High-Dose-Rate brachytherapy is a critical component in the treatment of locally advanced cervical cancer. While automated treatment planning systems, such as BRIGHT, have demonstrated the ability to generate high-quality plans, their clinical adoption is hindered by the complexity of their configuration. Deploying such a system in a new hospital requires the manual definition of a clinical protocol that accurately reflects the local institution’s specific standard of care. This "cold start" problem is time-consuming for both doctors and researchers.

This thesis proposes a novel framework for the Automated Discovery of Clinical Protocols. By formulating the protocol configuration as a bi-level optimization problem, we employ the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm to autonomously extract implicit expert knowledge from a repository of historical clinical plans. The system evolves a set of protocol parameters that, when fed into BRIGHT, reproduce radiation dose distributions as preferred by human experts.

We validate this approach using anonymized patient data from Virginia Commonwealth University. Through a series of experiments with incrementally increasing complexity, ranging from optimizing simple dose thresholds to evolving the definitions of dosimetric metrics, we demonstrate that the proposed framework can successfully identify protocols that generate treatment plans that are quantitatively similar to the clinical ground truth. This research serves as a proof-of-concept, offering a pathway to rapidly deploy automated planning systems while ensuring alignment with local clinical expertise.