Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
Adjuvant nivolumab vs pembrolizumab in stage IIB/IIC melanoma: a reinforcement learning-based simulation study.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Perkin Perihan, Köş Fahriye Tuğba
Аннотация
BACKGROUND: Adjuvant programmed cell death protein 1 (PD-1) inhibitors (nivolumab, pembrolizumab) improve recurrence-free survival (RFS) in stage IIB-IIC melanoma, yet no head-to-head trial directly compares them. Traditional indirect methods estimate relative efficacy but often fail to integrate toxicity and patient-level trade-offs. Reinforcement learning (RL) provides a framework to simulate decision-making under uncertainty and competing clinical priorities.
METHODS: We developed an RL model treating each simulated patient as the environment, with state variables including age, ECOG status, stage, time-to-recurrence, and adverse event (AE) outcomes. Actions were treatment choices between nivolumab and pembrolizumab. Rewards combined gains in RFS (+ 1 per 2 months) with penalties for grade 3-4 AEs and discontinuations, incorporating both raw and placebo-adjusted AE rates. Q-learning was iterated across 1000 virtual trial episodes until policy convergence.
RESULTS: The RL-derived policies reflected conditional treatment preferences rather than a single optimal agent. In scenarios weighted toward tolerability, nivolumab was favored due to lower grade 3-4 AE and discontinuation rates. When incremental RFS gains were prioritized, pembrolizumab emerged as the preferred option. Placebo-adjusted versus raw AE modeling materially influenced the balance of preferences, underscoring the importance of attribution in comparative safety assessment.
CONCLUSION: Our RL framework complements existing comparative methods by making treatment trade-offs explicit and scenario-dependent. Rather than declaring a universal "best" PD-1 inhibitor, the model contextualizes efficacy-toxicity balances, supporting transparent decision-making in settings where small absolute differences may meaningfully influence patient and clinician preferences.
METHODS: We developed an RL model treating each simulated patient as the environment, with state variables including age, ECOG status, stage, time-to-recurrence, and adverse event (AE) outcomes. Actions were treatment choices between nivolumab and pembrolizumab. Rewards combined gains in RFS (+ 1 per 2 months) with penalties for grade 3-4 AEs and discontinuations, incorporating both raw and placebo-adjusted AE rates. Q-learning was iterated across 1000 virtual trial episodes until policy convergence.
RESULTS: The RL-derived policies reflected conditional treatment preferences rather than a single optimal agent. In scenarios weighted toward tolerability, nivolumab was favored due to lower grade 3-4 AE and discontinuation rates. When incremental RFS gains were prioritized, pembrolizumab emerged as the preferred option. Placebo-adjusted versus raw AE modeling materially influenced the balance of preferences, underscoring the importance of attribution in comparative safety assessment.
CONCLUSION: Our RL framework complements existing comparative methods by making treatment trade-offs explicit and scenario-dependent. Rather than declaring a universal "best" PD-1 inhibitor, the model contextualizes efficacy-toxicity balances, supporting transparent decision-making in settings where small absolute differences may meaningfully influence patient and clinician preferences.