Mediastinoscopy and bronchial carcinoma: experience with 600 mediastinoscopies.

Lk, Lacquet; Mertens, A; Kleef, J. van; Jongerius, C. M.

doi:10.1136/thx.30.2.141

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…Although false-positive biopsies are probably rare, they do occur. 10 Third, some investigators consider the results of mediastinoscopy to be a falsenegative result only if a metastasis was missed in an area accessible during mediastinoscopy, whereas others consider any unfound mediastinal metastases, regardless of location, to constitute a false-negative result. This inconsistency results in varying estimates of sensitivity.…”

Section: Test Performance Datamentioning

confidence: 99%

Strategies for Test Selection in the Staging of Lung Neoplasms

Owens¹,

Sox²,

Inouye³

1989

Semin Respir Crit Care Med

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Although several strategies have been proposed for the staging of lung neoplasms, there is still considerable disagreement as to the appropriate use of mediastinoscopy, computed tomography (CT), standard tomography (ST), and magnetic resonance imaging (MRI). We believe the disagreement is in part related to two problems. First, data on test performance have been inaccurate, inconsistent, or unavailable. Second, the implications of pretest probability and test performance on post-test probability have not been explicitly considered when test selection recommendations have been formulated.In this article we first discuss general concepts of test interpretation derived from Bayes' theorem. We then review test performance data for CT, ST, and mediastinoscopy and present general guidelines for test selection that can be applied to any proposed strategy for lung cancer staging. IMPORTANT CONCEPTUAL LESSONS FROM BAYES' THEOREMOf the possible tests one can consider for staging or diagnosis of lung cancer, all are imperfect. That is, both false-positive and false-negative test results occur. Even mediastinoscopy has a significant falsenegative rate. 1-4 Thus, one cannot know the patient's true state with certainty; intelligent test interpretation requires the use of probability.Bayes' theorem is a straightforward algebraic expression that allows calculation of the post-test probability of disease if the pretest probability, test sensitivity and specificity are known (see Appendix). A number of important lessons follow from an understanding of the rule:1. The pretest probability of disease has a dramatic effect on the post-test probability. Figure 1 illustrates the post-test probability of mediastinal metastases after a positive CT scan. In panel A, the pretest probability is 0.1, and the sensitivity and specificity are 0.74 and 0.79, respectively. 5 Recall that the sensitivity of a test represents the probability that a diseased patient will have a positive test; specificity denotes the probability that a nondiseased patient will have a negative test. The posttest probability, calculated with Bayes' theorem, is 0.28, as shown in the figure. In panel B, the calculation is repeated using identical sensitivity and specificity data, but with a pretest probability of 0.5. Note the post-test probability is now 0.78, considerably higher than before. In each case, the CT scan is "positive," but the post-test probability of disease is very different in the two situations. This example illustrates the importance of pretest probability: rational test interpretation is virtually impossible if one cannot estimate, at least roughly, the pretest probability. 2. The sensitivity (also called the true-positive rate) of a test primarily affects the interpretation of a negative result. In Figure 2 panel A shows the post-test probability of disease after positive and negative CT scans, calculated for a pretest probability of 0.50. The sensitivity and specificity are each 0.75. Sensitivity is defined as the probability that a dis-

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Section: Test Performance Datamentioning

confidence: 99%