Takx Richard A P, de Jong Pim A, Leiner Tim, Oudkerk Matthijs, de Koning Harry J, Mol Christian P, Viergever Max A, Išgum Ivana
Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands.
Center for Medical Imaging - North East, Netherlands, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
PLoS One. 2014 Mar 13;9(3):e91239. doi: 10.1371/journal.pone.0091239. eCollection 2014.
To determine the agreement and reliability of fully automated coronary artery calcium (CAC) scoring in a lung cancer screening population.
1793 low-dose chest CT scans were analyzed (non-contrast-enhanced, non-gated). To establish the reference standard for CAC, first automated calcium scoring was performed using a preliminary version of a method employing coronary calcium atlas and machine learning approach. Thereafter, each scan was inspected by one of four trained raters. When needed, the raters corrected initially automaticity-identified results. In addition, an independent observer subsequently inspected manually corrected results and discarded scans with gross segmentation errors. Subsequently, fully automatic coronary calcium scoring was performed. Agatston score, CAC volume and number of calcifications were computed. Agreement was determined by calculating proportion of agreement and examining Bland-Altman plots. Reliability was determined by calculating linearly weighted kappa (κ) for Agatston strata and intraclass correlation coefficient (ICC) for continuous values.
44 (2.5%) scans were excluded due to metal artifacts or gross segmentation errors. In the remaining 1749 scans, median Agatston score was 39.6 (P25-P75∶0-345.9), median volume score was 60.4 mm3 (P25-P75∶0-361.4) and median number of calcifications was 2 (P25-P75∶0-4) for the automated scores. The κ demonstrated very good reliability (0.85) for Agatston risk categories between the automated and reference scores. The Bland-Altman plots showed underestimation of calcium score values by automated quantification. Median difference was 2.5 (p25-p75∶0.0-53.2) for Agatston score, 7.6 (p25-p75∶0.0-94.4) for CAC volume and 1 (p25-p75∶0-5) for number of calcifications. The ICC was very good for Agatston score (0.90), very good for calcium volume (0.88) and good for number of calcifications (0.64).
Fully automated coronary calcium scoring in a lung cancer screening setting is feasible with acceptable reliability and agreement despite an underestimation of the amount of calcium when compared to reference scores.
确定在肺癌筛查人群中全自动冠状动脉钙化（CAC）评分的一致性和可靠性。
分析了1793例低剂量胸部CT扫描（非增强、非门控）。为建立CAC的参考标准，首先使用采用冠状动脉钙化图谱和机器学习方法的初步版本进行自动钙评分。此后，由四名经过培训的评估者之一对每次扫描进行检查。必要时，评估者对最初自动识别的结果进行校正。此外，一名独立观察者随后检查手动校正后的结果，并丢弃存在严重分割错误的扫描。随后，进行全自动冠状动脉钙评分。计算阿加斯顿评分、CAC体积和钙化灶数量。通过计算一致性比例和检查布兰德-奥特曼图来确定一致性。通过计算阿加斯顿分层的线性加权kappa（κ）和连续值的组内相关系数（ICC）来确定可靠性。
44例（2.5%）扫描因金属伪影或严重分割错误被排除。在其余1749例扫描中，自动评分的阿加斯顿评分中位数为39.6（第25-75百分位数：0-345.9），体积评分中位数为60.4mm³（第25-75百分位数：0-361.4），钙化灶数量中位数为2（第25-75百分位数：0-4）。κ显示自动评分和参考评分之间阿加斯顿风险类别的可靠性非常好（0.85）。布兰德-奥特曼图显示自动定量低估了钙评分值。阿加斯顿评分的中位数差异为2.5（第25-75百分位数：0.0-53.2），CAC体积的中位数差异为7.6（第25-75百分位数：0.0-94.4），钙化灶数量的中位数差异为1（第25-75百分位数：0-5）。ICC对阿加斯顿评分非常好（0.90），对钙体积非常好（0.88），对钙化灶数量良好（0.64）。
在肺癌筛查环境中，全自动冠状动脉钙评分是可行的，具有可接受的可靠性和一致性，尽管与参考评分相比钙含量被低估。
