Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm

Abstract

We aimed to improve the diagnostic accuracy of automatic myocardial perfusion SPECT (MPS) interpretation analysis for the prediction of coronary artery disease (CAD) by integrating several quantitative perfusion and functional variables for noncorrected (NC) data by Support Vector Machine (SVM) algorithm, a computer method for machine learning.

Methods: Rest-stress gated (99m)Tc MPS NC studies (n = 957) from 623 consecutive patients with correlating invasive coronary angiography and 334 with a low likelihood of CAD (<5%) were assessed. Stenosis ≥50% in left main or ≥70% in all other vessels was considered abnormal. Total perfusion deficit (TPD) was computed automatically. In addition, ischemic changes (ISCHs) and ejection fraction changes (EFCs) between stress and rest were derived by quantitative software. The SVM was trained using a group of 125 patients (25 with low-likelihood, 25 with 0-vessel, 25 with 1-vessel, 25 with 2-vessel, and 25 with 3-vessel CAD) with the above quantitative variables and second-order polynomial fitting. The remaining patients (n = 832) were categorized using probability estimates, with CAD defined as a probability estimate ≥ 0.50. The diagnostic accuracy of SVM was also compared with visual segmental scoring by 2 experienced readers.

Results: The sensitivity of SVM (84%) was significantly better than ISCH (75%, P < 0.05) and EFC (31%, P < 0.05). The specificity of SVM (88%) was significantly better than TPD (78%, P < 0.05) and EFC (77%, P < 0.05). The diagnostic accuracy of SVM (86%) was significantly better than TPD (81%), ISCH (81%), or EFC (46%) (P < 0.05 for all). The receiver-operating-characteristic (ROC) area under the curve for SVM (0.92) was significantly better than TPD (0.90), ISCH (0.87), and EFC (0.64) (P < 0.001 for all). The diagnostic accuracy of SVM was comparable to the overall accuracy of both visual readers (86% vs. 84%, P = NS). The ROC area under the curve for SVM (0.92) was significantly better than that of both visual readers (0.87 and 0.88, P < 0.03).

Conclusion: Computational integration of quantitative perfusion and functional variables using the SVM approach significantly improves the diagnostic accuracy of MPS and can significantly outperform visual assessment based on ROC analysis.

Figure 1

Sensitivity, specificity and accuracy of Support Vector Machines (SVM) versus Total Perfusion Deficit (TPD), Ischemic Change (ISCH), and Ejection Fraction Change (EFC) for detection of ≥70% coronary artery lesions. Red indicates significant difference compared to SVM (p < 0.05).

Figure 2

The Receiver Operating Characteristic (ROC) curves comparing the Support Vector Machines (SVM) and Total Perfusion Deficit (TPD), Ischemic Change (ISCH), Motion and Thickening Change (MTC), and Ejection Fraction Change (EFC) for detection of ≥70% coronary artery lesions. * Indicates statistically significant difference compared to a SVM (p < 0.05).

Figure 3

Sensitivity, specificity and accuracy of Support Vector Machines (SVM) versus visual analysis (SSS and SDS) for detection of ≥70% coronary artery lesions. * Indicates statistically significant difference compared to SVM (p < 0.05).

Figure 4

The Receiver Operating Characteristic (ROC) curves comparing the Support Vector Machines (SVM) and visual analysis for detection of ≥70% coronary artery lesions. * Indicates statistically significant difference compared to SVM (p < 0.05).