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Detection and localization in test accuracy: a Bayesian perspective. (English) Zbl 1315.62081

Summary: In assessing the area under the ROC curve for the accuracy of a diagnostic test, it is imperative to detect and locate multiple abnormalities per image. This approach takes that into account by adopting a statistical model that allows for correlation between the reader scores of several regions of interest (ROI).
The ROI method of partitioning the image is taken. The readers give a score to each ROI in the image and the statistical model takes into account the correlation between the scores of the ROI’s of an image in estimating test accuracy. The test accuracy is given by \(\operatorname{Pr}[Y > Z] + (1/2)\operatorname{Pr}[Y = Z]\), where \(Y\) is an ordinal diagnostic measurement of an affected ROI, and \(Z\) is the diagnostic measurement of an unaffected ROI. This way of measuring test accuracy is equivalent to the area under the ROC curve. The parameters are the parameters of a multinomial distribution, then based on the multinomial distribution, a Bayesian method of inference is adopted for estimating the test accuracy.
Using a multinomial model for the test results, a Bayesian method based on the predictive distribution of future diagnostic scores is employed to find the test accuracy. By resampling from the posterior distribution of the model parameters, samples from the posterior distribution of test accuracy are also generated. Using these samples, the posterior mean, standard deviation, and credible intervals are calculated in order to estimate the area under the ROC curve. This approach is illustrated by estimating the area under the ROC curve for a study of the diagnostic accuracy of magnetic resonance angiography for diagnosis of arterial atherosclerotic stenosis. A generalization to multiple readers and/or modalities is proposed.
A Bayesian way to estimate test accuracy is easy to perform with standard software packages and has the advantage of employing the efficient inclusion of information from prior related imaging studies.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62F15 Bayesian inference
62F03 Parametric hypothesis testing
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[1] DOI: 10.1118/1.596358 · doi:10.1118/1.596358
[2] Congdon P., Bayesian Statistical Modeling (2001) · Zbl 0967.62019
[3] DOI: 10.1177/0272989X9801800412 · doi:10.1177/0272989X9801800412
[4] Masaryk A. M., Radiology 148 pp 839– (1991)
[5] Metz C. E., Radiology 121 pp 337– (1976)
[6] DOI: 10.2307/2533958 · Zbl 0904.62044 · doi:10.2307/2533958
[7] DOI: 10.1016/S1076-6332(00)80324-4 · doi:10.1016/S1076-6332(00)80324-4
[8] DOI: 10.1016/S1076-6332(03)80578-0 · doi:10.1016/S1076-6332(03)80578-0
[9] Pepe M. S., The Statistical Evaluation of Medical Tests for Classification and Prediction (2003) · Zbl 1039.62105
[10] DOI: 10.1177/0272989X9601600411 · doi:10.1177/0272989X9601600411
[11] Starr S. J., Radiology 116 pp 533– (1975)
[12] DOI: 10.1177/0272989X8800800309 · doi:10.1177/0272989X8800800309
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