{
  "abstract": "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).\n\nMaterials 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.\n\nResults: 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).\n\nConclusions: Changes in deep learning–based CT quantification results over 1 year of pirfenidone treatment demonstrated significant prognostic value in patients with IPF.\n\nKey Points: Question Disease progression and mortality in patients with idiopathic pulmonary fibrosis (IPF) continue to occur unpredictably beyond 1 year of pirfenidone treatment.\n\nFindings Changes in deep learning–based quantification of normal lung and fibrosis extent after 1 year of pirfenidone treatment were significant prognostic predictors.\n\nCritical 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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Ji Young Lee"
    },
    {
      "name": "Kum Ju Chae"
    },
    {
      "name": "Myoung-jin Jang"
    },
    {
      "name": "Jonathan H. Chung"
    },
    {
      "name": "Jin Mo Goo"
    },
    {
      "name": "Sun Mi Choi"
    },
    {
      "name": "Jimyung Park"
    },
    {
      "name": "Jong Hyuk Lee",
      "orcid": "0000-0002-9594-683X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2eajzyzhfd12n.bib",
  "cited_by": 0,
  "doi": "10.1186/s13244-026-02447-6",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13731539/",
  "id": "2eajzyzhfd12n",
  "issns": [
    "1869-4101"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s13244-026-02447-6",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Article"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s13244-026-02447-6.pdf",
  "pmcid": "PMC13731539",
  "pmid": "42975762",
  "published": "2026-11-03",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/2eajzyzhfd12n.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:27:25Z",
      "native_id": "10.1186/s13244-026-02447-6",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s13244-026-02447-6"
    },
    {
      "first_seen": "2026-11-08T12:21:43Z",
      "native_id": "PMC13731539",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s13244-026-02447-6"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:1869-4101",
      "subfield": "imaging"
    }
  ],
  "title": "Deep learning–based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis",
  "title_text": "Deep learning–based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:21:43Z",
  "url": "https://research.draconitic.com/paper/2eajzyzhfd12n",
  "venue": "Insights into Imaging",
  "versions": []
}