7 research outputs found
When is Better Best? A multiobjective perspective
Purpose: To identify the most informative methods for reporting results of
treatment planning comparisons.
Methods: Seven papers from the past year of International Journal of
Radiation Oncology Biology Physics reported on comparisons of treatment plans
for IMRT and IMAT. The papers were reviewed to identify methods of comparisons.
Decision theoretical concepts were used to evaluate the study methods and
highlight those that provide the most information.
Results: None of the studies examined the correlation between objectives.
Statistical comparisons provided some information but not enough to make
provide support for a robust decision analysis.
Conclusion: The increased use of treatment planning studies to evaluate
different methods in radiation therapy requires improved standards for
designing the studies and reporting the results
A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT
Purpose: Current inverse planning methods for IMRT are limited because they
are not designed to explore the trade-offs between the competing objectives
between the tumor and normal tissues. Our goal was to develop an efficient
multiobjective optimization algorithm that was flexible enough to handle any
form of objective function and that resulted in a set of Pareto optimal plans.
Methods: We developed a hierarchical evolutionary multiobjective algorithm
designed to quickly generate a diverse Pareto optimal set of IMRT plans that
meet all clinical constraints and reflect the trade-offs in the plans. The top
level of the hierarchical algorithm is a multiobjective evolutionary algorithm
(MOEA). The genes of the individuals generated in the MOEA are the parameters
that define the penalty function minimized during an accelerated deterministic
IMRT optimization that represents the bottom level of the hierarchy. The MOEA
incorporates clinical criteria to restrict the search space through protocol
objectives and then uses Pareto optimality among the fitness objectives to
select individuals.
Results: Acceleration techniques implemented on both levels of the
hierarchical algorithm resulted in short, practical runtimes for optimizations.
The MOEA improvements were evaluated for example prostate cases with one target
and two OARs. The modified MOEA dominated 11.3% of plans using a standard
genetic algorithm package. By implementing domination advantage and protocol
objectives, small diverse populations of clinically acceptable plans that were
only dominated 0.2% by the Pareto front could be generated in a fraction of an
hour.
Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that
meet all dosimetric protocol criteria in a feasible amount of time. It
optimizes not only beamlet intensities but also objective function parameters
on a patient-specific basis
Investigation of effective decision criteria for multiobjective optimization in IMRT
Purpose: To investigate how using different sets of decision criteria impacts the quality of intensity modulated radiation therapy (IMRT) plans obtained by multiobjective optimization