inter-observer agreement of visual assessment was poor (kappa = 0.19). The accuracies of texture analysis were 69% with RLNU and 71% with LGZE, which were better than that of visual assessment.
CONCLUSION: Texture analysis of CTDE could differentiate CS from non-CS with high reproducibility.
MATERIALS AND METHODS: Twenty CS patients and 15 non-CS patients who had undergone myocardial CT delayed enhancement (CTDE) were included. A total of 36 texture features were calculated according to the CT attenuation of CTDE. We investigated the diagnostic value to differentiate CS from non-CS. We also assessed the intra- and inter-rater reproducibility for each feature and inter-observer agreement for visual assessment.
RESULTS: Seven extracted features had significantly higher run length non-uniformity (RLNU) values (5.4 × 102 ± 6.2 × 102 vs. 11.2 × 102 ± 4.9 × 102, p = 0.037) and significantly lower low gray-level zone emphasis (LGZE) values (7.1 × 10-3 ± 8.6 × 10-3 vs. 18.1 × 10-3 ± 16.9 × 10-3, p = 0.017) in CS than in non-CS. Intra- and inter-rater reproducibility of RLNU and LGZE were excellent (ICCs > 0.8), while inter-observer agreement of visual assessment was poor (kappa = 0.19). The accuracies of texture analysis were 69% with RLNU and 71% with LGZE, which were better than that of visual assessment.
CONCLUSION: Texture analysis of CTDE could differentiate CS from non-CS with high reproducibility.