Moon Jung Won, Bae Jang Pyo, Lee Ho Yun, Kim Namkug, Chung Man Pyo, Park Hye Yun, Chang Yongjun, Seo Joon Beom, Lee Kyung Soo
Department of Radiology (J.W.M.), Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
Department of Radiology (J.P.B., N.K., Y.C., J.B.S.), Asan Medical Center, University of Ulsan College of Medicine, #88, Olympic-Ro, 43-gil, Songpa-gu, Seoul, 138-736, Korea.
Eur Radiol. 2016 May;26(5):1368-77. doi: 10.1007/s00330-015-3946-2. Epub 2015 Aug 9.
To evaluate automated texture-based segmentation of dual-energy CT (DECT) images in diffuse interstitial lung disease (DILD) patients and prognostic stratification by overlapping morphologic and perfusion information of total lung.
Suspected DILD patients scheduled for surgical biopsy were prospectively included. Texture patterns included ground-glass opacity (GGO), reticulation and consolidation. Pattern- and perfusion-based CT measurements were assessed to extract quantitative parameters. Accuracy of texture-based segmentation was analysed. Correlations between CT measurements and pulmonary function test or 6-minute walk test (6MWT) were calculated. Parameters of idiopathic pulmonary fibrosis/usual interstitial pneumonia (IPF/UIP) and non-IPF/UIP were compared. Survival analysis was performed.
Overall accuracy was 90.47% for whole lung segmentation. Correlations between mean iodine values of total lung, 50-97.5th (%) attenuation and forced vital capacity or 6MWT were significant. Volume of GGO, reticulation and consolidation had significant correlation with DLco or SpO2 on 6MWT. Significant differences were noted between IPF/UIP and non-IPF/UIP in 6MWT distance, mean iodine value of total lung, 25-75th (%) attenuation and entropy. IPF/UIP diagnosis, GGO ratio, DILD extent, 25-75th (%) attenuation and SpO2 on 6MWT showed significant correlations with survival.
DECT combined with pattern analysis is useful for analysing DILD and predicting survival by provision of morphology and enhancement.
• Dual-energy CT (DECT) produces morphologic and parenchymal enhancement information. • Automated lung segmentation enables analysis of disease extent and severity. • This prospective study showed value of DECT in DILD patients. • Parameters on DECT enable characterization and survival prediction of DILD.
评估双能CT（DECT）图像基于纹理的自动分割在弥漫性间质性肺疾病（DILD）患者中的应用，并通过叠加全肺的形态学和灌注信息进行预后分层。
前瞻性纳入计划接受手术活检的疑似DILD患者。纹理模式包括磨玻璃影（GGO）、网状影和实变影。评估基于模式和灌注的CT测量值以提取定量参数。分析基于纹理的分割的准确性。计算CT测量值与肺功能测试或6分钟步行试验（6MWT）之间的相关性。比较特发性肺纤维化/普通间质性肺炎（IPF/UIP）和非IPF/UIP的参数。进行生存分析。
全肺分割的总体准确率为90.47%。全肺平均碘值、50 - 97.5%（%）衰减与用力肺活量或6MWT之间存在显著相关性。GGO、网状影和实变影的体积与6MWT时的一氧化碳弥散量（DLco）或血氧饱和度（SpO₂）有显著相关性。IPF/UIP和非IPF/UIP在6MWT距离、全肺平均碘值、25 - 75%（%）衰减和熵方面存在显著差异。IPF/UIP诊断、GGO比例、DILD范围、25 - 75%（%）衰减和6MWT时的SpO₂与生存率显示出显著相关性。
DECT结合模式分析有助于通过提供形态学和强化信息来分析DILD并预测生存。
• 双能CT（DECT）可产生形态学和实质强化信息。• 自动肺分割能够分析疾病范围和严重程度。• 这项前瞻性研究显示了DECT在DILD患者中的价值。• DECT上的参数能够对DILD进行特征描述和生存预测。
