Abstract
Background Pre-operative assessment of non-small cell lung cancer (NSCLC) is a major application of positron emission tomography (FDG-PET). Despite substantial evidence of diagnostic accuracy, relatively little attention has been paid to its effects on patient outcomes. This paper addresses this by extending an existing decision model to include patient-elicited utilities. Patients and methods A decision-tree model of the effect of FDG-PET on pre-operative staging was converted to a Markov model. Utilities for futile and appropriate thoracotomy were elicited from 75 patients undergoing staging investigation for NSCLC. The decision model was then used to estimate the expected value of perfect information (EVPI) associated with three sources of uncertainty—the accuracy of PET, the accuracy of CT and the patient related utility of a futile thoracotomy. Results The model confirmed the apparent cost-effectiveness of FDG-PET and indicated that the EVPI associated with the utility of futile thoracotomy considerably exceeds that associated with measures of accuracy. Conclusion The study highlights the importance of patient related utilities in assessing the cost-effectiveness of diagnostic technologies. In the specific case of PET for pre-operative staging of NSCLC, future research effort should focus on such elicitation, rather than further refinement of accuracy estimates.
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