Single-threshold-guided adaptive cancer therapy with partial-cycle treatment: A mechanistic and reinforcement learning analysis.

Adaptive cancer therapy seeks to modulate aggressive treatment to preserve drug-sensitive tumor cells that suppress resistant populations, but existing strategies often rely on frequent treatment decisions enabled by intensive surveillance, limiting clinical feasibility. Here, we propose a clinically motivated alternative that shortens the treatment window within a fixed and relatively long surveillance cycle, thereby avoiding the need for frequent monitoring. Based on this idea, we develop a mechanistic modeling framework for single-threshold-guided adaptive therapy with partial surveillance-cycle treatment (AT-PSC) and benchmark its performance using reinforcement learning. Using clinically calibrated parameters from an individual patient, simulations show that AT-PSC prolongs the time to progression (TTP) by 402 days compared with adaptive therapy using full surveillance-cycle treatment, while substantially reducing treatment exposure (dose reduced by 10.1%). Consequently, AT-PSC achieves significantly larger TTP gains than continuous therapy (1891 days) and two-threshold-guided adaptive therapy AT50 (1123 days). Simulations using data from six additional patients and sensitivity analyses further demonstrate that these benefits are robust across heterogeneous tumor growth profiles, while individual-based treatment should be considered to maximize TTP. Reinforcement learning yields comparable outcomes under the same fixed treatment window and can further extend TTP when the treatment window is adaptively adjusted. Together, these results support AT-PSC as a clinically feasible strategy to improve disease control while reducing treatment burden, and suggest that a practical regimen, such as a 14-day treatment window within a 30-day surveillance cycle, can provide sustained benefits for a broad patient population.