What question did this study set out to answer?

The study aims to determine whether quantitative lung fibrosis metrics from CT scans can predict pneumonitis risk in NSCLC patients undergoing immune checkpoint inhibitor treatment.

May 20, 2026

B23-01 Lower Lobe and Total Lung Quantitative Fibrosis Scores Identified by Quantitative CT Predict Pneumonitis Risk in Non-Small Cell Lung Cancer (NSCLC)

Key Points

The study aims to determine whether quantitative lung fibrosis metrics from CT scans can predict pneumonitis risk in NSCLC patients undergoing immune checkpoint inhibitor treatment.
Analyzed a cohort of 241 metastatic NSCLC patients treated with immune checkpoint inhibitors.
Conducted quantitative analysis on pre-treatment CT scans to extract lung fibrosis metrics.
Used univariate logistic regression to assess the relationship between these metrics and the risk of pneumonitis.
Lower-lobe fibrosis mass metrics were significantly associated with increased pneumonitis risk, including ground-glass opacity mass (OR 1.005, 95% CI 1.001-1.010, p=0.030).
Peel (peripheral) QLFs demonstrated a stronger association with pneumonitis risk than core (central) QLFs, particularly with peel consolidation volume (OR 1.102, 95% CI 1.013-1.198, p=0.023).
Total-lung QLF mass showed a trend towards association with increased risk (OR 1.002, 95% CI 1.000-1.004, p=0.086).

Abstract

Abstract Rationale Interstitial lung abnormalities (ILAs) are established risk factors for immune checkpoint inhibitor-related pneumonitis (ICI-P), but qualitative ILA assessment is subjective. We evaluated whether quantitative lung fibrosis (QLF) metrics from pre-treatment computed tomography (CT) predict ICI-P in patients with non-small cell lung cancer (NSCLC) receiving immune checkpoint inhibitors (ICIs). Methods A cohort of 241 metastatic NSCLC patients treated with ICIs were retrospectively analyzed. Pre-treatment CTs underwent quantitative analysis using commercial software (VIDA) to extract QLFs representing consolidation, ground-glass opacity (GGO), emphysema, reticulation, and honeycombing. QLFs were quantified by percentage, volume, and estimated mass at regional (upper vs. lower lobes, core (central) vs. peel (peripheral)) and total lung levels. Univariate logistic regression assessed associations between QLFs and ICI-P. Results Quantitative analysis demonstrated regional associations with ICI-P. Upper-lobe QLFs were not associated with ICI-P (all p ≥ 0.13). In contrast, lower-lobe fibrosis mass metrics were significantly associated with increased ICI-P risk, including GGO mass (OR 1.005, 95% CI 1.001-1.010, p = 0.030), QILD mass (OR 1.004, 95% CI 1.000-1.008, p = 0.031), and QILA mass (OR 1.004, 95% CI 1.001-1.007, p = 0.024). Total-lung QLF mass also trended toward association (OR 1.002, 95% CI 1.000-1.004, p = 0.086). In subregional analysis, peel (peripheral) QLFs showed stronger association with ICI-P than core (central) QLFs; peel consolidation volume was associated with pneumonitis (OR 1.102, 95% CI 1.013-1.198, p = 0.023), whereas no core metric reached significance, suggesting that subpleural fibrosis may preferentially drive pneumonitis risk. Conclusions Total lung and lower lobe QLFs from quantitative CT are associated with ICI-P risk in NSCLC. Mass-based QLF metrics showed the most consistent predictive signal, and lower lobe consolidation localized this risk. Core-peel analysis suggests that pleural surface fibrosis may confer additional risk. Incorporating total and regional QLFs into pre-ICI imaging may improve identification of patients at elevated pneumonitis risk and guide surveillance strategies. This abstract is funded by: None

Bookmark

B23-01 Lower Lobe and Total Lung Quantitative Fibrosis Scores Identified by Quantitative CT Predict Pneumonitis Risk in Non-Small Cell Lung Cancer (NSCLC)

Key Points

Abstract

Cite This Study