Diagnosis of malignant pulmonary nodules can greatly reduce the occurrence of lung cancer death, and computed tomography (CT) is commonly used in diagnosis. In addition, tumor-associated autoantibodies (TAAbs) show high specificity and stability. We aim to establish a computable risk model of pulmonary nodules by combining CT with TAAb detection.

The concentrations of 7 TAAbs (p53, PGP9.5, SOX2, GAGE7, GBU4-5, CAGE, MAGEA1, and CAGE) were assayed using the enzyme-linked immunosorbent assay in 136 patients with pulmonary nodules (84 with newly diagnosed lung adenocarcinoma, 21 with squamous cell carcinoma, and 31 with benign nodules) and 42 control subjects without pulmonary nodules. We then drew receiver operating characteristic curves and conducted logistic regression to analyze the diagnostic efficiency of our method in the detection of lung cancer.

The positivity rate of the 7 TAAbs was 49.5%, and the specificity was 83.6%. Our regression results indicated 65% overall accuracy, 44.76% sensitivity, and 76.71% specificity. Notably, when combined with CT imaging and the demographic characteristics, diagnostic accuracy increased to 73.4%, sensitivity to 61.5%, and specificity to 87.1%. The positive predictive value and negative predictive value were 93% and 41%, respectively.

Our study provides a method that combines 7 serum TAAbs with imaging and demographic characteristics to diagnose malignant pulmonary nodules more accurately than existing methods.