Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm.
Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm.
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DOI：
10.2967/jnumed.112.111542
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发表时间：
2013-04
期刊：
影响因子：
--
通讯作者：
Slomka P
中科院分区：
文献类型：
--
作者：
Arsanjani R;Xu Y;Dey D;Fish M;Dorbala S;Hayes S;Berman D;Germano G;Slomka P
We aimed to improve the diagnostic accuracy of automatic myocardial perfusion SPECT (MPS) interpretation analysis for prediction of coronary artery disease (CAD) by integrating several quantitative perfusion and functional variables for non-corrected (NC) data by support vector machines (SVM), a computer method for machine learning. 957 rest/stress 99mtechnetium gated MPS NC studies from 623 consecutive patients with correlating invasive coronary angiography and 334 with low likelihood of CAD (LLK < 5% ) were assessed. Patients with stenosis ≥ 50% in left main or ≥ 70% in all other vessels were considered abnormal. Total perfusion deficit (TPD) was computed automatically. In addition, ischemic changes (ISCH) and ejection fraction changes (EFC) between stress and rest were derived by quantitative software. The SVM was trained using a group of 125 pts (25 LLK, 25 0-, 25 1-, 25 2- and 25 3-vessel CAD) using above quantitative variables and second order polynomial fitting. The remaining patients (N = 832) were categorized based on probability estimates, with CAD defined as (probability estimate ≥ 0.50). The diagnostic accuracy of SVM was also compared to visual segmental scoring by two experienced readers. Sensitivity of SVM (84%) was significantly better than ISCH (75%, p < 0.05) and EFC (31%, p < 0.05). Specificity of SVM (88%) was significantly better than that of TPD (78%, p < 0.05) and EFC (77%, p < 0.05). Diagnostic accuracy of SVM (86%) was significantly better than TPD (81%), ISCH (81%), or EFC (46%) (p < 0.05 for all). The Receiver-operator-characteristic area-under-the-curve (ROC-AUC) for SVM (0.92) was significantly better than TPD (0.90), ISCH (0.87), and EFC (0.60) (p < 0.001 for all). Diagnostic accuracy of SVM was comparable to the overall accuracy of both visual readers (85% vs. 84%, p < 0.05). ROC-AUC for SVM (0.92) was significantly better than that of both visual readers (0.87 and 0.88, p < 0.03). Computational integration of quantitative perfusion and functional variables by SVM approach allows significant improvement of diagnostic accuracy of MPS, and can significantly outperform visual assessment based on ROC analysis.
