Explainable AI reveals squamous histology and U-shaped PD-L1 patterns as primary subgroup predictors of neoadjuvant and perioperative immunotherapy benefit in NSCLC: a machine learning analysis

The introduction of neoadjuvant and perioperative immunotherapy has broadened treatment options for resectable non-small cell lung cancer (NSCLC). However, clinical benefit varies across subpopulations, and standard linear models cannot fully capture the complex feature interactions and trial-level differences found in aggregate data. We applied an integrated framework combining multilevel meta-regression with an inverse-variance weighted eXtreme Gradient Boosting (XGBoost) algorithm. SHapley Additive exPlanations (SHAP) were used to interpret the model and identify efficacy variance drivers across seven randomized controlled trials. Multilevel meta-regression demonstrated that event-free survival (EFS) benefit correlated positively with PD-L1 expression, peaking in the \(≥\) 50% subgroup (adjusted HR = 0.44, 95% CI: 0.33-−0.57). XGBoost-SHAP analysis revealed trial-level variance as the dominant driver of heterogeneity. Among subgroup-level clinical covariates, a non-linear U-shaped PD-L1 predictive pattern (<1% and \(≥\) 50%), squamous cell carcinoma (SCC) histology (HR = 0.53, 95% CI: 0.43-−0.65), and smoking history emerged as primary predictors. Synthesizing these signatures into a hypothesis-generating subgroup stratification framework indicated that SCC and PD-L1-negative (<1%) non-squamous cohorts benefit from continuous perioperative blockade. Conversely, exploratory analyses suggested that PD-L1-positive ( \(≥\) 1%) non-squamous tumors achieved maximal observed benefit from exclusively neoadjuvant regimens (HR = 0.50). Our results suggest that for PD-L1-positive non-squamous cases, the added benefit of extended adjuvant therapy may be limited. However, given our reliance on aggregate data and trial-level imbalances, these findings remain hypothesis-generating and should not alter current clinical practice. Rather, they offer an exploratory framework to inform patient selection for future de-escalation trials.