Both subjects with a low likelihood for coronary artery disease (CAD) and patients with normal findings on coronary angiography have been used as reference populations in non-invasive stress testing, including myocardial perfusion scintigraphy. Both of these criteria of normality--low likelihood of CAD and normal coronary angiography--have been criticised, and consensus on this issue is lacking. The aim of this study was to compare two different reference populations by testing the performance of artificial neural networks designed to interpret myocardial scintigrams. The networks were trained on myocardial perfusion scintigrams from 87 patients with angiographically documented CAD and on studies from one of two different reference groups: 48 patients with no signs of CAD based on angiography or 128 healthy volunteers with a likelihood for CAD <5%. The performance of the two different networks was then tested using scintigrams from a separate test group of 68 patients. Coronary angiography was used as the gold standard in this group. The network trained on patients with no signs of CAD based on angiography showed an area under the receiver operating characteristic (ROC) curve of 93%. The ROC area for the network trained on healthy volunteers was 72%, and this difference was statistically significant (P=0.03). The results of this study using artificial neural networks suggest that normal angiography should be preferred as the reference standard in myocardial scintigraphy when a patient is examined for CAD prior to possible angiography. Whether the same is true for other indications, e.g. in prognostic evaluation, is unknown.
Subject classification (UKÄ)
- Radiology, Nuclear Medicine and Medical Imaging
- Reference standards
- Neural network
- Myocardial single-photon emission tomography