〔Abstract〕 Objective To explore the factors influencing the efficacy of first-generation EGFR tyrosine kinase inhibitors(EGFR-TKIs) in patients with EGFR-mutated advanced lung adenocarcinoma and to construct and validate a corresponding nomogram prediction model. Methods A total of 220 patients with EGFR-mutated advanced lung adenocarcinoma treated with icotinib were enrolled and randomly divided into a training group(154 cases) and a validation group(66 cases) in a 7 ∶3 ratio. Cox regression analysis was performed to identify factors affecting the efficacy of first-generation EGFR-TKIs in the training group. A prediction model was constructed, and calibration curves and receiver operating characteristic(ROC) curves were plotted to validate the model′s performance. Results In the training group, the objective response rate was 35.71%, the disease control rate was 77.27%, the median progression-free survival(PFS) was 12.5 months, the median overall survival was 18 months, the 2-year OS rate was 66.23%, and the PFS rate was 42.21%. Univariate analysis showed that brain metastasis, bone metastasis, TNM stage, differentiation degree, neutrophil-to-lymphocyte ratio(NLR), post-treatment p53 levels, p53 difference(Δp53), post-treatment cancer antigen gene(CAGE) levels, and CAGE difference(ΔCAGE) were associated with PFS(P2=4.429, P=0.351). ROC curve analysis in the training group showed that the nomogram model had a sensitivity of 80.00%, specificity of 77.53%, and AUC of 0.864 for predicting therapeutic efficacy, while the validation group showed a sensitivity of 74.08%, specificity of 71.43%, and AUC of 0.835. Conclusion Changes in lung cancer autoantibodies(Δp53 and ΔCAGE), TNM stage, and NLR are key factors influencing the efficacy of first-generation EGFR-TKIs in EGFR-mutated advanced lung adenocarcinoma. The nomogram prediction model based on p53 and CAGE demonstrates good predictive performance.