Related Experiment Video
Updated: Oct 9, 2026
Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
Published on: June 20, 2025
Predictive Model for Identifying Fibrotic Interstitial Lung Abnormalities on Computed Tomography for Respiratory
Kazuya Ichikado1, Hidenori Ichiyasu2, Kazuhiro Iyonaga3
1Division of Respiratory Medicine, Saiseikai Kumamoto Hospital, Kumamoto, Japan.
Background And Objective:
Interstitial lung abnormalities (ILAs) are often unreported, and fibrotic ILAs carry a worse prognosis. In this study, we aimed to develop and validate a prediction model for fibrotic ILAs using non-radiological factors.
Methods:
Using data from the Kumamoto ILA study (a multicenter prospective cohort in Japan), we developed a four-variable model (age, pack-years, fine crackles, elevated serum surfactant protein-D) in 164 patients (2022-2023, derivation cohort) and temporally validated it in 207 patients (2023-2024, validation cohort), with internal-external cross-validation for between-center heterogeneity. A simplified integer score was similarly assessed against 24-month functional decline and treatment initiation within 3 years.
Results:
Fibrotic ILAs comprised 82% of cases (approximately 60% subsequently met ATS-defined ILD criteria), consistent across cohorts. The four-variable model achieved an AUC of 0.79 (derivation) and 0.76 (validation; pooled 0.76, 95% CI 0.68-0.82), with good calibration and minimal between-center heterogeneity. The integer score performed similarly (AUC 0.79/0.75) and correlated with functional decline (%FVC ≥ 10% or %DLco ≥ 15%) and treatment initiation.
Conclusion:
Age, smoking exposure, fine crackles, and elevated serum surfactant protein-D are key predictors of progressive fibrotic ILAs, supporting early risk stratification and clinical decision-making.
Trial Registration:
UMIN000045149/2021.12.1.
