This study demonstrated the feasibility of a fully automated, prescription-free VMAT planning framework for primary prostate cancer, indicating its potential for future clinical implementation. The proposed framework directly optimized treatment plans in radiobiological objective space, producing Pareto-optimal, clinically deliverable solutions using predefined TCP and NTCP levels. It enables patient-specific trade-off analysis taking into account tumor control and normal tissue complication risk. The work provides a foundation for further development, including the incorporation of geometric uncertainties, acceleration through parallel or GPU-based computation, and application to additional tumor sites.
Kuhn et al. (Sun,) studied this question.
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