ObjectiveBy integrating biological assays with imaging evaluations, a clinical prediction model is developed based on a cohort of ten thousand individuals to enhance the accuracy of distinguishing between benign and malignant pulmonary nodules. MethodsA retrospective analysis was conducted on the clinical data of 1,017 patients with pulmonary nodules who underwent chest CT and testing for seven types of lung cancer-related serum autoantibodies (7-AABs) at the First Affiliated Hospital of Zhejiang University School of Medicine from January 2020 to April 2024, all of whom had definitive pathological diagnosis results. Statistical analysis was performed using R and MSTATA software, with the development of univariate and multivariate logistic regression models, as well as a nomogram model. The performance of the models was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). ResultsA total of 1,017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58.12±12.41) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (57.99±12.56) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest AUC value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis included age, imaging nodule diameter, nodule characteristics, and the combination of CT and 7-AABs testing as independent predictive factors to construct a nomogram prediction model. The AUC values for this model were 0.831 and 0.861 in the training and validation sets, respectively, demonstrating excellent performance in decision curve analysis (DCA). ConclusionThe combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.