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.

Optimal choice of beam energy in radiation therapy is easy in many well‐documented cases, but less obvious in some others. Low‐energy beams may provide better conformity around the target than their high‐energy counterparts due to reduced lateral scatter, but they also contribute to overdosage of peripheral normal tissue. Beam energy was added as an optimization parameter in an automatic aperture‐based inverse planning system. We have investigated a total of six cases for two sites (prostate and lung), representative of deep‐seated and moderately deep‐seated tumors. For one case for each site, different numbers of beam incidences were considered. The other cases for each site were optimized using a fixed number of incidences. Four types of plans were optimized: 6 MV, 23 MV, and mixed energy plans with one or two energies per incidence. Each plan was scored with a dose‐volume cost function. Cost function values, number of segments, monitor units, dose‐volume parameters, and isodose distributions were compared. For the prostate and lung cases, energy mixing improved plans in terms of cost function values, with a more important reduction for a small number of beam incidences. Use of high energy allowed better peripheral tissue sparing, while keeping similar target coverage and sensitive structures avoidance. Low energy contribution to monitor units usually increased with the number of beam incidences. Thus, for deep‐seated and moderately deep‐seated tumors, energy optimization can produce interesting plans with less peripheral dose and monitor units than for low energy alone.PACS numbers: 87.55.de, 87.55.dk, 87.56.N‐

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