Single-threshold–guided adaptive cancer therapy with partial-cycle treatment: A mechanistic and reinforcement learning analysis (opens in new tab)

Author summary This study presents an alternative approach to adaptive cancer therapy using partial surveillance-cycle treatment (AT-PSC), aimed at reducing the need for frequent monitoring while prolonging the time to progression (TTP) and decreasing treatment exposure. The proposed framework incorporates a single-threshold-guided strategy and is validated through reinforcement learning. Simulations using clinically calibrated parameters show that AT-PSC can extend TTP by 402 days compared t...