Sensitivity and specificity
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At the time the article was created Matt A. Morgan had no recorded disclosures.View Matt A. Morgan's current disclosures
At the time the article was last revised Francesco Sciacca had no recorded disclosures.View Francesco Sciacca's current disclosures
Sensitivity and specificity are fundamental characteristics of diagnostic imaging tests.
The two characteristics derive from a 2x2 box of basic, mutually exclusive outcomes from a diagnostic test:
- true positive (TP): an imaging test is positive and the patient has the disease/condition
- false positive (FP): an imaging test is positive and the patient does not have the disease/condition
- true negative (TN): an imaging test is negative and the patient does not have the disease/condition
- false negative (FN): an imaging test is negative and the patient has the disease/condition
On a first pass, we don't assume some relationship between the test and the disease/condition, but we hope there will be some relationship between the test and the disease/condition, because otherwise the test would be worthless.
For a given test and disease/condition, its sensitivity is how well it can be positive among all those with the condition. Therefore:
- sensitivity = TP / (TP + FN)
- true positives / (all those with the disease)
For a given test and disease/condition, its specificity is how well it can distinguish those with disease from those without. The test must not just fail to pick up a segment of the population (that might be poor sensitivity), it must distinguish those without the disease... the true negatives (TNs). Therefore:
- specificity = TN / (TN + FP)
- true negatives / (all those without the disease)
SpPin and SnNout rule
SnNout: if a diagnostic test, characterized by high sensitivity (Sn), returns the negative value (N), then it excludes the diagnosis (out) 2-3.
SpPin: if a diagnostic test, characterized by high specificity (Sp), returns the positive value (P), then it admits the diagnosis (in) 2-3.
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