Zhuoying Chen, Li Chen, Lichun Wu, Ke Zhang
CYFRA21-1 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.
OBJECTIVE: This 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.
METHODS: The authors retrospectively analyzed 1009 patients with pathologically confirmed Non-Small Cell Lung Cancer (NSCLC), including 796 cases of LUAD and 213 cases of LUSC.
RESULTS: Serum 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.
CONCLUSION: CYFRA21-1 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.