Improved diagnostic accuracy with three lung tumor markers compared to six-marker panel

Area of Science:

• Oncology
• Biomarker Discovery
• Diagnostic Accuracy

Background:

• Lung cancer diagnosis often involves multiple tumor markers, increasing sensitivity but also false positives.
• Patients may undergo numerous tests fearing missed cancer diagnosis.
• There is a need for a more refined panel of lung cancer biomarkers.

Purpose of the Study:

• To develop and validate a concise panel of tumor markers for lung cancer diagnosis.
• To improve the diagnostic performance and reduce false positives compared to existing multi-marker panels.

Main Methods:

• A logistic regression model was used to identify significant tumor markers for lung cancer diagnosis.
• A panel of three markers (carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA], and neuron-specific enolase [NSE]) was created.
• The diagnostic performance (Area Under the Curve [AUC]) of the three-marker panel was compared against a panel of six markers.

Main Results:

• Logistic regression identified CEA, CYFRA, and NSE as independently associated with lung cancer in 1,733 patients.
• The three-marker panel achieved an AUC of 0.656, outperforming the six-marker panel (AUC = 0.575).
• The three-marker panel demonstrated better diagnostic performance (P<0.001) with improved specificity.

Conclusions:

• A panel of three tumor markers (CEA, CYFRA, NSE) provides superior diagnostic value for lung cancer compared to six markers.
• This optimized panel enhances predictive value by significantly reducing the rate of false positives.
• The findings support a more efficient and accurate approach to lung cancer biomarker testing.