Ley-Zaporozhan Julia, Giannakis Athanasios, Norajitra Tobias, Weinheimer Oliver, Kehler Lars, Dinkel Julien, Ganter Claudia, Ley Sebastian, Van Lunteren Csilla, Eichinger Monika, Heussel Gudula, Kauczor Hans-Ulrich, Maier-Hein Klaus H, Kreuter Michael, Heussel Claus Peter
Department Radiology, University Hospital, LMU Munich, Munich, Germany.
Comprehensive Pneumology Center (CPC), Member of the German Center of Lung Research (DZL), Munich, Germany.
Respiration. 2021;100(7):580-587. doi: 10.1159/000515182. Epub 2021 Apr 15.
Evaluation of software tools for segmentation, quantification, and characterization of fibrotic pulmonary parenchyma changes will strengthen the role of CT as biomarkers of disease extent, evolution, and response to therapy in idiopathic pulmonary fibrosis (IPF) patients.
418 nonenhanced thin-section MDCTs of 127 IPF patients and 78 MDCTs of 78 healthy individuals were analyzed through 3 fully automated, completely different software tools: YACTA, LUFIT, and IMBIO. The agreement between YACTA and LUFIT on segmented lung volume and 80th (reflecting fibrosis) and 40th (reflecting ground-glass opacity) percentile of the lung density histogram was analyzed using Bland-Altman plots. The fibrosis and ground-glass opacity segmented by IMBIO (lung texture analysis software tool) were included in specific regression analyses.
In the IPF-group, LUFIT outperformed YACTA by segmenting more lung volume (mean difference 242 mL, 95% limits of agreement -54 to 539 mL), as well as quantifying higher 80th (76 HU, -6 to 158 HU) and 40th percentiles (9 HU, -73 to 90 HU). No relevant differences were revealed in the control group. The 80th/40th percentile as quantified by LUFIT correlated positively with the percentage of fibrosis/ground-glass opacity calculated by IMBIO (r = 0.78/r = 0.92).
In terms of segmentation of pulmonary fibrosis, LUFIT as a shape model-based segmentation software tool is superior to the threshold-based YACTA, tool, since the density of (severe) fibrosis is similar to that of the surrounding soft tissues. Therefore, shape modeling as used in LUFIT may serve as a valid tool in the quantification of IPF, since this mainly affects the subpleural space.
评估用于肺实质纤维化改变的分割、量化和特征分析的软件工具，将强化CT作为特发性肺纤维化（IPF）患者疾病范围、进展及对治疗反应的生物标志物的作用。
通过3种完全不同的全自动软件工具YACTA、LUFIT和IMBIO，分析了127例IPF患者的418幅非增强薄层MDCT图像以及78例健康个体的78幅MDCT图像。使用Bland-Altman图分析YACTA和LUFIT在分割肺容积以及肺密度直方图第80百分位数（反映纤维化）和第40百分位数（反映磨玻璃影）方面的一致性。将IMBIO（肺纹理分析软件工具）分割的纤维化和磨玻璃影纳入特定回归分析。
在IPF组中，LUFIT在分割更多肺容积（平均差异242 mL，95%一致性界限-54至539 mL）以及量化更高的第80百分位数（76 HU，-6至158 HU）和第40百分位数（9 HU，-73至9HU）方面优于YACTA。在对照组中未发现相关差异。LUFIT量化的第80/40百分位数与IMBIO计算的纤维化/磨玻璃影百分比呈正相关（r = 0.78/r = 0.92）。
在肺纤维化分割方面，作为基于形状模型的分割软件工具，LUFIT优于基于阈值的YACTA工具，因为（重度）纤维化的密度与周围软组织相似。因此，LUFIT中使用的形状建模可作为IPF量化的有效工具，因为这主要影响胸膜下间隙。
