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The Quantitative Science of Evaluating Imaging Evidence
Author(s) -
Tessa S. S. Genders,
Bart S. Ferket,
M. G. Myriam Hunink
Publication year - 2017
Publication title -
jacc. cardiovascular imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.79
H-Index - 120
eISSN - 1936-878X
pISSN - 1876-7591
DOI - 10.1016/j.jcmg.2016.12.010
Subject(s) - gold standard (test) , diagnostic test , pre and post test probability , test (biology) , bayesian probability , medical imaging , medicine , medical physics , computer science , radiology , artificial intelligence , emergency medicine , paleontology , biology
Cardiovascular diagnostic imaging tests are increasingly used in everyday clinical practice, but are often imperfect, just like any other diagnostic test. The performance of a cardiovascular diagnostic imaging test is usually expressed in terms of sensitivity and specificity compared with the reference standard (gold standard) for diagnosing the disease. However, evidence-based application of a diagnostic test also requires knowledge about the pre-test probability of disease, the benefit of making a correct diagnosis, the harm caused by false-positive imaging test results, and potential adverse effects of performing the test itself. To assist in clinical decision making regarding appropriate use of cardiovascular diagnostic imaging tests, we reviewed quantitative concepts related to diagnostic performance (e.g., sensitivity, specificity, predictive values, likelihood ratios), as well as possible biases and solutions in diagnostic performance studies, Bayesian principles, and the threshold approach to decision making.

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