The Society of Radiologists in Ultrasound convened a panel of specialists from a variety of medical disciplines to come to a consensus on the management of thyroid nodules identified with thyroid ultrasonography (US), with particular focus on which nodules should be subjected to US-guided fine needle aspiration and which thyroid nodules need not be subjected to fine-needle aspiration. The panel met in Washington, DC, October 26-27, 2004, and created this consensus statement. The recommendations in this consensus statement, which are based on analysis of the current literature and common practice strategies, are thought to represent a reasonable approach to thyroid nodular disease.

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In a patient with one or more thyroid nodules larger than 10 mm in diameter, the likelihood of thyroid cancer per patient is independent of the number of nodules, whereas the likelihood per nodule decreases as the number of nodules increases. For exclusion of cancer in a thyroid with multiple nodules larger than 10 mm, up to four nodules should be considered for FNA. Sonographic characteristics can be used to prioritize nodules for FNA based on their individual risk of cancer.

The data for medical decision analyses are often unreliable. Traditional sensitivity analysis--varying one or more probability or utility estimates from baseline values to see if the optimal strategy changes--is cumbersome if more than two values are allowed to vary concurrently. This paper describes a practical method for probabilistic sensitivity analysis, in which uncertainties in all values are considered simultaneously. The uncertainty in each probability and utility is assumed to possess a probability distribution. For ease of application we have used a parametric model that permits each distribution to be specified by two values: the baseline estimate and a bound (upper or lower) of the 95 percent confidence interval. Following multiple simulations of the decision tree in which each probability and utility is randomly assigned a value within its distribution, the following results are recorded: (a) the mean and standard deviation of the expected utility of each strategy; (b) the frequency with which each strategy is optimal; (c) the frequency with which each strategy "buys" or "costs" a specified amount of utility relative to the remaining strategies. As illustrated by an application to a previously published decision analysis, this technique is easy to use and can be a valuable addition to the armamentarium of the decision analyst.