Automated quantitative analysis can support the identification of progressive pulmonary fibrosis in fibrotic interstitial lung disease

Abstract

Abstract Background Identifying progression in fibrotic interstitial lung disease (f-ILD) is essential for treatment decisions and prognostication. Quantitative CT (qCT) may improve detection of progressive pulmonary fibrosis (PPF). We evaluated whether routine qCT enhances identification of disease progression. Methods We conducted a retrospective observational study of patients presenting to an ILD service over 2 years. Patients with f-ILD who underwent two CT scans within 24 months prior to ILD multidisciplinary team (ILD-MDT) review were included. CTs were initially reported by a mixed pool of radiologists and subsequently reviewed by an expert thoracic radiologist at ILD-MDT. The contextflow ADVANCE Chest CT was used to quantify lung abnormalities. The cohort was divided 70:30 into derivation and validation sets, stratified by ILD-MDT-defined progression. In the derivation cohort, optimal qCT thresholds were defined using Youden’s index and applied to the validation cohort to assess detection of previously unreported progression. Results 127/502 patients met inclusion criteria and were stratified into derivation (n=89) and validation (n=38) cohorts. In the derivation cohort, a 0.39% increase in combined honeycombing and reticulation yielded an AUC of 0.64 for identifying progression. In the validation cohort, this threshold achieved sensitivity and specificity of 0.71 for PPF detection. Although 27/38 (71.1%) patients were initially reported as stable or without comment on progression, 12/27 (44.4%) were later classified as having radiological progression at ILD-MDT. Of these discordant cases, qCT correctly identified progression in 10/12 (83.3%). Discussion This study demonstrates that qCT may assist the non-specialist radiologist in identifying fibrotic disease progression enabling earlier access to appropriate treatment.