Long-term outcomes from pembrolizumab monotherapy in patients with advanced NSCLC, PD-L1 expression ≥ 50 %, and poor performance status: Transformer-based AI to characterize prognostic complexity

Background: The use of first-line single agent immunotherapy in patients with advanced NSCLC and ECOG PS ≥ 2 remains controversial, as this frail population has been largely excluded from pivotal clinical trials. Real-world evidence suggests that although median survival is poor, a subset of these patients may achieve long-term benefit. Methods: We analyzed data from the Pembro-Real 5Y registry, a global real-world dataset with > 5 years follow-up. The cohort included patients with advanced NSCLC, PD-L1 TPS ≥ 50 %, treated with first line pembrolizumab outside of clinical trials. Univariable analyses were conducted to identify descriptive characteristics associated with survival. To address the complexity of long-term outcome prediction, we integrated Elastic Net regression and a transformer-based AI model (NAIM). The Elastic Net model was employed to mitigate collinearity and select relevant prognostic factors, while NAIM was used to explore non-linear, time-dependent interactions between variables. Endpoints included overall survival (OS) and 5-year survival rates. Results: Out of 1050 patients, 161 patients with ECOG PS ≥ 2 were included, showing a median OS of 5.4 months (95 % CI: 3.8–7.8), and a 5-year survival rate of 13.0 % (95 % CI: 8.1–19.9). Univariable analysis indicated that no single baseline variable was strongly predictive of 5-year survival, except for TMB, KRAS, and BRAF status, which were significantly limited by missingness. Elastic Net identified only two significant predictors of 5-year survival: high TMB (with unstable confidence intervals) and KRAS mutation. NAIM provided a dynamic perspective, confirming that bone metastases and baseline corticosteroid use were strong predictors of early mortality, whereas BMI increase and systemic health markers/host factors (e.g., hypertension and dyslipidemia) gained importance in long-term survivors. However, NAIM exhibited a notable performance drop from training to validation suggesting overfitting and the challenge of modeling long-term outcomes using baseline static variables. Conclusions: Despite the overall poor prognosis, a subset of patients with ECOG PS ≥ 2 achieves long-term survival with pembrolizumab monotherapy, indicating that performance status alone should not preclude treatment in all cases. Our analysis highlights the limitations of traditional statistical approaches and AI-driven models in predicting long-term benefit in this heterogeneous population. Future efforts should focus on refining hybrid modeling strategies and incorporating prospective validation to better identify those who may benefit from immunotherapy beyond short-term expectations.