B23-07 Quantitative Computed Tomography Analysis Distinguishes Acute Exacerbation From Progressive Fibrosing Phenotypes in Interstitial Lung Disease

Abstract Background Acute clinical deterioration in interstitial lung disease (ILD) may result from true inflammatory exacerbations or progressive fibrosis without meeting exacerbation criteria. These phenotypes require different therapeutic approaches (anti-inflammatory versus anti-fibrotic), yet reliable differentiation remains challenging. Quantitative CT analysis using IMBIO Lung Texture Analysis enables objective measurement of parenchymal changes, potentially distinguishing these clinically relevant phenotypes. Methods We analyzed 62 consecutive ILD patients presenting with acute respiratory deterioration: 38 meeting AEx-ILD criteria and 24 without exacerbation. All underwent quantitative CT analysis using IMBIO software at baseline (n = 49), acute presentation (n = 62), and follow-up (n = 37). We compared temporal changes in ground-glass opacities (GGO), reticular pattern, honeycombing, and pulmonary volumes between groups. Multivariable logistic regression models were developed to identify diagnostic parameters. Results Both groups showed significant GGO increases (exacerbation: +21.5%, p 0.001; non-exacerbation: +19.1%, p = 0.003), demonstrating that GGO magnitude alone does not discriminate between phenotypes (p = 0.825). However, the groups exhibited distinct patterns in fibrotic parameters. Patients with AEx-ILD demonstrated stable reticular pattern (-0.8%, p = 0.96) and moderate volume loss (-403 mL, p 0.001). In contrast, the non-exacerbation group showed significant reticular pattern progression (+5.5%, p = 0.020) and greater volume loss (-588 mL, p 0.001), although individual comparisons between groups did not reach statistical significance (p = 0.064 and p = 0.203, respectively).A multivariable classification model integrating changes in reticular pattern, honeycombing, total lung capacity, and pulmonary vascular volume achieved strong diagnostic performance (AUC=0.787, sensitivity 72%, specificity 76%), demonstrating that combined quantitative parameters successfully distinguish phenotypes when individual parameters cannot. The characteristic pattern of massive GGO increase with stable fibrotic parameters identifies true exacerbations, while progressive reticular changes with volume loss characterize fibrosing phenotypes.Notably, 6 patients in the non-exacerbation group (25%) showed significant radiological progression despite not meeting clinical exacerbation criteria, representing “silent progressors” who may benefit from treatment intensification. Conclusions Individual quantitative CT parameters show overlapping changes between acute exacerbations and progressive fibrosing phenotypes. However, multivariable analysis combining multiple IMBIO parameters provides robust phenotype discrimination (AUC=0.787). The pattern of inflammatory change (massive GGO) with stable fibrosis characterizes exacerbations requiring anti-inflammatory therapy, while progressive fibrotic changes identify patients potentially requiring anti-fibrotic intensification. This quantitative approach provides objective criteria for therapeutic decision-making and identifies silent progressors in this heterogeneous population, demonstrating that pattern recognition across multiple parameters is superior to single-parameter thresholds. This abstract is funded by: None