Deep learning classification of radiologic pattern is associated with progression of interstitial lung abnormalities and with survival.

Abstract

Rationale: Interstitial lung abnormalities (ILA) are common, but not all progress, highlighting the need for objective computed tomography (CT)-based biomarkers for risk stratification.

Objective: To determine whether deep learning-based classification of usual interstitial pneumonia (UIP) is associated with fibrosis progression and survival in individuals with ILA.

Methods: Baseline and follow-up CT scans from participants without clinically diagnosed interstitial lung disease in two large observational cohorts (COPDGene and AGES-Reykjavík) were analyzed using data-driven textural analysis (DTA) to quantify fibrosis and a deep learning-based classifier (MIL-UIP) to estimate UIP likelihood. Associations of baseline MIL-UIP with DTA trajectory and survival were evaluated using linear mixed and multivariable Cox models, respectively.

Measurements and main results: Baseline MIL-UIP > 0.5 was associated with relative annual DTA increases of 13.41% (95% CI: 8.10%, 18.99%; p < 0.001) and 14.10% (95% CI: 5.06%, 23.91%; p = 0.002) in COPDGene and AGES-Reykjavík, respectively. Each 0.1-point increase in MIL-UIP was associated with relative rates of DTA change that were 1.10 percentage points higher (95% CI: 0.45, 1.74; p < 0.001) in COPDGene and 1.30 percentage points higher (95% CI: 0.17, 2.45; p = 0.025) in AGES-Reykjavík. MIL-UIP > 0.5 was associated with higher mortality in COPDGene (HR 1.61; 95% CI: 1.08, 2.38; p = 0.018) and AGES-Reykjavík (HR 1.62; 95% CI: 1.01, 2.61; p = 0.046). Baseline DTA and MIL-UIP were highly associated with visual assessments of ILA, UIP, and fibrosis progression.

Conclusion: Automated assessment of UIP-like CT features is associated with fibrosis progression and mortality in individuals with ILA, supporting its potential use for risk stratification and clinical trials enrichment.