Perfusion- and pattern-based quantitative CT indexes using contrast-enhanced dual-energy computed tomography in diffuse interstitial lung disease: relationships with physiologic impairment and prediction of prognosis

ObjectivesTo 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.MethodsSuspected 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.ResultsOverall 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.ConclusionDECT combined with pattern analysis is useful for analysing DILD and predicting survival by provision of morphology and enhancement.Key Points• 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.

[1]  F. Martinez,et al.  Prognostic value of desaturation during a 6-minute walk test in idiopathic interstitial pneumonia. , 2003, American journal of respiratory and critical care medicine.

[2]  Arnold Simanowitz,et al.  international consensus statement , 2000 .

[3]  N. Müller,et al.  Disease progression in usual interstitial pneumonia compared with desquamative interstitial pneumonia. Assessment with serial CT. , 1996, Chest.

[4]  Kevin Flaherty,et al.  Pulmonary function testing in idiopathic interstitial pneumonias. , 2006, Proceedings of the American Thoracic Society.

[5]  Mathias Prokop,et al.  High-resolution CT of diffuse interstitial lung disease: key findings in common disorders , 2001, European Radiology.

[6]  O. Hilberg,et al.  Idiopathic Pulmonary Fibrosis - Diagnosis and Treatment , 2014 .

[7]  Joon Beom Seo,et al.  A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: comparison to a Bayesian classifier. , 2013, Medical physics.

[8]  David A. Lynch,et al.  Idiopathic pulmonary fibrosis: Diagnosis and treatment: International Consensus Statement , 2000 .

[9]  Joon Beom Seo,et al.  Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT. , 2014, Medical physics.

[10]  D. Hansell,et al.  Diffuse interstitial lung disease: overlaps and uncertainties , 2010, European Radiology.

[11]  A. Nicholson,et al.  The prognostic significance of the histologic pattern of interstitial pneumonia in patients presenting with the clinical entity of cryptogenic fibrosing alveolitis. , 2000, American journal of respiratory and critical care medicine.

[12]  Bram van Ginneken,et al.  Toward automated segmentation of the pathological lung in CT , 2005, IEEE Transactions on Medical Imaging.

[13]  Gary K Grunwald,et al.  Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment. , 2003, Radiology.

[14]  Joon Beom Seo,et al.  Development of an Automatic Classification System for Differentiation of Obstructive Lung Disease using HRCT , 2008, Journal of Digital Imaging.

[15]  Gary K Grunwald,et al.  Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. , 2008, Radiology.

[16]  J. Remy,et al.  Thoracic applications of dual energy. , 2010, Radiologic clinics of North America.

[17]  T. Hartman,et al.  Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis , 2013, European Respiratory Journal.

[18]  S. Mirsadraee,et al.  Dual-energy CT angiography for assessment of regional pulmonary perfusion in patients with chronic thromboembolic pulmonary hypertension: initial experience. , 2011, AJR. American journal of roentgenology.

[19]  Geoffrey D. Rubin,et al.  Adaptive border marching algorithm: Automatic lung segmentation on chest CT images , 2008, Comput. Medical Imaging Graph..

[20]  W. Travis,et al.  Idiopathic nonspecific interstitial pneumonia: prognostic significance of cellular and fibrosing patterns: survival comparison with usual interstitial pneumonia and desquamative interstitial pneumonia. , 2000, The American journal of surgical pathology.

[21]  Balaji Ganeshan,et al.  Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.

[22]  O. Simonetti,et al.  The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. , 2000, The New England journal of medicine.