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INTRODUCTION: There is currently no readily accessible measure to specifically quantify the amount of fibrosis in idiopathic pulmonary fibrosis (IPF). Such a measure could isolate contribution of fibrosis from other comorbidities to lung function abnormality and deterioration of disease, and potentially help determine if there has been response to antifibrotic treatment. METHODS: In a pilot study of 39 IPF patients, we used a CT-based visual scoring method to examine the correlation between the sum of all fibrotic features (all traction bronchiectasis, ground glass with traction bronchiectasis, honeycombing and reticulation; referred to as Total Fibrosis Score, TFS) or the individual fibrotic features, with lung function, Composite Physiologic Index (CPI) and time to death in the 5 years following CT measurement. RESULTS: TFS measurements were highly reproducible (r=0.982; p<0.001) and correlated significantly with TLCO, FVC and CPI. Traction bronchiectasis score was superior to others in its correlation to lung function and CPI, and as good as TFS. TFS and traction bronchiectasis score were also the best correlates (individually) to time to death (r=0.60 for both, and p=0.002 and p=0.004, respectively). CONCLUSION: We suggest that TFS and our 6-slices method of quantifying traction bronchiectasis on CT scans could be readily accessible and simple methods of quantifying lung fibrosis in IPF. These scores could assist in determining if clinical deterioration is due to worsening fibrosis, for correlation of research findings to amount of lung fibrosis, and to stratify patients for established drug treatment and clinical trials. Our findings also provide a basis for larger studies to validate these findings and determine if the scores could measure change in fibrosis.

Original publication




Journal article


BMJ Open Respir Res

Publication Date





imaging/CT MRI etc, Aged, Aged, 80 and over, Disease Progression, Female, Fibrosis, Humans, Idiopathic Pulmonary Fibrosis, Lung, Male, Middle Aged, Pilot Projects, Regression Analysis, Severity of Illness Index, Time Factors, Tomography, X-Ray Computed