Serum Tumor Marker Profiles in Interstitial Lung Diseases: Implications for Differential Diagnosis and Disease Severity Assessment
Du, Y.; Song, X.; Ding, Q.
Show abstract
BackgroundInterstitial lung diseases (ILDs), including idiopathic pulmonary fibrosis (IPF) and connective tissue disease-associated ILD (CTD-ILD), share similar features that complicate diagnosis. Tumor markers are often elevated in ILD, yet their diagnostic utility remains unclear. MethodsThis retrospective study included ILD patients hospitalized between 2018 and 2025. Serum levels of alpha-fetoprotein, carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 199, CA125, CA153, neuron-specific enolase (NSE), and cytokeratin 19 fragment (CYFRA 21-1) were analyzed. Arterial blood gases and erythrocyte sedimentation rates (ESRs) were also collected. Statistical analyses involved the Kruskal-Wallis test, Dunns post hoc test, Spearmans correlation, logistic regression, and receiver operating characteristic (ROC) curve analysis. ResultsCEA, CA199, and CA125 levels varied significantly among ILD subtypes (all p < 0.05). NSE differed among CTD-ILD subgroups (p = 0.0409). In IPF, CEA and NSE correlated inversely with PaO2 (r = -0.1556, p = 0.0380; r = -0.2205, p = 0.0031). In CTD-ILD, NSE correlated negatively with PaCO2 (r = -0.1811, p = 0.016), and CYFRA 21-1 with PaO2 (r = -0.1999, p = 0.0078). A diagnostic model incorporating CEA, CA199, sex, age, smoking, PaO2, and ESR differentiated IPF from CTD-ILD with an AUC of 0.833 (95% CI: 0.790-0.876), showing 73.6% sensitivity and 82.4% specificity at a cutoff of 0.569, outperforming single markers. ConclusionCEA, CA199, and CA125 aid in distinguishing ILD subtypes, while CEA, NSE, and CYFRA 21-1 correlate with impaired gas exchange. The combined clinical and biomarker model demonstrated superior performance in discriminating IPF from CTD-ILD, highlighting its clinical potential.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Development and Validation of a Nomogram for Predicting False Negative IGRA Results in Pulmonary Tuberculosis Patients Using Propensity Score Matching 94%
- SARS-CoV-2 infection induces mixed M1/M2 phenotype in circulating monocytes and alterations in both dendritic cell and monocyte subsets 94%
- Quantitative analysis of chest computed tomography of COVID-19 pneumonia using a software widely used in Japan 94%
Similar papers in this journal
- Vascular Inflammation in Lungs of Patients with Fatal Coronavirus Disease 2019 (COVID-19) Infection: Possible role for the NLRP3 inflammasome 92%
- The sialidase NEU3 promotes pulmonary fibrosis in mice 92%
- Biomarkers of collagen synthesis predict progression in the PROFILE idiopathic pulmonary fibrosis cohort 91%
Similar papers in this journal
- The burden of Progressive Fibrotic Interstitial lung disease across the UK 92%
- Single cell sequencing reveals cellular landscape alterations in the airway mucosa of patients with pulmonary long COVID 92%
- Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics 90%
Similar papers in this journal
- Monocyte and neutrophil levels are potentially linked to progression to IPF for patients with indeterminate UIP CT pattern 95%
- Phase 2 Study Design and Analysis Approach for BBT-877: An Autotaxin Inhibitor Targeting Idiopathic Pulmonary Fibrosis 90%
- Hospital outcomes in interstitial lung disease-related admissions: a multicentre retrospective study in the North West of England. 89%
Similar papers in this journal
- Racial and ethnic associations with interstitial lung disease and healthcare utilization in patients with systemic sclerosis 94%
- Anti-nuclear matrix protein 2 antibody-positive idiopathic inflammatory myopathies represent extensive myositis without dermatomyositis-specific rash 93%
- Evaluation of SIGLEC1 in the diagnosis of suspected systemic lupus erythematosus 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.