Novel insights into predicting the presence of micropapillary and solid components in stage IA lung adenocarcinoma using machine learning models of modifiable risk factors.

backgroundLung adenocarcinoma (LUAC) patients with micropapillary (MP) and/or solid (S) generally demonstrate a poorer survival prognosis. In the diagnosis and treatment of stage IA LUAC, precisely establishing personalized treatment strategies for patients is crucial for both clinical practice and scientific investigation. Our study aims to develop a novel prediction model based on machine learning (ML) to predict the probability of MP/S patterns in stage IA LUAC patients.

methodsOur retrospective analysis was conducted on 1,933 patients diagnosed with stage IA LUAC

resultsNodule type, spiculation, Carcinoembryonic antigen level, maximum solid component diameter, median CT value, and CT value range were identified as independent influencing factors for predicting MP/S patterns. The K-Nearest Neighbors (KNN) model performed best among all ten models. The internal validation indicated an area under the curve (AUC) of 0.790, a Brier score of 0.167, and a Hosmer-Lemeshow (HL) test P value of 0.817, while external validation yielded an AUC of 0.790, a Brier score of 0.167, and a HL test P value of 0.120. Shapley additive explanation analysis revealed "nodule type" could alter the predicted probability of MP/S component presence by 13.6%, establishing it as a significant factor.

conclusionAn interpretable KNN model was successfully developed to predict the presence of MP/S components in stage IA LUAC patients, demonstrating superior predictive performance. Accurate evaluation of relevant tumor characteristics possesses substantial clinical significance, as it enables guidance on the optimization of surgical approaches to enhance patient prognosis.