Background and purpose: A tumor subvolume-based, risk-adaptive optimization strategy is presented.
Methods and materials: Risk-adaptive optimization employs a biologic objective function instead of an objective function based on physical dose constraints. Using this biologic objective function, tumor control probability (TCP) is maximized for different tumor risk regions while at the same time minimizing normal tissue complication probability (NTCP) for organs at risk. The feasibility of risk-adaptive optimization was investigated for a variety of tumor subvolume geometries, risk-levels, and slopes of the TCP curve. Furthermore, the impact of a correlation parameter, delta, between TCP and NTCP on risk-adaptive optimization was investigated.
Results: Employing risk-adaptive optimization, it is possible in a prostate cancer model to increase the equivalent uniform dose (EUD) by up to 35.4 Gy in tumor subvolumes having the highest risk classification without increasing predicted normal tissue complications in organs at risk. For all tumor subvolume geometries investigated, we found that the EUD to high-risk tumor subvolumes could be increased significantly without increasing normal tissue complications above those expected from a treatment plan aiming for uniform dose coverage of the planning target volume. We furthermore found that the tumor subvolume with the highest risk classification had the largest influence on the design of the risk-adaptive dose distribution. The parameter delta had little effect on risk-adaptive optimization. However, the clinical parameters D(50) and gamma(50) that represent the risk classification of tumor subvolumes had the largest impact on risk-adaptive optimization.
Conclusions: On the whole, risk-adaptive optimization yields heterogeneous dose distributions that match the risk level distribution of different subvolumes within the tumor volume.