Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 138-736, Korea.
Korean J Radiol. 2011 May-Jun;12(3):297-307. doi: 10.3348/kjr.2011.12.3.297. Epub 2011 Apr 25.
To evaluate the usefulness of an automated system for quantification and discrimination of usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP).
An automated system to quantify six regional high-resolution CT (HRCT) patterns: normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS, was developed using texture and shape features. Fifty-four patients with pathologically proven UIP (n = 26) and pathologically proven NSIP (n = 28) were included as part of this study. Inter-observer agreement in measuring the extent of each HRCT pattern between the system and two thoracic radiologists were assessed in 26 randomly selected subsets using an interclass correlation coefficient (ICC). A linear regression analysis was used to assess the contribution of each disease pattern to the pulmonary function test parameters. The discriminating capacity of the system between UIP and NSIP was evaluated using a binomial logistic regression.
The overall ICC showed acceptable agreement among the system and the two radiologists (r = 0.895 for the abnormal lung volume fraction, 0.706 for the fibrosis fraction, 0.895 for NL, 0.625 for GGO, 0.626 for RO, 0.893 for HC, 0.800 for EMPH, and 0.430 for CONS). The volumes of NL, GGO, RO, and EMPH contribute to forced expiratory volume during one second (FEV₁) (r = 0.72, β values, 0.84, 0.34, 0.34 and 0.24, respectively) and forced vital capacity (FVC) (r = 0.76, β values, 0.82, 0.28, 0.21 and 0.34, respectively). For diffusing capacity (DL(co)), the volumes of NL and HC were independent contributors in opposite directions (r = 0.65, β values, 0.64, -0.21, respectively). The automated system can help discriminate between UIP and NSIP with an accuracy of 82%.
The automated quantification system of regional HRCT patterns can be useful in the assessment of disease severity and may provide reliable agreement with the radiologists' results. In addition, this system may be useful in differentiating between UIP and NSIP.
评估一种用于量化和区分普通间质性肺炎(UIP)和非特异性间质性肺炎(NSIP)的自动化系统的实用性。
使用纹理和形状特征开发了一种用于量化六个区域高分辨率 CT(HRCT)模式的自动化系统:正常(NL);磨玻璃影(GGO);网状影(RO);蜂窝肺(HC);肺气肿(EMPH);和实变影(CONS)。54 名经病理证实的 UIP(n=26)和经病理证实的 NSIP(n=28)患者被纳入本研究。在 26 个随机选择的子集内,使用组内相关系数(ICC)评估系统与两名胸部放射科医生之间测量每个 HRCT 模式程度的观察者间一致性。使用二项逻辑回归分析评估每个疾病模式对肺功能测试参数的贡献。使用二项逻辑回归评估系统在 UIP 和 NSIP 之间的区分能力。
系统与两名放射科医生之间的总体 ICC 显示出可接受的一致性(异常肺容积分数的 r=0.895,纤维化分数的 r=0.706,NL 的 r=0.895,GGO 的 r=0.625,RO 的 r=0.626,HC 的 r=0.893,EMPH 的 r=0.800,和 CONS 的 r=0.430)。NL、GGO、RO 和 EMPH 的体积与第一秒用力呼气量(FEV₁)(r=0.72,β 值,0.84、0.34、0.34 和 0.24)和用力肺活量(FVC)(r=0.76,β 值,0.82、0.28、0.21 和 0.34)相关。对于弥散量(DL(co)),NL 和 HC 的体积是相互独立的贡献者(r=0.65,β 值,0.64、-0.21)。自动化系统可以帮助以 82%的准确率区分 UIP 和 NSIP。
区域 HRCT 模式的自动量化系统在评估疾病严重程度方面可能很有用,并可能与放射科医生的结果提供可靠的一致性。此外,该系统可能有助于区分 UIP 和 NSIP。