Introduction: Biomarker-driven strategies are central to personalized oncology, yet local treatments such as radiotherapy still lack validated stratification frameworks. Conventional frequentist trial designs often require prohibitively large patient cohorts without incorporating previously validated data. We therefore propose a Bayesian trial framework, which utilizes information of a patient’s biomarker and historical information of the biomarker effect to optimize the assigned dose. The aim of this work is to develop and illustrate such a framework for radiotherapy in rectal cancer organ preservation.

Methods: We designed a prospective two-arm Bayesian response-adaptive trial concept incorporating the principle of optimal stopping. Biomarker measurements obtained during treatment guided dose adaptation, illustrated here using the imaging-derived early regression index (ERI). Feasibility and statistical performance were evaluated through a fully simulated trial in rectal cancer organ preservation. Sensitivity analysis was applied to investigate the effect of prior information on the posterior distribution.

Results: Simulations modeled the patients’ response using biomarker-dependent tumor control and biomarker-independent toxicity curves. One subgroup of patients would benefit most from moderate dose escalation, achieving improved tumor control without excessive toxicity. In contrast, poor and excellent responders gained limited additional benefit at clinically acceptable doses. Under the optimistic prior scenario, the proposed design could claim the benefit of the adaptive dose with fewer than 100 simulated patients, while with weakly informative priors less than 150 patients are needed.

Conclusion: This Bayesian response-adaptive design provides a quantitative framework to integrate biomarkers such as ERI into radiotherapy personalization. It enables a structured evaluation of dose–response relationships and may help facilitate the translation of biomarker findings into local cancer therapy, acknowledging the underlying model assumptions.