Background <p>Lymphovascular invasion (LVI) is a significant histopathological marker associated with poor prognosis in patients. However, there is a notable lack of reliable, non-invasive preoperative tools to predict LVI accurately.</p> Purpose <p>To develop and validate a computed tomography (CT)-based classification and regression tree (CART) model for the preoperative prediction of LVI in patients with clinical stage IA lung adenocarcinoma (LUAD).</p> Materials and methods <p>This multicenter cohort study recruited patients who underwent resection and had a preoperative CT examination. An internal cohort (<i>n</i> = 525) is included to construct the LVI classification and regression tree model (LVI-CART). An external cohort (<i>n</i> = 115) and a public cohort (<i>n</i> = 57) are then used to fully validate the predictive performance of the LVI-CART. Kaplan–Meier survival analysis and univariable Cox regression analyses were conducted to investigate the relationship between predicted LVI status and survival.</p> Results <p>The LVI-CART model includes two features, diameter and nodule type, and shows acceptable performance in predicting pathological LVI, with area under the curve values of 0.719, 0.756, and 0.835 in the internal validation set, external validation set and test set, respectively. A predicted LVI positive relative to the median value in the outcomes cohort was found to be independently associated with 1-, 3-year RFS and 1-, 3-, 5-year OS (all <i>p</i>-values &lt; 0.05).</p> Conclusions <p>The LVI-CART model could be used to preoperatively predict LVI and identify patients with poor prognosis in clinical IA LUAD. The model is like to be simple and easily applicable to risk stratification.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Lymphovascular invasion is a critical histopathological indicator of poor prognosis, necessitating reliable non-invasive preoperative predictive tools.</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>The classification and regression tree model for predicting lymphovascular invasion (LVI-CART) model demonstrates adequate predictive ability for pathological LVI, portending poor recurrence-free and overall survival.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>The LVI-CART model provides clinicians with an easy-to-use method for preoperative identification of patients with clinical stage IA lung adenocarcinoma who are LVI-positive. It also provides a framework for a comprehensive assessment of patient survival risk.</i></p> Graphical Abstract <p></p>