{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Humphries SM"],"funding":["Lung Foundation Australia","Boehringer Ingelheim","National Institute for Health Research (NIHR)","National Health and Medical Research Council"],"pagination":["1045-1053"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9796832"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(12)"],"pubmed_abstract":["<h4>Background and objective</h4>Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data-driven texture analysis (DTA) in a large cohort of well-characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry.<h4>Methods</h4>This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adj"],"journal":["Respirology (Carlton, Vic.)"],"pubmed_title":["Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis."],"pmcid":["PMC9796832"],"funding_grant_id":["CS-2018-18-ST2-004"],"pubmed_authors":["Wells AU","Reynolds PN","Corte TJ","Walters EH","Keir GJ","Mackintosh JA","Hopkins PMA","Calandriello L","Glaspole I","Goh N","Moodley Y","Grainge C","Lynch DA","Silva M","Chapman S","Jo HE","Humphries SM","Ellis S","Walsh SLF","Baraghoshi D"],"additional_accession":[]},"is_claimable":false,"name":"Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis.","description":"<h4>Background and objective</h4>Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data-driven texture analysis (DTA) in a large cohort of well-characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry.<h4>Methods</h4>This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adj","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2026-05-29T20:16:20.259Z","creation":"2025-04-04T10:58:50.633Z"},"accession":"S-EPMC9796832","cross_references":{"pubmed":["35875881"],"doi":["10.1111/resp.14333"]}}