Probability in radiology.
In this article, a summary of the basic rules of probability using examples of their application in radiology is presented. Those rules describe how probabilities may be combined to obtain the chance of "success" with either of two diagnostic or therapeutic procedures or with both. They define independence and relate it to the conditional probability. They describe the relationship (Bayes rule) between sensitivity, specificity, and prevalence on the one hand and the positive and negative predictive values on the other. Finally, the two distributions most commonly encountered in statistical models of radiologic data are presented: the binomial and normal distributions.
Halpern
Elkan F
EF
DATA Group, Department of Radiology, Massachusetts General Hospital, Zero Emerson Pl, Suite 2H, Boston, MA 02114, USA. elk@the-data-group.org
Gazelle
G Scott
GS
D001499:Bayes Theorem; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011336:Probability; D011871:Radiology