SU‐E‐T‐537: Local Multi‐Criteria Optimization for Clinical Tradeoff Decision Guidance in RT Planning

Abstract

Purpose:We present a novel method for generating a local segment of Pareto surface around the best achievable plan predicted by a knowledge model. This local‐MCO will provide an efficient method to enable clinically viable tradeoff decisions in IMRT planning tailored to the patient's specific needs.Methods:Multi‐criteria optimization (MCO) methods provide physicians and planners the ability to explore tradeoff options in RT planning. However, generic MCO methods are often time consuming because a global Pareto surface (PS) need to be explored if patient‐specific clinical conditions and planners’ planning experiences are not taken into consideration. We have developed a local‐MCO approach, which incorporates the knowledge model prediction based on the individual patient's features and planning experience into the generation of a local PS around a predicted plan. In the proposed method, the starting points of clinical relevant organ sparing objectives are predicted by the knowledge models, and then a local PS is searched near the model predictions. As an initial assessment, the local PS is compared with the global PS generated for two prostate cancer cases. The mean minimum distance from each plan on the local PS to the global PS and the range of clinical acceptable dosimetric parameters covered by the local and global PS are calculated.Results:The local PS agrees well with the global PS. The mean minimum distance between the local and global PS in the PTV‐bladder‐rectum dose objective space are about 3% and 1% of prescription dose for the two plans, respectively. Although the local PS is only a small portion of the global PS, they cover most of the clinically relevant dose range.Conclusion:The local MCO results in a smaller but clinically more relevant PS. It is an efficient method to provide physicians with guidance of patient‐specific trade‐off options based on practice experience.Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical Systems.