To investigate the predictive value of size measurements of the solid components in pulmonary subsolid nodules with different CT window settings and to evaluate the degree of pathological malignancy in lung adenocarcinoma. Methods: The preoperative chest CT images and pathological data of 125 patients were retrospectively evaluated. The analysis included 127 surgically resected lung adenocarcinomas that manifested as subsolid nodules. All subsolid nodules were divided into two groups: 69 in group A, including 22 adenocarcinomas in situ (AIS) and 47 minimally invasive adenocarcinomas (MIA); 58 in group B that included invasive pulmonary adenocarcinomas (IPA). The size of the solid component in the pulmonary subsolid nodules were calculated in one dimensional, two dimensional and three dimensional views using lung and mediastinal windows that were recorded as 1D-SCLW, 2D-SCLW, 3D-SCLW, 1D-SCMW, 2D-SCMW and 3D-SCMW, respectively. Furthermore, the volume of solid component with a threshold of -300HU was measured using lung window (3D-SCT). All the quantitative features were evaluated by the Mann-Whitney U test. Multivariate analysis was used to identify the significant predictor of the degree of pathological malignancy. Results: The 1D-SCLW, 2D-SCLW, 3D-SCLW, 1D-SCMW, 2D-SCMW, 3D-SCMW and 3D-SCT views of group B were significantly larger than those of group A (p < 0.001). The multivariate logistic regression analysis indicated that 3D-SCT (OR = 1.018, 95%CI: 1.005 ~ 1.03, p <0.05＝was the independent predictive factor. The larger SCT was significantly associated with IPAs. Conclusion: 3D-SCT of subsolid nodules during preoperative CT can be used to predict the degree of pathological malignancy in lung adenocarcinoma, which may provide a more objective and convenient selection criterion for clinical application. Advances in knowledge: Applying threshold of -300 HU with lung window setting would be better than other window setting for the evaluation of solid component in subsolid nodules. Computer-aided volumetry of the solid component in subsolid nodules can more accurately predict the degree of pathological malignancy than the other dimensional measurements.