Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis.

Maldonado F, Moua T, Rajagopalan S, Karwoski RA, Raghunath S, Decker PA, Hartman TE, Bartholmai BJ, Robb RA, Ryu JH
Eur Respir J. 2014 43 (1): 204-12

PMID: 23563264 · DOI:10.1183/09031936.00071812

Accurate assessment of prognosis in idiopathic pulmonary fibrosis remains elusive due to significant individual radiological and physiological variability. We hypothesised that short-term radiological changes may be predictive of survival. We explored the use of CALIPER (Computer-Aided Lung Informatics for Pathology Evaluation and Rating), a novel software tool developed by the Biomedical Imaging Resource Laboratory at the Mayo Clinic Rochester (Rochester, MN, USA) for the analysis and quantification of parenchymal lung abnormalities on high-resolution computed tomography. We assessed baseline and follow-up (time-points 1 and 2, respectively) high-resolution computed tomography scans in 55 selected idiopathic pulmonary fibrosis patients and correlated CALIPER-quantified measurements with expert radiologists' assessments and clinical outcomes. Findings of interval change (mean 289 days) in volume of reticular densities (hazard ratio 1.91, p=0.006), total volume of interstitial abnormalities (hazard ratio 1.70, p=0.003) and per cent total interstitial abnormalities (hazard ratio 1.52, p=0.017) as quantified by CALIPER were predictive of survival after a median follow-up of 2.4 years. Radiologist interpretation of short-term global interstitial lung disease progression, but not specific radiological features, was also predictive of mortality. These data demonstrate the feasibility of quantifying interval short-term changes on high-resolution computed tomography and their possible use as independent predictors of survival in idiopathic pulmonary fibrosis.

MeSH Terms (13)

Aged Disease Progression Female Humans Idiopathic Pulmonary Fibrosis Image Processing, Computer-Assisted Lung Male Pattern Recognition, Automated Prognosis Proportional Hazards Models Spirometry Tomography, X-Ray Computed

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