Linear Energy Transfer-Guided Optimization in Intensity Modulated Proton Therapy: Feasibility Study and Clinical Potential

Purpose: To investigate the feasibility and potential clinical benefit of linear energy transfer (LET) guided plan optimization in intensity modulated proton therapy (IMPT). Methods and Materials: A multicriteria optimization (MCO) module was used to generate a series of Pareto-optimal IMPT base plans (BPs), corresponding to defined objectives, for 5 patients with head-and-neck cancer and 2 with pancreatic cancer. A Monte Carlo platform was used to calculate dose and LET distributions for each BP. A custom-designed MCO navigation module allowed the user to interpolate between BPs to produce deliverable Pareto-optimal solutions. Differences among the BPs were evaluated for each patient, based on dose-volume and LET-volume histograms and 3-dimensional distributions. An LET-based relative biological effectiveness (RBE) model was used to evaluate the potential clinical benefit when navigating the space of Pareto-optimal BPs. Results: The mean LET values for the target varied up to 30% among the BPs for the head-and-neck patients and up to 14% for the pancreatic cancer patients. Variations were more prominent in organs at risk (OARs), where mean LET values differed by a factor of up to 2 among the BPs for the same patient. An inverse relation between dose and LET distributions for the OARs was typically observed. Accounting for LET-dependent variable RBE values, a potential improvement on RBE-weighted dose of up to 40%, averaged over several structures under study, was noticed during MCO navigation. Conclusions: We present a novel strategy for optimizing proton therapy to maximize dose-averaged LET in tumor targets while simultaneously minimizing dose-averaged LET in normal tissue structures. MCO BPs show substantial LET variations, leading to potentially significant differences in RBE-weighted doses. Pareto-surface navigation, using both dose and LET distributions for guidance, provides the means for evaluating a large variety of deliverable plans and aids in identifying the clinically optimal solution. (C) 2013 Elsevier Inc.