Background Relatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis. We assessed the prevalence and risk factors for preclinical pulmonary fibrosis (PrePF) in first-degree relatives of patients with FIP and determined the utility of deep learning in detecting PrePF on CT.
Methods First-degree relatives of patients with FIP over 40 years of age who believed themselves to be unaffected by pulmonary fibrosis underwent CT scans of the chest. Images were visually reviewed, and a deep learning algorithm was used to quantify lung fibrosis. Genotyping for common idiopathic pulmonary fibrosis risk variants in MUC5B and TERT was performed.
Findings In 494 relatives of patients with FIP from 263 families of patients with FIP, the prevalence of PrePF on visual CT evaluation was 15.6% (95% CI 12.6 to 19.0). Compared with visual CT evaluation, deep learning quantitative CT analysis had 84% sensitivity (95% CI 0.72 to 0.89) and 86% sensitivity (95% CI 0.83 to 0.89) for discriminating subjects with visual PrePF diagnosis. Subjects with PrePF were older (65.9, SD 10.1 years) than subjects without fibrosis (55.8 SD 8.7 years), more likely to be male (49% vs 37%), more likely to have smoked (44% vs 27%) and more likely to have the MUC5B promoter variant rs35705950 (minor allele frequency 0.29 vs 0.21). MUC5B variant carriers had higher quantitative CT fibrosis scores (mean difference of 0.36%), a difference that remains significant when controlling for age and sex.
Interpretation PrePF is common in relatives of patients with FIP. Its prevalence increases with age and the presence of a common MUC5B promoter variant. Quantitative CT analysis can detect these imaging abnormalities.
- Idiopathic pulmonary fibrosis
- Interstitial Fibrosis
- Imaging/CT MRI etc
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Contributors SKM compiled and analysed visual CT reads, performed genotyping, statistical analyses, wrote first draft of manuscript and revised the manuscript in collaboration with the other authors. SH created and implemented the quantitative high-resolution CT algorithm described in this article; he also performed %HAA quantification on CT scans in this study and contributed to the first draft of the manuscript. JP led research coordination, data management and study subject recruitment at the University of Colorado. ADW performed nucleic acid extractions and sample management for this project. JW contributed to data management and study subject recruitment. IVY contributed to and advised on the overall study design, genetic analyses and gene expression analyses. TF oversaw statistical analyses presented in this article. KKB and MPS were integral to study subject recruitment. MIS contributed to the overall study design. DAL and JHC. performed radiological reviews for this study. CM, JAK, TSB and JEL led patient recruitment, study design, and data and sample management at Vanderbilt University. DAS led overall study design and subject recruitment, and contributed to each stage of data analyses and manuscript drafting. All authors contributed to manuscript revisions.
Funding NIH-NHLBI (UH2/3-HL123442, R01-HL097163, R21/R33-HL120770, P01-HL092870, K23-HL136785, K08-HL130595, F32HL123240), U.S. DOD (W81XWH-17-1-0597).
Competing interests DAS is the founder and chief scientific officer of Eleven P15, a company focused on the early diagnosis and treatment of pulmonary fibrosis. DAS has an awarded patent (US patent no: 8,673,565) for the treatment and diagnosis of fibrotic lung disease. DAL and SMH have a pending patent (application US20170330320A1) for image analysis; SMH reports a consulting agreement with Boehringer Ingelheim.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Anonymized data are available upon reasonable request.
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