The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis
Abstract
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.
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Self-citationInterpretative strategies for lung function tests
Pellegrino, R., Viegi, G., Brusasco, V., Crapo, R. O., Burgos, F., Casaburi, R., Coates, A., van der Grinten, C. P. M., Gustafsson, P., Hankinson, J., et al. (2005) · European Respiratory Journal · doi:10.1183/09031936.05.00035205 · Cited by 5,945
Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images
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Changes in Clinical and Physiologic Variables Predict Survival in Idiopathic Pulmonary Fibrosis
Collard, H. R., King, T. E., Bartelson, B. B., Vourlekis, J. S., Schwarz, M. I., & Brown, K. K. (2003) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200211-1311oc · Cited by 764
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Idiopathic Interstitial Pneumonia: What Is the Effect of a Multidisciplinary Approach to Diagnosis?
Flaherty, K. R., King, T. E., Raghu, G., Lynch, J. P., Colby, T. V., Travis, W. D., Gross, B. H., Kazerooni, E. A., Toews, G. B., Long, Q., et al. (2004) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200402-147oc · Cited by 673
Fibrotic Idiopathic Interstitial Pneumonia: The Prognostic Value of Longitudinal Functional Trends
Latsi, P. I., du Bois, R. M., Nicholson, A. G., Colby, T. V., Bisirtzoglou, D., Nikolakopoulou, A., Veeraraghavan, S., Hansell, D. M., & Wells, A. U. (2003) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200210-1245oc · Cited by 583
Interstitial Lung Disease: A Quantitative Study Using the Adaptive Multiple Feature Method
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Prognostic Value of Desaturation during a 6-Minute Walk Test in Idiopathic Interstitial Pneumonia
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Colombi, D., Bodini, F. C., Petrini, M., Maffi, G., Morelli, N., Milanese, G., Silva, M., Sverzellati, N., & Michieletti, E. (2020) · Radiology · doi:10.1148/radiol.2020201433 · Cited by 456
Walsh, S. L. F., Calandriello, L., Silva, M., & Sverzellati, N. (2018) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(18)30286-8 · Cited by 390
Jacob, J., Bartholmai, B. J., Rajagopalan, S., Kokosi, M., Nair, A., Karwoski, R., Walsh, S. L. F., Wells, A. U., & Hansell, D. M. (2017) · European Respiratory Journal · doi:10.1183/13993003.01011-2016 · Cited by 309
Self-citationNatural history of idiopathic pulmonary fibrosis
Kim, H. J., Perlman, D., & Tomic, R. (2015) · Respiratory Medicine · doi:10.1016/j.rmed.2015.02.002 · Cited by 191
Evaluating disease severity in idiopathic pulmonary fibrosis
Robbie, H., Daccord, C., Chua, F., & Devaraj, A. (2017) · European Respiratory Review · doi:10.1183/16000617.0051-2017 · Cited by 93
Metadata
- Authors
- Maldonado, F., Moua, T., Rajagopalan, S., Karwoski, R. A., Raghunath, S., Decker, P. A., Hartman, T. E., Bartholmai, B. J., Robb, R. A., & Ryu, J. H.
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2013-04-05
- Volume
- 43
- Issue
- 1
- Pages
- 204-212
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- OpenAlex
- W2155777150
- Cited by
- 264
- References
- 25
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- closed
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- Maldonado, F., Moua, T., Rajagopalan, S., Karwoski, R. A., Raghunath, S., Decker, P. A., Hartman, T. E., Bartholmai, B. J., Robb, R. A., & Ryu, J. H. (2014). Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis. European Respiratory Journal. https://doi.org/10.1183/09031936.00071812
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