In summary, the ROC curve has found many useful applications in radiology. While the statistics and mathematics behind ROC analysis can be complex, the ROC curve is fundamentally just a plot of the trade-off between sensitivity and specificity. In fact, many of the assumptions of ROC analysis (binary classification, reliance on a reference standard) are the same as those necessary to calculate sensitivity and specificity. An indication of an observer's degree of diagnostic certainty is the key additional data element that must be collected to calculate an ROC curve. Ongoing developments in ROC analysis will address more complex types of diagnostic situations and will likely expand the applicability of ROC analysis.
|Original language||English (US)|
|Number of pages||8|
|State||Published - Jul 2005|
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging