Hsu Chien-Chin, Chen Yu-Wen, Hao Chi-Long, Chong Jun-Ted, Lee Chun-I, Tan Hau-Tong, Wu Ming-Sheng, Wu Jung-Chou
Department of Nuclear Medicine, Pingtung Christian Hospital, Pingtung, Taiwan.
Kaohsiung J Med Sci. 2008 Sep;24(9):445-52. doi: 10.1016/S1607-551X(09)70001-4.
The aim of this study was to assess the reproducibility and diagnostic performance for coronary artery disease (CAD) of an automated software package, 4D-MSPECT, and compare the results with a visual approach. We enrolled 60 patients without previously known CAD, who underwent dual-isotope rest Tl-201/stress Tc-99m sestamibi myocardial perfusion imaging and subsequent coronary angiography within 3 months. The automated summed stress score (A-SSS), summed rest score (A-SRS) and summed difference score (A-SDS) were obtained using a 17-segment five-point scale model with 4D-MSPECT. For intraobserver and interobserver variability assessment, automated scoring was done by a nuclear medicine physician twice and by a nuclear medicine technologist. The visual summed stress score (V-SSS), summed rest score (V-SRS), and summed difference score (V-SDS) were obtained by consensus of two nuclear medicine physicians. The intraobserver and interobserver agreements of automated segmental scores were excellent. The intraobserver and interobserver summed scores also correlated well. Agreements between visual and automated segmental scores were moderate (weighted kappa of 0.55 and 0.50 for stress and rest images, respectively). Correlations between automated and visual summed scores were high, with correlation coefficients of 0.89, 0.85 and 0.82 for SSS, SRS and SDS, respectively (all p < 0.001). The receiver operating characteristic area under the curve for diagnosis of CAD by V-SSS, V-SDS, A-SSS and A-SDS were 0.78 +/- 0.06, 0.87 +/- 0.05, 0.84 +/- 0.05 and 0.90 +/- 0.04, respectively. A-SDS had better diagnostic performance than A-SSS and V-SSS (p = 0.043 and p = 0.032, respectively), whereas there was no statistically significant difference between A-SDS and V-SDS (p = 0.56). Using V-SDS > or = 2 as a diagnostic threshold, the sensitivity, specificity, and accuracy for CAD were 83.7%, 76.5% and 81.7%, respectively. Using A-SDS > or = 3 as a diagnostic threshold, the sensitivity, specificity, and accuracy for CAD were 79.1%, 82.4% and 80.0%, respectively. In conclusion, the reproducibility of automated semiquantitative analysis with 4D-MSPECT was excellent. The diagnostic performance of automated semiquantitative analysis with 4D-MSPECT was comparable with the visual approach.
本研究旨在评估自动化软件包4D-MSPECT对冠状动脉疾病（CAD）的可重复性和诊断性能，并将结果与视觉评估方法进行比较。我们纳入了60例既往无CAD的患者，这些患者在3个月内接受了双同位素静息Tl-201/负荷Tc-99m sestamibi心肌灌注显像及随后的冠状动脉造影。使用4D-MSPECT的17节段五点量表模型获得自动负荷积分（A-SSS）、静息积分（A-SRS）和差值积分（A-SDS）。为评估观察者内和观察者间的变异性，由一名核医学医师和一名核医学技术人员对自动评分进行两次操作。视觉负荷积分（V-SSS）、静息积分（V-SRS）和差值积分（V-SDS）由两名核医学医师共同确定。自动节段评分的观察者内和观察者间一致性良好。观察者内和观察者间的总积分也具有良好的相关性。视觉和自动节段评分之间的一致性为中等（负荷和静息图像的加权kappa分别为0.55和0.50）。自动和视觉总积分之间的相关性较高，SSS、SRS和SDS的相关系数分别为0.89、0.85和0.82（均p<0.001）。V-SSS、V-SDS、A-SSS和A-SDS诊断CAD的受试者工作特征曲线下面积分别为0.78±0.06、0.87±0.05、0.84±0.05和0.90±0.04。A-SDS的诊断性能优于A-SSS和V-SSS（p分别为0.043和0.032），而A-SDS和V-SDS之间无统计学显著差异（p=0.56）。以V-SDS≥2为诊断阈值，CAD的敏感性、特异性和准确性分别为83.7%、76.5%和81.7%。以A-SDS≥3为诊断阈值，CAD的敏感性、特异性和准确性分别为79.1%、82.4%和80.0%。总之，4D-MSPECT自动半定量分析的可重复性良好。4D-MSPECT自动半定量分析的诊断性能与视觉评估方法相当。
