Revel Marie-Pierre, Faivre Jean-Baptiste, Remy-Jardin Martine, Deken Valérie, Duhamel Alain, Marquette Charles-Hugo, Tacelli Nunzia, Bakai Anne-Marie, Remy Jacques
Department of Radiology, Calmette Hospital, University of Lille 2, Boulevard Jules Leclerc, 59037, Lille, France.
Eur Radiol. 2008 Dec;18(12):2723-30. doi: 10.1007/s00330-008-1065-z. Epub 2008 Jul 5.
Automated lobar quantification of emphysema has not yet been evaluated. Unenhanced 64-slice MDCT was performed in 47 patients evaluated before bronchoscopic lung-volume reduction. CT images reconstructed with a standard (B20) and high-frequency (B50) kernel were analyzed using a dedicated prototype software (MevisPULMO) allowing lobar quantification of emphysema extent. Lobar quantification was obtained following (a) a fully automatic delineation of the lobar limits by the software and (b) a semiautomatic delineation with manual correction of the lobar limits when necessary and was compared with the visual scoring of emphysema severity per lobe. No statistically significant difference existed between automated and semiautomated lobar quantification (p > 0.05 in the five lobes), with differences ranging from 0.4 to 3.9%. The agreement between the two methods (intraclass correlation coefficient, ICC) was excellent for left upper lobe (ICC = 0.94), left lower lobe (ICC = 0.98), and right lower lobe (ICC = 0.80). The agreement was good for right upper lobe (ICC = 0.68) and moderate for middle lobe (IC = 0.53). The Bland and Altman plots confirmed these results. A good agreement was observed between the software and visually assessed lobar predominance of emphysema (kappa 0.78; 95% CI 0.64-0.92). Automated and semiautomated lobar quantifications of emphysema are concordant and show good agreement with visual scoring.
尚未对肺气肿的自动肺叶定量进行评估。对47例在接受支气管镜肺减容术前接受评估的患者进行了非增强64层MDCT检查。使用专用的原型软件(MevisPULMO)分析了用标准(B20)和高频(B50)内核重建的CT图像,该软件可对肺气肿范围进行肺叶定量。肺叶定量是在以下情况下获得的:(a)软件对肺叶界限进行全自动描绘;(b)必要时进行半自动描绘并手动校正肺叶界限,并与每个肺叶的肺气肿严重程度视觉评分进行比较。自动和半自动肺叶定量之间不存在统计学上的显著差异(五个肺叶中p>0.05),差异范围为0.4%至3.9%。两种方法之间的一致性(组内相关系数,ICC)在左上叶(ICC = 0.94)、左下叶(ICC = 0.98)和右下叶(ICC = 0.80)方面非常好。右上叶的一致性良好(ICC = 0.68),中叶的一致性中等(IC = 0.53)。Bland和Altman图证实了这些结果。在软件与视觉评估的肺气肿肺叶优势之间观察到良好的一致性(kappa 0.78;95%CI 0.64 - 0.92)。肺气肿的自动和半自动肺叶定量结果一致,并且与视觉评分显示出良好的一致性。