Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning

Abstract

Inverse planning in external beam radiotherapy often requires a scalar objective function that incorporates importance factors to mimic the planner's preferences between conflicting objectives. Defining those importance factors is not straightforward, and frequently leads to an iterative process in which the importance factors become variables of the optimization problem. In order to avoid this drawback of inverse planning, optimization using algorithms more suited to multiobjective optimization, such as evolutionary algorithms, has been suggested. However, much inverse planning software, including one based on simulated annealing developed at our institution, does not include multiobjective-oriented algorithms. This work investigates the performance of a modified simulated annealing algorithm used to drive aperture-based intensity-modulated radiotherapy inverse planning software in a multiobjective optimization framework. For a few test cases involving gastric cancer patients, the use of this new algorithm leads to an increase in optimization speed of a little more than a factor of 2 over a conventional simulated annealing algorithm, while giving a close approximation of the solutions produced by a standard simulated annealing. A simple graphical user interface designed to facilitate the decision-making process that follows an optimization is also presented.

Accelerated 3D Monte Carlo light dosimetry using a graphics processing unit (GPU) cluster

“…Simulated annealing is used as an optimization technique for radiation treatment planning in the clinical setting, with successes reported in both external beam radiation therapy (EBRT) 3,4 and high dose-rate (HDR) brachytherapy. 5,6 For EBRT, the basic optimization problem is the determination of the appropriate temporal and spatial arrangements of multiple external radiation beams, which can be tailored in highly sophisticated and precise ways in modern 3-D conformal radiation therapy.…”

Interactive visual guidance for automated stereotactic radiosurgery treatment planning

“…Lotov et al (2004), Bortz et al (2014) and Korhonen and Wallenius (1988) all discuss the design of Pareto front based interfaces, but do not deal with radiation therapy, while Craft, Halabi, Shih, and Bortfeld (2006) and Wang, Jin, Zhao, Peng, and Hu (2014) provide an analysis of Pareto tradeoffs in radiation therapy planning, but do not include an interface. Rosen, Liu, Childress, and Liao (2005), Ehrgott and Winz (2008) and Aubry, Beaulieu, Sévigny, Beaulieu, and Tremblay (2006), on the other hand, do use Pareto-optimality to generate radiation therapy interfaces. Rosen et al (2005) introduce TPEx, a simplified dose volume histogram-based interface which allows experts to navigate through a number of allowable plans.…”

“…The navigation, however, is performed strictly in terms of dose and volume properties and ultimately, the final plan is generated using a non-deterministic algorithm, leading to potential inconsistencies for the end user. Ehrgott and Winz (2008) and Aubry et al (2006) both provide simpler interfaces, with basic filtering functionality for plan selection.…”

A self‐adaptive case‐based reasoning system for dose planning in prostate cancer radiotherapy

“…Aubry et al 38 proposed a modified Simulated Annealing algorithm to solve the radiotherapy dose planning problem. First, Pareto set of all nondominated solutions was generated and ranked by each objective.…”