Quantitative CT lung fibrosis and immune checkpoint inhibitor–related pneumonitis in NSCLC.
Abstract
e20531 Background: 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 pretreatment computed tomography (CT) are associated with ICI-P in patients with non–small cell lung cancer (NSCLC) receiving immune checkpoint inhibitors (ICIs). Methods: We retrospectively analyzed 241 patients with metastatic NSCLC treated with ICIs. Pretreatment CT scans 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 total lung, lobar (upper vs lower), and subregional (core [central] vs peel [peripheral]) levels. Associations between QLFs and ICI-P were evaluated using univariate logistic regression. Results: Regional QLFs demonstrated differential associations with ICI-P. Upper-lobe QLFs were not associated with pneumonitis (all p≥0.13). In contrast, lower-lobe mass-based fibrosis metrics were associated with increased ICI-P risk, including GGO mass (OR 1.005, 95% CI 1.001–1.010; p=0.030), quantitative ILD (QILD) mass (OR 1.004, 95% CI 1.000–1.008; p=0.031), and quantitative ILA (QILA) mass (OR 1.004, 95% CI 1.001–1.007; p=0.024). Total-lung QILA mass showed a trend toward association (OR 1.002, 95% CI 1.000–1.004; p=0.086). Subregional analysis demonstrated stronger associations in peripheral (peel) regions; peel consolidation volume was associated with ICI-P (OR 1.102, 95% CI 1.013–1.198; p=0.023), whereas no core metric reached significance. Conclusions: Quantitative fibrosis metrics from pretreatment CT, particularly lower-lobe and peripheral mass-based QLFs, are associated with immune checkpoint inhibitor–related pneumonitis in NSCLC. Incorporation of regional quantitative fibrosis measures into baseline imaging assessment may help identify patients at elevated pneumonitis risk and inform surveillance strategies. These findings support further multivariable validation of quantitative CT fibrosis metrics as imaging biomarkers for immunotherapy-related pneumonitis. Associations between quantitative lung fibrosis metrics and immune checkpoint inhibitor–related pneumonitis in NSCLC. Region Metric (Mass-based QLF) (gm) OR 95% CI p-value Lower lobe GGO Mass 1.005 1.001–1.010 0.030 Lower lobe QILD Mass 1.004 1.000–1.008 0.031 Lower lobe QILA Mass 1.004 1.001–1.007 0.024 Upper lobe No significant predictors — — ≥0.13 Total lung QILA Mass 1.002 1.000–1.004 0.086
Article Details
Journal Info
Journal of Clinical Oncology
Lippincott Williams & Wilkins
Authors (20)
Mitchell Boshkos
1The University of Texas MD Anderson Cancer Center, Division of Internal Medicine, Houston, United States
Amgad Muneer
The University of Texas MD Anderson Cancer Center, Houston, TX
Eman Showkatian
The University of Texas MD Anderson Cancer Center, Houston, TX
Felipe Soto
1Indiana University, Department of Internal Medicine, Indianapolis, United States
Ferando Camacho
The University of Texas MD Anderson Cancer Center, Houston, TX
Gregory Gaskey
The University of Texas MD Anderson Cancer Center, Houston, TX
Mazen Aiche
The University of Texas MD Anderson Cancer Center, Houston, TX
Sarah Forsythe
The University of Texas MD Anderson Cancer Center, Houston, TX
Karthik Suresh
Department of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD
Rupali Sood
Johns Hopkins Hospital, Baltimore, MD
Mohammad I. Ghanbar
Department of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD
Samuel Peterson
The University of Texas MD Anderson Cancer Center, Houston, TX
Joshua Schirm
The University of Texas MD Anderson Cancer Center, Houston, TX
Kevin Ho
Ohio State University, Columbus, OH
Girish Shroff
The University of Texas MD Anderson Cancer Center, Houston, TX
Aung Naing
Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX
Saadia Faiz
The University of Texas MD Anderson Cancer Center, Houston, TX
Jia Wu
Mehmet Altan
Ajay Sheshadri