Paper
1 April 1994 Indices of performance
Sean M. Hammond, Ian R. L. Davies
Author Affiliations +
Abstract
We explore the relative value of a number of commonly used performance indices in breast cancer screening by mammography. Such indices are used extensively in quality assurance evaluation of the screening services on offer and it is important that they provide an accurate measure of performance. The conditional probabilities, sensitivity and specificity are widely used as measures of performance. While these measures are necessary elements in performance evaluation, there is an interest in providing a single composite measure of general performance which remains stable over situations with differing incidence of abnormality. Such indices should be useful in the evaluation of performance of individual screeners as well as entire screening centers. In addition they will be of value in the assessment of training where the ratio of abnormal to normal mammograms is higher than in practice. Three classes of overall performance index are identified and discussed, probabilities, area under the ROC, and correlation. A Monte Carlo study is described in which 13 indices are compared on a large number of data sets with various criteria levels. Criteria used to evaluate these indices were as follows: ease of interpretation, ease of calculation, evenness of distribution, not influenced by differences in criteria. Two indices emerge with properties that make them particularly useful for mammography screening. These are the Pollack-Norman Area index and the point-symmetry adjusted phi (or G-Index).
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sean M. Hammond and Ian R. L. Davies "Indices of performance", Proc. SPIE 2166, Medical Imaging 1994: Image Perception, (1 April 1994); https://doi.org/10.1117/12.171738
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Cited by 2 scholarly publications.
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KEYWORDS
Mammography

Diagnostics

Monte Carlo methods

Breast cancer

Cancer

Binary data

Composites

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