Purpose: To investigate the predictive value of energy spectrum CT quantitative parameters combined with serum tumor markers (CEA, CA-125) on Ki-67 expression in lung adenocarcinoma. Methods: The clinicopathological and imaging data of 64 patients with lung adenocarcinoma confirmed by pathology from June 2020 to February 2022 were retrospectively analyzed. All patients underwent dual-phase energy spectrum CT examination, and serum CEA and CA-125 levels before treatment were clear. Based on postoperative pathological results, patients were divided into two groups, the high expression group of Ki-67 (>30%) and the low expression group of Ki-67 (≤30%). The iodine value (IC), standardized iodine ratio (NIC), and the slope of the energy spectrum curve (λ
HU) were measured by a dual-energy post-processing workstation. The expression levels of SERUM CEA and CA-125 before treatment were obtained according to medical records. Statistical analysis of the data was performed with SPSS 22.0; t
-test or Mann−Whitney U test and \chi^2
tests were used to compare the differences in parameters between the two groups, and the ROC (receiver operating characteristic curve, ROC) curve was used to evaluate the prediction efficiency of the parameters. Results: The IC, NIC, and λ
HU values in the low expression group were higher than those of the high expression group, and the differences were statistically significant. Serum CEA and CA-125 levels in the Ki-67 high expression group were higher than those of the low expression group, and the difference was statistically significant. There were no significant differences in other parameters between the two groups. ROC curve analysis showed that CEA had the best predictive efficiency for Ki-67, with an area under the curve of 0.697, sensitivity of 39.17%, and specificity of 100%. Conclusions: The quantitative parameters of energy spectrum CT in the venous phase, serum CEA, and CA12-5 levels have a certain value in predicting the expression of Ki-67, which can provide a basis for selecting a clinical treatment plan.