Diagnostic Performance of Territorial Phase Analysis Parameters for Detecting Significance Coronary Artery Stenosis in Patients With Normal Left Ventricular Ejection Fraction

Background and Aims Stress phase analysis derived from gated myocardial perfusion imaging (GMPI) is strongly associated with major adverse cardiac events (MACE). Evaluation of mechanical dyssynchrony parameters may provide additional value for identifying significant coronary stenosis and risk stratification in patients with normal left ventricular ejection fraction (LVEF). This study demonstrates the diagnostic performance of territorial phase analysis parameters alongside perfusion in differentiating the significance of stenosis. Methods We retrospectively analyzed 45 vessel territories from 15 patients who underwent both GMPI and coronary angiography. Stenosis severity was categorized into significant and non-significant groups. Territorial mechanical synchrony was assessed using phase analysis. Statistical analyses included the Mann-Whitney U test, Spearman’s correlation, and Receiver Operating Characteristic (ROC) curve analysis. Results A significant, moderate positive correlation was observed between stenosis grade and perfusion abnormalities (rho = 0.366, p = 0.013). All phase parameters were significantly higher in the significant stenosis group (p < 0.05). Phase standard deviation (PHSD) demonstrated the highest diagnostic accuracy with an Area Under the Curve (AUC) of 0.842 (95% CI: 0.731–0.953, p < 0.001). Entropy (PHE) followed closely with an AUC of 0.832 (95% CI: 0.716–0.948, p < 0.001), while Bandwidth (PHBW) showed an AUC of 0.731 (p = 0.009). Conclusion Territorial phase analysis parameters, particularly PHSD and PHE, are robust indicators of mechanical dyssynchrony associated with significant coronary artery stenosis. Clinical application of these parameters may improve the identification of significant coronary artery disease and enhance patient risk stratification. Figure 1.Diagnostic performance for territorial vessel characteristics using three parameters histograms derived from phase analysis GMPI (left) and an ROC analysis with AUC values (right).