Bois John P, Scott Chris, Chareonthaitawee Panithaya, Gibbons Raymond J, Rodriguez-Porcel Martin
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Department of Cardiovascular Diseases, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA.
EJNMMI Res. 2019 Jan 31;9(1):11. doi: 10.1186/s13550-019-0476-y.
Myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) is commonly used to assess patients with cardiovascular disease. However, in certain scenarios, it may have limited specificity in the identification of hemodynamically significant coronary artery disease (e.g., false positive), potentially resulting in additional unnecessary testing and treatment. Phase analysis (PA) is an emerging, highly reproducible quantitative technology that can differentiate normal myocardial activation (synchrony) from myocardial scar (dyssynchrony). The objective of this study is to determine if PA can improve the specificity SPECT MPI.
An initial cohort of 340 patients (derivation cohort), referred for SPECT-MPI, was prospectively enrolled. Resting MPI studies were assessed for resting perfusion defects (scar). These were utilized as the reference standard for scar. Subsequently, we collected a second independent validation cohort of 138 patients and tested the potential of PA to reclassify patients for the diagnosis of "scar" or "no scar." Patients were assigned to three categories depending upon their pre-test probability of scar based on multiple clinical and imaging parameters: ≤ 10% (no scar), 11-74% (indeterminate), and ≥ 75% (scar). The ability of PA variables to reclassify patients with scar to a higher group and those without scar to a lower group was then determined using the net reclassification index (NRI).
Entropy (≥ 59%) was independently associated with scar in both patient cohorts with an odds ratio greater than five. Furthermore, when added to multiple clinical/imaging variables, the use of entropy significantly improved the area under the curve for assessment of scar (0.67 vs. 0.59, p = 0.04). The use of entropy correctly reclassified 24% of patients without scar, by clinical model, to a lower risk category (as determined by pre-test probability) with an overall NRI of 18% in this validation cohort.
The use of PA entropy can improve the specificity of SPECT MPI and may serve as a useful adjunctive tool to the interpreting physician. The current study determined the optimal PA parameters to detect scar (derivation cohort) and applied these parameters to a second, independent, patient group and noted that entropy (≥ 59%) was independently associated with scar in both patient cohorts. Therefore, PA, which requires no additional imaging time or radiation, enhances the diagnostic capabilities of SPECT MPI.
The use of PA entropy significantly improved the specificity of SPECT MPI and could influence the labeling of a patient as having or not having myocardial scar and thereby may influence not only diagnostic reporting but also potentially prognostic determination and therapeutic decision-making.
单光子发射计算机断层扫描（SPECT）心肌灌注成像（MPI）常用于评估心血管疾病患者。然而，在某些情况下，其在识别血流动力学显著的冠状动脉疾病时可能特异性有限（例如假阳性），这可能导致额外的不必要检查和治疗。相位分析（PA）是一种新兴的、高度可重复的定量技术，可区分正常心肌激活（同步性）与心肌瘢痕（不同步）。本研究的目的是确定PA是否能提高SPECT MPI的特异性。
前瞻性纳入了340例因SPECT-MPI就诊的初始队列患者（推导队列）。对静息MPI研究进行静息灌注缺损（瘢痕）评估。这些被用作瘢痕的参考标准。随后，我们收集了138例患者的第二个独立验证队列，并测试了PA对患者重新分类以诊断“瘢痕”或“无瘢痕”的潜力。根据基于多个临床和影像参数的瘢痕预测试概率，将患者分为三类：≤10%（无瘢痕）、11 - 74%（不确定）和≥75%（瘢痕）。然后使用净重新分类指数（NRI）确定PA变量将有瘢痕患者重新分类到更高组以及将无瘢痕患者重新分类到更低组的能力。
在两个患者队列中，熵（≥59%）均与瘢痕独立相关，优势比大于5。此外，当将熵添加到多个临床/影像变量中时，使用熵显著改善了评估瘢痕的曲线下面积（0.67对0.59，p = 0.04）。在该验证队列中，通过临床模型，熵的使用将24%无瘢痕患者正确重新分类到较低风险类别（由预测试概率确定），总体NRI为18%。
PA熵的使用可提高SPECT MPI的特异性，并可能成为解读医师的有用辅助工具。当前研究确定了检测瘢痕的最佳PA参数（推导队列），并将这些参数应用于第二个独立患者组，发现熵（≥59%）在两个患者队列中均与瘢痕独立相关。因此，无需额外成像时间或辐射的PA增强了SPECT MPI的诊断能力。
PA熵的使用显著提高了SPECT MPI的特异性，并可能影响患者是否有心肌瘢痕的判定，从而不仅可能影响诊断报告，还可能潜在地影响预后判定和治疗决策。
