• Samuel Y Ash, Rola Harmouche, Diego Lassala Lopez Vallejo, Julian A Villalba, Kris Ostridge, River Gunville, Carolyn E Come, Onieva Onieva Jorge J Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, 1249 Boylston St, Boston, MA, 02115, USA., James C Ross, Gary M Hunninghake, Souheil Y El-Chemaly, Tracy J Doyle, Pietro Nardelli, Gonzalo V Sanchez-Ferrero, Hilary J Goldberg, Ivan O Rosas, San Jose Estepar Raul R Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, 1249 Boylston St, Boston, MA, 02115, USA., and George R Washko.
• Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, 75 Francis St., PBB, CA-3, Boston, MA, 02115, USA. syash@partners.org.
• Resp Res. 2017 Mar 7; 18 (1): 45.

BackgroundPrior studies of clinical prognostication in idiopathic pulmonary fibrosis (IPF) using computed tomography (CT) have often used subjective analyses or have evaluated quantitative measures in isolation. This study examined associations between both densitometric and local histogram based quantitative CT measurements with pulmonary function test (PFT) parameters and mortality. In addition, this study sought to compare risk prediction scores that incorporate quantitative CT measures with previously described systems.MethodsForty six patients with biopsy proven IPF were identified from a registry of patients with interstitial lung disease at Brigham and Women's Hospital in Boston, MA. CT scans for each subject were visually scored using a previously published method. After a semi-automated method was used to segment the lungs from the surrounding tissue, densitometric measurements including the percent high attenuating area, mean lung density, skewness and kurtosis were made for the entirety of each patient's lungs. A separate, automated tool was used to detect and quantify the percent of lung occupied by interstitial lung features. These analyses were used to create clinical and quantitative CT based risk prediction scores, and the performance of these was compared to the performance of clinical and visual analysis based methods.ResultsAll of the densitometric measures were correlated with forced vital capacity and diffusing capacity, as were the total amount of interstitial change and the percentage of interstitial change that was honeycombing measured using the local histogram method. Higher percent high attenuating area, higher mean lung density, lower skewness, lower kurtosis and a higher percentage of honeycombing were associated with worse transplant free survival. The quantitative CT based risk prediction scores performed similarly to the clinical and visual analysis based methods.ConclusionsBoth densitometric and feature based quantitative CT measures correlate with pulmonary function test measures and are associated with transplant free survival. These objective measures may be useful for identifying high risk patients and monitoring disease progression. Further work will be needed to validate these measures and the quantitative imaging based risk prediction scores in other cohorts.

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