Jacob, J;
Bartholmai, B;
Rajagopalan, S;
van Moorsel, C;
van Es, H;
van Beek, F;
Struik, M;
... Wells, A; + view all
(2018)
Predicting outcome in idiopathic pulmonary fibrosis using automated CT analysis.
American Journal of Respiratory and Critical Care Medicine
, 198
(6)
pp. 767-776.
10.1164/rccm.201711-2174OC.
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Abstract
Aims: To determine whether computer-derived computed tomography measures, specifically measures of pulmonary vessel-related structures, can better predict functional decline and survival in idiopathic pulmonary fibrosis (IPF) and reduce requisite sample sizes in drug trial populations. Methods: IPF patients undergoing volumetric non-contrast CT imaging at the Royal Brompton Hospital, London and St Antonius Hospital, Utrecht, were examined to identify pulmonary functional measures (including forced vital capacity), and visual and computer-derived (CALIPER) CT features predictive of mortality and forced vital capacity decline. The discovery cohort constituted 247 consecutive patients with validation of results in a separate cohort of 284 patients all fulfilling drug trial entry criteria. Results: In discovery and validation cohorts, CALIPER-derived features, particularly vessel-related structure (VRS) scores were amongst the strongest predictors of survival and forced vital capacity decline. CALIPER results were accentuated in patients with less extensive disease, outperforming pulmonary function measures. When used as a cohort enrichment tool, a CALIPER VRS score >4.4% of the lung was able to reduce the requisite sample size of an IPF drug trial by 26%. Conclusions: Our study has validated a new quantitative CT measure in IPF patients fulfilling drug trial entry criteria, the VRS scores, that outperformed current gold-standard measures of outcome. When used for cohort enrichment in an IPF drug-trial setting, VRS threshold scores can reduce a required IPF drug trial population size by 25%, thereby limiting prohibitive trial costs. Importantly VRS scores identify patients in whom antifibrotic medication prolongs life and reduces forced vital capacity decline. Aims: To determine whether computer-derived computed tomography measures, specifically measures of pulmonary vessel-related structures, can better predict functional decline and survival in idiopathic pulmonary fibrosis (IPF) and reduce requisite sample sizes in drug trial populations. Methods: IPF patients undergoing volumetric non-contrast CT imaging at the Royal Brompton Hospital, London and St Antonius Hospital, Utrecht, were examined to identify pulmonary functional measures (including forced vital capacity), and visual and computer-derived (CALIPER) CT features predictive of mortality and forced vital capacity decline. The discovery cohort constituted 247 consecutive patients with validation of results in a separate cohort of 284 patients all fulfilling drug trial entry criteria. Results: In discovery and validation cohorts, CALIPER-derived features, particularly vessel-related structure (VRS) scores were amongst the strongest predictors of survival and forced vital capacity decline. CALIPER results were accentuated in patients with less extensive disease, outperforming pulmonary function measures. When used as a cohort enrichment tool, a CALIPER VRS score >4.4% of the lung was able to reduce the requisite sample size of an IPF drug trial by 26%. Conclusions: Our study has validated a new quantitative CT measure in IPF patients fulfilling drug trial entry criteria, the VRS scores, that outperformed current gold-standard measures of outcome. When used for cohort enrichment in an IPF drug-trial setting, VRS threshold scores can reduce a required IPF drug trial population size by 25%, thereby limiting prohibitive trial costs. Importantly VRS scores identify patients in whom antifibrotic medication prolongs life and reduces forced vital capacity decline. Some of the results of these studies have been previously reported in the form of an abstract (Jacob J, Bartholmai B, Altmann A, et al. Predicting time to decline in FVC using baseline visual and computer-based CT analysis and baseline functional indices. Clin Radiol; 72: S24.
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