SU-E-T-221: Evaluation of Technology Using Probabilistic Decision Models

Medical physicists are often asked to evaluate or choose appropriatetechnology for clinical applications. These are multidimensionalproblems that also suffer from different degrees of uncertainty in thevariables. Probabilistic decision models are a robust andmathematically correct means of handling these issues. The principlesof constructing such models are presented along with practicalexamples in the areas of IGRT, IMRT and proton therapy.

Influence diagrams are used to model the variables and theiruncertainties and include action and reward variables. Influencediagrams are directed acyclic graphs that use Bayesian probabilitycalculus to propagate probabilities and to update prior probabilitiesin the presence of evidence. An influence diagram was used to modelthe question of whether brain tumors are better treated with x-rayIMRT or proton therapy, with or without CT-guided localization. Datafor the conditional probabilities of the model were obtained from theliterature and included models of TCP, NTCP and induction of secondmalignancies, as well as data on the probability density functions forinterfraction patient motion. Dosimetric data were obtained using theCMS treatment planning system.

Several different tumor types and sites were studied. The critical variables in the model were identified andstudied using analyses of evidence, parameters and value ofinformation. The impact of imaging was significant, regardless of theradiation type. The models used in determining some of theconditional probabilities parameters also played an important role inranking alternatives.

Although such choices are difficult, physicists mustproceed with the best data at hand. Without a rigorous framework onwhich to build a model of the process, decisions are likely to be based onunstated assumptions and incorrect inference. The example ofcomparing irradiation modalities for brain tumors shows the power ofinfluence diagrams in this critical context.