Objective To validate the performance of a CT imaging feature-based prediction model for identifying high-grade histological patterns (HGP), specifically micropapillary and solid subtypes, in stage ⅠA invasive lung adenocarcinoma. Methods A previously developed prediction model was applied to a cohort of 650 patients with stage ⅠA lung adenocarcinoma from the Fourth Hospital of Hebei Medical University. The model’s ability to discriminate HGP (assessed by area under the receiver operating characteristic curve), calibration, and clinical utility were evaluated based on extracted imaging parameters including tumor size, density, and lobulation. Results Validation revealed that the model demonstrated good performance in discriminating HGP (area under the curve>0.7). Calibration of the original model improved its calibration performance. Decision curve analysis (DCA) indicated that the model’s predicted HGP patient population closely approximated the actual population when using a threshold probability>0.6. Conclusion This study confirms the effectiveness of a CT imaging feature-based prediction model for identifying HGP in stage ⅠA lung adenocarcinoma in a clinical setting. Successful application of this model may be significant for determining surgical strategies and improving patient prognosis. Despite certain limitations, these findings provide new directions for future research.