Application of influence diagrams to prostate intensity-modulated radiation therapy plan selection

Abstract

The purpose is to incorporate clinically relevant factors such as patient-specific and dosimetric information as well as data from clinical trials in the decision-making process for the selection of prostate intensity-modulated radiation therapy (IMRT) plans. The approach is to incorporate the decision theoretic concept of an influence diagram into the solution of the multiobjective optimization inverse planning problem. A set of candidate IMRT plans was obtained by varying the importance factors for the planning target volume (PTV) and the organ-at-risk (OAR) in combination with simulated annealing to explore a large part of the solution space. The Pareto set for the PTV and OAR was analysed to demonstrate how the selection of the weighting factors influenced which part of the solution space was explored. An influence diagram based on a Bayesian network with 18 nodes was designed to model the decision process for plan selection. The model possessed nodes for clinical laboratory results, tumour grading, staging information, patient-specific information, dosimetric information, complications and survival statistics from clinical studies. A utility node was utilized for the decision-making process. The influence diagram successfully ranked the plans based on the available information. Sensitivity analyses were used to judge the reasonableness of the diagram and the results. In conclusion, influence diagrams lend themselves well to modelling the decision processes for IMRT plan selection. They provide an excellent means to incorporate the probabilistic nature of data and beliefs into one model. They also provide a means for introducing evidence-based medicine, in the form of results of clinical trials, into the decision-making process.

Reduced-order parameter optimization for simplifying prostate IMRT planning

“…Furthermore, given a set of solutions, the problem of choosing the "best" one from a clinical and patientspecific perspective still remains (Amols and Ling, 2002). A decision-making process that incorporates clinical reasoning was introduced by Meyer et al (Meyer et al, 2004), but it only works when the trade-offs between different plans are very large. Another natural approach is to cast IMRT as a constrained minimization problem (see, e.g., (Romeijn et al, 2003, Shepard et al, 1999), but this is still not the clinical standard, since efficiently solving such problems with DVH-based constraints is very difficult.…”

Role of Positron Emission Tomography in the Treatment of Occult Disease in Head-and-Neck Cancer: A Modeling Approach

“…An influence diagram is a Bayesian network with additional nodes representing decisions and utilities [7, 8, 9, 10]. Decision trees can represent the same model, but the complexity of the graph renders any but the most simple nearly intractable to use.…”

A ‘learning-by-doing’ treatment planning tutorial for medical physicists

“…For instance, Brachytherapy planning 11 is a feature in the standard Prism release, not yet included in the tutorial. Also, additional Prism components such as electron beam planning 12 , a convolution/superposition dose algorithm 29 and intensity-modulated radiation therapy (IMRT) 29,30 are not included in the standard Prism release, but exist as research tools.…”