Development and internal validation of predictive models for spread through air spaces in clinical stage IA lung adenocarcinoma.

objectiveSpread through air spaces (STAS) in lung adenocarcinoma impacted prognosis and treatment decisions, but lacked reliable preoperative prediction. We aimed to construct an easy-to-use model for clinical stage IA adenocarcinoma patients.

methodsThis study analyzed 1212 patients with clinical stage IA lung adenocarcinoma undergoing lung resections from November 2020 to January 2022. Two logistic regression models were developed. Model 1 used demographic and computed tomography features, and Model 2 incorporated maximum standardized uptake values additionally. Internal validation used tenfold cross-validation. Model discrimination and calibration were described by the area under the curve (AUC) and Spiegelhalter z test, respectively.

resultsPrevalence of STAS was 10.6%. Model 1 consisted of maximum tumor diameter, smoking history, location, spiculation and lobulation, showing moderate discrimination (AUC = 0.700). Model 2 consisted of smoking history, the maximum standardized uptake value, spiculation and lobulation, receiving an AUC of 0.807 and good calibration. Model 2 has a sensitivity and a specificity of 0.857 and 0.652. A nomogram for Model 2 was also developed.

conclusionOur study developed and validated two predictive models for STAS for clinical stage IA lung adenocarcinoma. Model 2, integrating maximum standardized uptake value, outperformed Model 1 and offered a more comprehensive approach to predicting STAS. Surgeon could consider the results of Model 2 and intraoperative frozen sections sequentially to optimize surgical strategies. External validation remained warranted.