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Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative \u201ccost\u201d of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a \u201cbudget\u201d). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such \u201cthreshold-aware\u201d optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new \u201cthreshold-aware\u201d policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012165","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T13:37:56Z","timestamp":1718372276000},"page":"e1012165","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":9,"title":["Threshold-awareness in adaptive cancer therapy"],"prefix":"10.1371","volume":"20","author":[{"given":"MingYi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob 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