Deep learning–based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis

@article{lee2026deep,
  title = {{Deep learning–based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis}},
  author = {Ji Young Lee and Kum Ju Chae and Myoung-jin Jang and Jonathan H. Chung and Jin Mo Goo and Sun Mi Choi and Jimyung Park and Jong Hyuk Lee},
  journal = {Insights into Imaging},
  year = {2026},
  month = nov,
  doi = {10.1186/s13244-026-02447-6},
  url = {https://link.springer.com/article/10.1186/s13244-026-02447-6},
  note = {CC BY 4.0},
}

TY  - JOUR
TI  - Deep learning–based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis
AU  - Ji Young Lee
AU  - Kum Ju Chae
AU  - Myoung-jin Jang
AU  - Jonathan H. Chung
AU  - Jin Mo Goo
AU  - Sun Mi Choi
AU  - Jimyung Park
AU  - Jong Hyuk Lee
PY  - 2026
DA  - 2026/11/03
JO  - Insights into Imaging
DO  - 10.1186/s13244-026-02447-6
UR  - https://link.springer.com/article/10.1186/s13244-026-02447-6
AB  - Objectives: To evaluate the prognostic value of changes in deep learning–based CT quantification following 1 year of pirfenidone treatment in patients with idiopathic pulmonary fibrosis (IPF).  Materials and methods: This study included patients with IPF who received at least 1 year of pirfenidone between September 2009 and March 2022 at two hospitals. The extent of normal lung and fibrosis (sum of reticular opacity and honeycombing) was quantified on baseline and 1-year follow-up chest CT scans using a deep learning–based tool, and the changes were calculated. The primary outcome was a composite of all-cause mortality or lung transplantation; the secondary outcome was a ≥ 10% absolute decline in forced vital capacity (FVC) occurring after the 1-year follow-up. Associations between changes in CT quantification and these outcomes were assessed using Cox proportional hazards regression analyses.  Results: A total of 436 patients (341 men; mean age, 69.8 years) were included. Greater preservation of normal lung extent over 1 year was associated with a lower risk of the composite outcome (adjusted hazard ratio (HR) per 5% increase: 0.90; 95% confidence interval (CI): 0.85, 0.96; p < 0.001), whereas increases in fibrosis extent were associated with a higher risk (adjusted HR per 5% increase: 1.16, 95% CI: 1.06, 1.27, p = 0.002). Regarding the secondary outcome of ≥ 10% absolute FVC decline, only the change in fibrosis extent remained a significant predictor (adjusted HR per 5% increase: 1.26, 95% CI: 1.09, 1.45, p = 0.001).  Conclusions: Changes in deep learning–based CT quantification results over 1 year of pirfenidone treatment demonstrated significant prognostic value in patients with IPF.  Key Points: Question Disease progression and mortality in patients with idiopathic pulmonary fibrosis (IPF) continue to occur unpredictably beyond 1 year of pirfenidone treatment.  Findings Changes in deep learning–based quantification of normal lung and fibrosis extent after 1 year of pirfenidone treatment were significant prognostic predictors.  Critical relevance statement Incorporating 1-year changes in deep learning–based CT quantification improves prognostication of mortality, lung transplantation, and lung function decline, supporting more individualized risk stratification in idiopathic pulmonary fibrosis.
SN  - 1869-4101
ER  -