The Value of a Seven-Autoantibody Panel Combined with the Mayo Model in the Differential Diagnosis of Pulmonary Nodules

Abstract

Background: Identifying malignant pulmonary nodules and detecting early-stage lung cancer (LC) could reduce mortality. This study investigated the clinical value of a seven-autoantibody (7-AAB) panel in combination with the Mayo model for the early detection of LC and distinguishing benign from malignant pulmonary nodules (MPNs).

Methods: The concentrations of the elements of a 7-AAB panel were quantitated by enzyme-linked immunosorbent assay (ELISA) in 806 participants. The probability of MPNs was calculated using the Mayo predictive model. The performances of the 7-AAB panel and the Mayo model were analyzed by receiver operating characteristic (ROC) analyses, and the difference between groups was evaluated by chi-square tests (χ ²).

Results: The combined area under the ROC curve (AUC) for all 7 AABs was higher than that of a single one. The sensitivities of the 7-AAB panel were 67.5% in the stage I-II LC patients and 60.3% in the stage III-IV patients, with a specificity of 89.6% for the healthy controls and 83.1% for benign lung disease patients. The detection rate of the 7-AAB panel in the early-stage LC patients was higher than that of traditional tumor markers. The AUC of the 7-AAB panel in combination with the Mayo model was higher than that of the 7-AAB panel alone or the Mayo model alone in distinguishing MPN from benign nodules. For early-stage MPN, the sensitivity and specificity of the combination were 93.5% and 58.0%, respectively. For advanced-stage MPN, the sensitivity and specificity of the combination were 91.4% and 72.8%, respectively. The combination of the 7-AAB panel with the Mayo model significantly improved the detection rate of MPN, but the positive predictive value (PPV) and the specificity were not improved when compared with either the 7-AAB panel alone or the Mayo model alone.

Conclusion: Our study confirmed the clinical value of the 7-AAB panel for the early detection of lung cancer and in combination with the Mayo model could be used to distinguish benign from malignant pulmonary nodules.

Figure 1

Concentration and the area under the curve (AUC) of each autoantibody between lung cancer (LC) cases and health controls (HC). (a) p53; (b) PGP9.5; (c) SOX2; (d) GAGE7; (e) GBU4-5; (f) MAGEA1; (g) CAGE; (h) AUCs for each autoantibody; (i) combined AUC for the 7-AAB panel.

Figure 2

Diagnostic performance of the 7-AAB panel in lung cancer (LC) patients. SCC: squamous cell carcinoma; SCLC: small-cell lung cancer; TM: tumor markers; HC: healthy controls; BLD: benign pulmonary disease. (a) Sensitivities of the 7-AAB panel in different disease stages and histological types; (b) specificities of the 7-AAB panel in HC and BLD patients; (c) comparison of the 7-AAB panel and traditional tumor markers in LC patients.

Figure 3

Diagnostic performance of the 7-AAB panel and the Mayo model between benign pulmonary nodules (BPN) and malignant pulmonary nodules (MPN) with early-stage. (a) The AUC of the 7-AAB panel; (b) the AUC of the Mayo model; (c) the AUC of the 7-AAB panel combination with the Mayo model; (d) sensitivity in MPN patients with early stage; (e) positive predictive values (PPVs) in MPN patients with early stage; (f) specificity in BPN patients.

Figure 4

Diagnostic performance of the 7-AAB panel and the Mayo model between benign pulmonary nodules (BPN) and malignant pulmonary nodules (MPN) with advanced stage. (a) The AUC of the 7-AAB panel; (b) the AUC of the Mayo model; (c) the AUC of the 7-AAB panel combination with the Mayo model; (d) sensitivity in MPN patients with advanced stage; (e) positive predictive values (PPVs) in MPN patients with advanced stage; (f) specificity in BPN patients.