Quantitative CT in the real-world evaluation of progressive pulmonary fibrosis; the REVISE PPF multicentre observational study

Rationale and Objective

We assessed whether an FDA-cleared quantitative CT (qCT) tool (Brainomix, e-Lung) identifies progressive pulmonary fibrosis (PPF) earlier than routine clinical diagnosis and evaluated the prognostic value of baseline and serial imaging biomarkers.

Methods

Multicentre retrospective study involving University of Alabama (UAB), Weill Cornell Medicine (WCM) and University of Chicago (UChicago); n = 509. HRCTs were analysed with e-Lung to derive Weighted Reticulovascular Score (WRVS, %), Total Disease Extent (TDE, %), and e-Lung Volume (mL). Prespecified progression thresholds were an absolute WRVS increase of ≥ 3 percentage points, an absolute TDE increase of ≥ 1.5 percentage points, and a relative e-Lung Volume decline of ≥ 10%, over less than 2 years. We compared the earliest qCT progression date with recorded clinical PPF diagnoses, assessed clinically stable controls, and modelled associations with future PPF and mortality using adjusted time-varying Cox models.

Results

Among 200 patients with PPF and serial HRCTs meeting the prespecified timing criterion, qCT identified progression earlier in 78% (UAB), 83% (WCM), and 47% (UChicago), with mean lead times of 434, 847, and 220 days, respectively. Among 187 clinically stable patients meeting the same serial-imaging criterion, progression thresholds were met in 67%, 56%, and 42%, respectively. High-risk baseline WRVS (≥15%) predicted future PPF (OR 4.09, 2.48, and 2.30) and mortality (HR 2.03 pooled UAB/WCM; 2.75 UChicago). Per 1-percentage-point increase, adjusted mortality HRs were 1.15 and 1.19 for WRVS and 1.05 and 1.04 for TDE in pooled UAB/WCM and UChicago, respectively. Threshold crossings were also associated with mortality (WRVS: HR 6.50 pooled UAB/WCM, 4.05 UChicago; TDE: 7.60 and 3.26; e-Lung Volume: 8.20 and 2.59).

Conclusion

qCT detects radiological progression months to years before clinical PPF diagnoses. Integrating e-Lung into ILD pathways could accelerate treatment decisions and personalize follow-up.

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