Comparative evaluation of machine learning models for predicting PD-L1 high expression in resectable NSCLC: a dual-center study integrating [

Background: Accurate prediction of programmed death-ligand 1 (PD-L1) high expression (tumor proportion score [TPS] ≥50%) is important for identifying patients with resectable non-small cell lung cancer (NSCLC) who may benefit from neoadjuvant chemoimmunotherapy (nCIT). This study aimed to evaluate eight machine learning (ML) algorithms and develop a non-invasive, [ Methods: A retrospective, dual-center cohort of 269 patients with stage IB-IIIB resectable NSCLC who underwent [ Results: Of the 269 enrolled patients, 79.9% were male and 32.0% were older than 65 years. LASSO regression identified five core predictors: smoking status, histological type, T stage, histological grade, and SUVmax. In the independent external validation set, Support Vector Machine (SVM) (AUC = 0.858), Random Forest (RF) (AUC = 0.849), and Logistic Regression (LR) (AUC = 0.833) demonstrated good discriminative performance. However, DeLong's test indicated no statistically significant advantage of the complex models over the traditional LR model (all adjusted P = 1.000). Prioritizing model transparency and interpretability, an LR-based nomogram was established, which exhibited favorable calibration and provided clinical net benefit across a wide range of threshold probabilities in both cohorts. Conclusions: We developed and validated an interpretable, [