Treatment Optimization Using Physical and Radiobiological Objective Functions

Risk-adaptive optimization: Selective boosting of high-risk tumor subvolumes

“…Using this biological objective function, one maximizes the TCP values for the different tumor risk regions while at the same time minimizing the NTCP values for OAR. This was achieved by maximizing the biological objective function UTCP δ , which is given by the following expression: (1) where δ is the estimated fraction of patients for which tumor and normal tissue response are statistically independent (0.0 ≤ δ ≤ 1.0 ) [13]. For instance, given a value of 0.2 for the correlation-parameter, δ, dictates that for 20 % of patients tumor control and incidence of normal tissue complication are uncorrelated while the remaining 80% of patients may suffer a normal tissue complication in the attempt to achieve local tumor control.…”

“…The first limiting case assumes that each cure leads to a complication and therefore P + is obtained as the difference of TCP and NTCP. Hence, (10) In the second limiting case it is assumed that tumor control probability is independent of normal tissue complication probability, and P + in this case is also known as uncomplicated tumor control probability: (11) Using equations (5) and (7), the voxel-based UTCP 1 is given by: (12) A more general expression, UTCP δ , can be obtained by linear combination of these two limiting cases: (13) For small delta-values, UTCP δ can be interpreted as follows: a given absolute increase/decrease in the chance for local tumor control on one side will be balanced with a similar decrease/ increase in risk for complications on the other. A derivation of the gradient of UTCP 1 and UTCP δ can be found in the appendix.…”

“…In an effort to balance tumor control and normal tissue complications, Brahme [13] explored the use of biological inverse-treatment-plan-optimization. Biologically optimized treatment planning has the potential to strike the right balance between cure and complications.…”

Clinically Relevant Optimization Based on Simulated Annealing Algorithm in IMRT for Prostate Cancer

“…It is not only a tool to evaluate the treatment plan, but also the connection between the input parameters and the output dose distribution. Now Objective functions used in radiotherapy optimization are based either on physical factors or on biological formulations [1]. The former employs a physical quadratic dose-based objective function, which, via a combination of weighted terms, penalizes differentially violations of the various dose and/or dose-volume constraints specified with respect to the organs and the targets considered in the optimization process [2][3][4].…”

Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches

“…This paper is not intended to be a complete survey of the use of optimization techniques within treatment planning problems (see [29,36,38,50] for more complete overviews of conformal radiation therapy, for example). In addition to these survey articles, there are a variety of other approaches (for which we cite representative papers) including those based on optimal control primitives [1], or simulated annealing [27,32,43,44], iterative (relaxation) techniques [10], approaches using biological objectives [7,24,33] techniques of multi-objective [22] and neuro-dynamic programming [20]. In this paper we specifically outline three particular problem areas that arise in treatment planning and highlight the discrete nature of some of the decisions that need to be made.…”