Abstract
BACKGROUND: Single Tumor-associated autoantibodies (TAAbs) and tumor-associated antigens (TAAs) have been found to have lower diagnostic efficacy in lung cancer. Our objective is to develop and validate a lung cancer prediction model that utilizes TAAbs and TAAs and to enhance the accuracy of lung cancer detection. METHODS: 1830 subjects were randomly divided into training and validation sets at a 7:3 ratio for this study. Lasso regression analysis was used to remove collinear variables, whereas univariate logistic regression analysis was employed to identify potential independent risk factors for lung cancer. A diagnostic model was constructed using multivariate logistic analysis. The results were presented as a nomogram and assessed for various performance measures, including area under the curve, calibration curve, and decision curve analysis. RESULTS: The diagnostic model was developed using gender, age, GAGE7, MAGE-A1, CA125, and CEA as variables. The training set had an AUC of 0.787, while the validation set had an AUC of 0.750. The calibration curves of the training and validation sets showed a strong agreement between anticipated and observed values. The nomogram performed better than any individual variable in both the training and validation sets in terms of net benefits for lung cancer detection, according to DCA analysis. CONCLUSIONS: This study proposes a diagnostic model for lung cancer that uses TAAbs and TAAs and incorporates individual characteristics. This model can be easily applied to personalized diagnosis.