Literature DB >> 34204184

Differentiation of Idiopathic Pulmonary Fibrosis from Connective Tissue Disease-Related Interstitial Lung Disease Using Quantitative Imaging.

Jonathan H Chung1, Ayodeji Adegunsoye2, Brenna Cannon3, Rekha Vij2, Justin M Oldham4, Christopher King5, Steven M Montner1, Prahasit Thirkateh6, Scott Barnett7, Ronald Karwoski8, Brian J Bartholmai9, Mary Strek2, Steven D Nathan7.   

Abstract

A usual interstitial pneumonia (UIP) imaging pattern can be seen in both idiopathic pulmonary fibrosis (IPF) and connective tissue disease-related interstitial lung disease (CTD-ILD). The purpose of this multicenter study was to assess whether quantitative imaging data differ between IPF and CTD-ILD in the setting of UIP. Patients evaluated at two medical centers with CTD-ILD or IPF and a UIP pattern on CT or pathology served as derivation and validation cohorts. Chest CT data were quantitatively analyzed including total volumes of honeycombing, reticulation, ground-glass opacity, normal lung, and vessel related structures (VRS). VRS was compared with forced vital capacity percent predicted (FVC%) and percent predicted diffusing capacity of the lungs for carbon monoxide (DLCO%). There were 296 subjects in total, with 40 CTD-ILD and 85 IPF subjects in the derivation cohort, and 62 CTD-ILD and 109 IPF subjects in the validation cohort. VRS was greater in IPF across the cohorts on univariate (p < 0.001) and multivariable (p < 0.001-0.047) analyses. VRS was inversely correlated with DLCO% in both cohorts on univariate (p < 0.001) and in the derivation cohort on multivariable analysis (p = 0.003) but not FVC%. Total volume of normal lung was associated with DLCO% (p < 0.001) and FVC% (p < 0.001-0.009) on multivariable analysis in both cohorts. VRS appears to have promise in differentiating CTD-ILD from IPF. The underlying pathophysiological relationship between VRS and ILD is complex and is likely not explained solely by lung fibrosis.

Entities:  

Keywords:  computer-assisted; connective tissue disease; idiopathic pulmonary fibrosis; image interpretation; multidetector computed tomography; usual interstitial pneumonia

Year:  2021        PMID: 34204184     DOI: 10.3390/jcm10122663

Source DB:  PubMed          Journal:  J Clin Med        ISSN: 2077-0383            Impact factor:   4.241


  2 in total

1.  Unsupervised machine learning identifies predictive progression markers of IPF.

Authors:  Jeanny Pan; Johannes Hofmanninger; Karl-Heinz Nenning; Florian Prayer; Sebastian Röhrich; Nicola Sverzellati; Venerino Poletti; Sara Tomassetti; Michael Weber; Helmut Prosch; Georg Langs
Journal:  Eur Radiol       Date:  2022-09-06       Impact factor: 7.034

2.  Computed Tomography Imaging in ILD: New Trends for the Clinician.

Authors:  Gregor S Zimmermann
Journal:  J Clin Med       Date:  2022-10-09       Impact factor: 4.964

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.