1 Principles of Evidence-Based Imaging

Abstract

The standard medical education in Western medicine has emphasized skills and knowledge learned from experts, particularly those encountered in the course of postgraduate medical education, and through national publications and meetings. This reliance on experts, referred to by Dr. Paul Gerber of Dartmouth Medical School as “eminence-based medicine” (1), is based on the construct that the individual practitioner, particularly a specialist devoting extensive time to a given discipline, can arrive at the best approach to a problem through his or her experience. The practitioner builds up an experience base over years and digests information from national experts who have a greater base of experience due to their focus in a particular area. The evidence-based imaging (EBI) paradigm, in contradistinction, is based on the precept that a single practitioner cannot through experience alone arrive at an unbiased assessment of the best course of action. Assessment of appropriate medical care should instead be derived through evidence-based process. The role of the practitioner, then, is not simply to accept information from an expert, but rather to assimilate and critically assess the research evidence that exists in the literature to guide a clinical decision (2–4).

Appendices

IV. Take Home Appendix 1: Equations

Test result	Present	Outcome	Absent
Positive Negative	a (TP) c (FN)		b (FP) d (TN)
a.Sensitivity		a/(a  +  c)
b.Specificity		d/(b  +  d)
c.Prevalence		(a  +  c)/(a  +  b  +  c  +  d)
d.Accuracy		(a  +  d)/(a  +  b  +  c  +  d)
e.Positive predictive valuea		a/(a  +  b)
f.Negative predictive valuea		d/(c  +  d)
g.95% confidence interval (CI)		${\rm p} \pm \sqrt{\frac{\rm p(1-n)}{\rm n}}$ p  =  proportion n  =  number of subjects
h.Likelihood ratio		$\frac{\rm Sensitivity}{1-\rm specificity}=\frac{\rm a(b+d)}{\rm b(a+c)}$

1. ^a  Only correct if the prevalence of the outcome is estimated from a random sample or based on an a priori estimate of prevalence in the general population; otherwise, use of Bayes’ theorem must be used to calculate PPV and NPV. TP true positive; FP false positive; FN false negative; TN true negative.

V. Take Home Appendix 2: Summary of Bayes’ Theorem

• A. Information before test  ×  Information from test  =  Information after test

• B. Pretest probability (prevalence) sensitivity/ 1  −  specificity  =  posttest probability (predictive value)

• C. Information from the test also known as the likelihood ratio, described by the equation: sensitivity/1  −  specificity

• D. Examples 1 and 2 predictive values: The predictive values (posttest probability) change according to the differences in prevalence (pretest probability), although the diagnostic performance of the test (i.e., sensitivity and specificity) is unchanged. The following examples illustrate how the prevalence (pretest probability) can affect the predictive values (posttest probability) having the same information in two different study groups

Equations for calculating the results in the ­previous examples are listed in Appendix 1. As the prevalence of carotid artery disease increases from 0.16 (low) to 0.82 (high), the positive ­predictive value (PPV) of a positive contrast-enhanced CT increases from 0.67 to 0.98, respectively. The sensitivity and specificity remain unchanged at 0.83 and 0.92, respectively. These examples also illustrate that the diagnostic performance of the test (i.e., sensitivity and specificity) does not depend on the prevalence (pretest probability) of the disease. CTA, CT angiogram.

Example 1: Low prevalence of carotid artery disease
	Disease (carotid artery disease)	No disease (no carotid artery disease)	Total
Test positive (positive CTA)	20	10	30
Test negative (negative CTA)	4	120	124
Total	24	130	154

 

Example 2: High prevalence of carotid artery disease
	Disease (carotid artery disease)	No disease (no carotid artery disease)	Total
Test positive (positive CTA)	500	10	510
Test negative (negative CTA)	100	120	220
Total	600	130	730

1. Results: sensitivity  =  500/600  =  0.83; specificity  =  120/130  =  0.92; prevalence  =  600/730  =  0.82; positive predictive value  =  0.98; negative predictive value  =  0.55.