A decision support-system for the mediastinal staging of non-small cell lung cancer

Lung cancer is a very frequent tumor in the developed world and the leading cause of cancer death, with non-small cell lung cancer being the most prevalent type and with most difficult prognosis. In this paper we present a decision support system built for finding the optimal selection of tests and therapy for each patient. The system basically consists of an influence diagram with super value nodes. The parameter λ, which in costeffectiveness analyses represents the amount of money that the decision maker is willing to pay to obtain a unit of effectiveness, has been included in the influence diagram, and has allowed us to find a trade-off between cost and effectiveness. Finally, given the uncertainty on the values of the parameters, we have assigned, with the expert’s help, a probability distribution to each parameter of the model and have performed a probabilistic sensitivity analysis.