〔Abstract〕 To explore the factors influencing the efficacy of first-generation EGFR tyrosine kinase in- hibitors (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 . Re- sults 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 me- tastasis , 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 as- sociated with PFS (P < 0. 05) . LASSO regression identified four predictive variables , and Cox regression analysis revealed that TNM stage , NLR , Δp53 , and ΔCAGE were independent factors influencing the efficacy of first-gener- ation EGFR-TKIs ( P < 0. 05 ) . The calibration curve passed the Hosmer-Lemeshow goodness-of-fit test ( χ2 = 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-genera- tion EGFR-TKIs in EGFR-mutated advanced lung adenocarcinoma. The nomogram prediction model based on p53 and CAGE demonstrates good predictive performance .