ArticleClinics (Sao Paulo, Brazil)2026
Differential analysis of lung adenocarcinoma and lung squamous cell carcinoma based on serum tumor markers: diagnostic performance and clinical utility.
Article in Clinics (Sao Paulo, Brazil), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveThis study evaluated the performance of Serum Tumor Markers (STMs) in differentiating Lung Adenocarcinoma (LUAD) from Lung Squamous Cell Carcinoma (LUSC), as well as their cost-effectiveness and net clinical benefit.
methodsThe authors retrospectively analyzed 1009 patients with pathologically confirmed Non-Small Cell Lung Cancer (NSCLC), including 796 cases of LUAD and 213 cases of LUSC.
resultsSerum levels of Cytokeratin 19 Fragment (CYFRA21-1), Squamous Cell Carcinoma Antigen (SCCA), Neuron-Specific Enolase (NSE), and Pro-Gastrin-Releasing Peptide (ProGRP) were significantly higher in LUSC, whereas Carcinoembryonic Antigen (CEA) was higher in LUAD (all p < 0.05). Among individual markers, SCCA and CYFRA21-1 showed the best discrimination, with Areas Under the Receiver Operating Characteristic Curve (AUCs) of 0.77 and 0.76, respectively, while CEA showed limited performance (AUC = 0.55). A multivariate model incorporating age, CEA, CYFRA21-1, and SCCA achieved an AUC of 0.82 (95% Confidence Interval [95% CI]: 0.79-0.85). At the selected threshold, sensitivity and specificity were 0.76 and 0.72, respectively. Model calibration was acceptable overall despite a statistically significant Hosmer-Lemeshow test (p = 0.002). Decision Curve Analysis (DCA) demonstrated a higher net benefit for the model than strategies of performing immunohistochemistry in all patients or none across threshold probabilities of 0.1-0.7. A stepwise diagnostic strategy ‒ initial STM testing followed by immunohistochemistry ‒ was associated with reduced projected diagnostic costs.
conclusionCYFRA21-1 and SCCA were the most informative STMs for distinguishing LUAD from LUSC. A parsimonious multivariate model demonstrated good discrimination, acceptable calibration, and clinical net benefit. External validation and prospective evaluation are warranted before clinical implementation.
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