AI and Rare Diseases ── Heart and lung ── 2026-11-06
Rare cardiovascular and respiratory diseases: treatment, trials and AI
- 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). 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.
Journal IF-equivalent: 6.5 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-05; not the official Clarivate IF)Reference: Lee JY, Chae KJ, Jang Mj, Chung JH, Goo JM, Choi SM, et al. Deep learning–based quantification of CT changes after 1 year of pirfenidone treatment as a prognostic marker in idiopathic pulmonary fibrosis. Insights Imaging. 2026 Nov 3;17(1):277. doi:10.1186/s13244-026-02447-6. PMID: 42975762.Checked: Abstract only - Abstract
BACKGROUND: Emerging evidence suggests that sphingolipid metabolism is involved in respiratory diseases, including idiopathic pulmonary fibrosis (IPF). This study aimed to evaluate the diagnostic and prognostic value of sphingolipid-related genes in IPF and to identify potential sphingolipid-associated biomarkers. METHODS: Sphingolipid-related genes were obtained from the GeneCards database. Non-negative matrix factorization (NMF) was performed to identify molecular clusters and differentially expressed genes (DEGs). High-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to determine fibroblast-associated genes. The intersecting genes were used to construct diagnostic and prognostic models using multiple machine learning algorithms. Hub genes were identified from the overlap between the diagnostic and prognostic signatures. In silico gene knockout was conducted using scTenifoldKnk. Functional validation was performed using Cell Counting Kit-8 (CCK-8) assay, wound healing assay, quantitative real-time polymerase chain reaction (RT-qPCR), and western blotting (WB). RESULTS: Three molecular clusters with significant prognostic differences were identified. hdWGCNA revealed 60 key fibroblast-associated genes. Diagnostic and prognostic models constructed from these genes showed promising diagnostic and prognostic utility across independent cohorts, while the prognostic model exhibited some degree of cohort-dependent variability. CCDC80 was identified as a hub gene associated with sphingolipid-defined molecular heterogeneity and fibroblast-related transcriptional programs. scTenifoldKnk analysis indicated that simulated CCDC80 knockout affected fibrosis-related signaling pathways. In vitro experiments demonstrated that CCDC80 knockdown attenuated fibroblast proliferation, migration, and fibrotic activation. CONCLUSION: This study identified sphingolipid-related molecular signatures with potential diagnostic and prognostic value in IPF and highlighted CCDC80 as a candidate profibrotic regulator. Further validation in larger and clinically standardized cohorts is required before clinical translation. CLINICAL TRIAL NUMBER: Not applicable.
Journal IF-equivalent: 3.4 (OpenAlex 2-year mean citedness, value as of 2026-10-09, retrieved 2026-10-10; not the official Clarivate IF)Reference: Liao Y, Yang L, Liu X, Yang L. Integrative Multi-omics and machine learning analysis of sphingolipid-associated molecular stratification identifies diagnostic and prognostic signatures in idiopathic pulmonary fibrosis. Orphanet J Rare Dis. 2026 Aug 25;21(1):384. doi:10.1186/s13023-026-04563-4. PMID: 42977968.Checked: Abstract only