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
(2025) Lung Cancer — Vol. 209, p. 108799 (2025)
Files
LungCancer-Cortellini2025.pdf
Open Access - Adobe PDF
- 3.68 MB
Details
- Authors
Cortellini, Alessio 
Garbo, Edoardo Citarella, Fabrizio Guarrasi, Valerio
- Abstract
- 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.
- Affiliations
Citations
Cortellini, A., Garbo, E., La Cava, G., Citarella, F., Santo, V., Brunetti, L., Pinato, D. J., Naidoo, J., Loza, M., Genova, C., Gettinger, S., Kim, S. Y., Jayakrishnan, R., Zarif, T. E., Russano, M., Pecci, F., Di Federico, A., Montrone, M., Owen, D. H., et al. (2025). 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. Lung Cancer, 209, 108799. https://doi.org/10.1016/j.lungcan.2025.108799 (Original work published 2025)