IMRT treatment planning for prostate cancer using prioritized prescription optimization and mean-tail-dose functions

Abstract

Treatment planning for intensity modulated radiation therapy (IMRT) is challenging due to both the size of the computational problems (thousands of variables and constraints) and the multi-objective, imprecise nature of the goals. We apply hierarchical programming to IMRT treatment planning. In this formulation, treatment planning goals/objectives are ordered in an absolute hierarchy, and the problem is solved from the top-down such that more important goals are optimized in turn. After each objective is optimized, that objective function is converted into a constraint when optimizing lower-priority objectives. We also demonstrate the usefulness of a linear/quadratic formulation, including the use of mean-tail-dose (mean dose to the hottest fraction of a given structure), to facilitate computational efficiency. In contrast to the conventional use of dose-volume constraints (no more than x% volume of a structure should receive more than y dose), the mean-tail-dose formulation ensures convex feasibility spaces and convex objective functions. To widen the search space without seriously degrading higher priority goals, we allowed higher priority constraints to relax or 'slip' a clinically negligible amount during lower priority iterations. This method was developed and tuned for external beam prostate planning and subsequently tested using a suite of 10 patient datasets. In all cases, good dose distributions were generated without individual plan parameter adjustments. It was found that allowance for a small amount of 'slip,' especially in target dose homogeneity, often resulted in improved normal tissue dose burdens. Compared to the conventional IMRT treatment planning objective function formulation using a weighted linear sum of terms representing very different dosimetric goals, this method: (1) is completely automatic, requiring no user intervention, (2) ensures high-priority planning goals are not seriously degraded by lower-priority goals, and (3) ensures that lower priority, yet still important, normal tissue goals are separately pushed as far as possible without seriously impacting higher priority goals.

A Novel Reduced-Order Prioritized Optimization Method for Radiation Therapy Treatment Planning

“…As previous studies have shown, a small relaxation of a higher-priority objective can result in a large improvement of a lower-priority goal [12], [13]. The studies indicated that, in the reduced-order space, the slip factor could significantly improve the average dose at the OARs, without significantly compromising the dose homogeneity at the PTV.…”

“…have previously suggested using ‘mean tail dose’ rather than conventional dose-volume constraints [30], which are linear functions and hence, computationally attractive. ‘Mean tail dose’ refers to the mean dose of either the hottest or coldest specified fractional volume and has been successfully used in PO studies in the past [13]. However, one disadvantage of the ‘mean tail dose’ approach is the increase in problem size, which would result in extensive execution times.…”

“…For IMRT problems with planning goals that exhibit distinctive levels of importance or priority, previous studies have demonstrated that PO may provide an intuitive way of generating a solution [11]–[13]. However, PO has been criticized for not allowing a large gain on a low-priority goal in trading off a small loss on a high-priority goal [14].…”

A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

“…Güray and Güler (2013) proposed a hierarchical goal-programming (HGP) model to address scheduling problem of residents and the senior academic staff to outpatient clinics (OCs) in a physical medicine and rehabilitation (PMR) department. In the past significant amount of research have been conducted using MODM approaches to deal with optimizations regarding cancer treatment planning (Pugachev and Xing 2002;Clark et al 2008;Craft et al 2012;Bertsimas et al 2013;Chan et al 2014). Most of the articles related to optimization for cancer treatment focus on beam angles and IMRT features, while there is lack of enough articles on dose planning optimization and for prostate cancer specifically.…”

Use of a constrained hierarchical optimization dataset enhances knowledge‐based planning as a quality assurance tool for prostate bed irradiation

“…CHO is an automated planning process developed at Memorial Sloan Kettering Cancer Center. It is based on the optimization technique named hierarchical constrained optimization . CHO respects clinical priorities and produces Pareto optimal plans.…”