Diagnostic and Prognostic Value of Myocardial Flow Reserve Quantification with Single Photon Emission Computed Tomography – a Systematic Review and Meta-Analysis

Diagnostic and Prognostic Value of Myocardial Flow Reserve Quantification with Single Photon Emission Computed Tomography – a Systematic Review and Meta-Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic and Prognostic Value of Myocardial Flow Reserve Quantification with Single Photon Emission Computed Tomography – a Systematic Review and Meta-Analysis Barnabas Baksa, Sreeyapureddy Surendranath Reddy, Sára Bundula, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7425841/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in EJNMMI Research → Version 1 posted 5 You are reading this latest preprint version Abstract Background : Myocardial flow reserve (MFR), derived from dynamic single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), offers quantitative insight into coronary physiology and may overcome limitations of conventional semi-quantitative SPECT. We aimed to systematically review and meta-analyse the diagnostic accuracy and prognostic value of SPECT-derived MFR in comparison with invasive coronary angiography (ICA), PET-MPI, and long-term patient outcomes. Results: A comprehensive literature search was done in scientific databases for studies comparing SPECT-derived MFR in patients with known or suspected coronary artery disease to ICA, PET-MPI, or ≥12-month follow-up for major adverse cardiac events. A meta-analysis was conducted using random-effects models for studies comparing SPECT-MFR with PET-MPI, and reporting diagnostic performance metrics including sensitivity and specificity. Thirty-two studies were included (n = 19 for ICA; n = 8 for PET-MPI; 1 for both ICA and PET-MPI and n = 4 for follow-up). Thirty studies showed a significant correlation between SPECT-derived MFR and reference standards with excellent area under the curve values (AUC>0.7) reported. Six PET-MPI comparator studies (with a total number of participants, n = 180) were included in the meta-analysis, yielding a pooled sensitivity of 78.5% (95% CI: 71.7-84.1%) and specificity of 89.3% (95% CI: 70.4-96.7%) (diagnostic odds ratio = 15.7 (95% CI: 6.270-39.269)). MFR consistently predicted major adverse cardiac events in prognostic studies, independent of obstructive coronary status. Conclusions: Quantitative MFR derived from dynamic SPECT-MPI correlates well with established diagnostic reference tests and independently predicts adverse outcomes. While PET remains the reference standard, SPECT-MPI offers a viable and more accessible alternative. Standardised protocols and large-scale prospective validation are needed to optimise its clinical implementation. PROSPERO Registration: CRD42024507703 Myocardial Perfusion Imaging Single-Photon Emission-Computed Tomography Positron-Emission Tomography Myocardial Blood Flow Coronary Artery Disease Coronary Angiography Meta-Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background Functional assessment of myocardial perfusion is critical for patients with coronary artery disease (CAD) of intermediate severity [ 1 ]. Static myocardial perfusion imaging by SPECT (SPECT-MPI) is an accessible and informative diagnostic test that has been used in clinical practice for decades. However, the conventional technique has several limitations because of its semi-quantitative nature. Patients with 3-vessel disease or diffuse microvascular dysfunction who have equally impaired perfusion throughout the myocardium (“balanced ischaemia”) may be misdiagnosed on conventional SPECT-MPI, thus lowering the diagnostic value of the examination [ 2 , 3 ]. There have been successful attempts to quantify myocardial perfusion with SPECT previously, but the appearance of the high-sensitivity, solid-state cadmium-zinc-telluride (CZT) detector SPECT devices brought a new opportunity in the last decade for the quantification with single-photon radio-tracers [ 4 – 8 ]. These devices have the possibility of relatively high-resolution spatial and temporal image acquisition, thus assessing myocardial time-activity-curves (TACs). The tracer’s myocardial tissue uptake (K1) value is measured based on the stress and rest TACs. The Renkin-Crone model is then usually utilised to quantify myocardial blood flow (MBF) in rest (rMBF) and pharmacologically induced stress (sMBF) [ 9 ]. Myocardial flow reserve (MFR), sometimes referred to as coronary flow reserve (CFR) or myocardial perfusion reserve (MPR), is calculated as sMBF/rMBF [ 10 , 11 ]. This parameter, also acquired by PET myocardial perfusion imaging (PET-MPI), reflects the ability of the coronary circulation to adapt to the myocardium’s increased blood flow demand and may bypass the disadvantages of semi-quantitative SPECT-MPI [ 11 , 12 ]. PET-MPI is considered the gold standard of perfusion quantification and assessing MFR. However, the low accessibility and high price of this examination create a demand for a more available alternative, like SPECT. This article aims to systematically review all currently available studies in the literature, assessing the diagnostic or prognostic value of SPECT-derived MFR by comparing it to a well-established reference standard test. We also intend to conduct a meta-analysis on a smaller but more homogeneous group of original articles to evaluate the diagnostic accuracy of SPECT-MPI in measuring MFR in comparison to the gold-standard PET-MPI. 2. Methods This systematic review and meta-analysis was conducted and reported in accordance with the PRISMA 2020 statement [ 13 ]. A comprehensive search was made in January 2025 in English in multiple scientific databases, including PubMed, Embase, Web of Science, Cochrane Library, and Scopus. The search strategy was designed to contain strings for the patient population, the index test, and the comparator reference standard/reference variable in MeSH terms and synonyms, divided by boolean operators and tailored to each database. Three different diagnostic tests (invasive coronary angiography (ICA), coronary CT angiography (CCTA), and PET myocardial perfusion imaging (PET-MPI)) and “patient follow-up” (FU) for at least 12 months were accepted as a reference standard. This review was pre-registered at the PROSPERO database (ID: CRD42024507703 ). No amendments to the registered protocol were made after registration. The search strings tailored to each database are available in Supplementary Material 1 . 2.1. Screening To reduce human error, screening was done independently by two researchers (B.B. and S.B.) based on the pre-defined inclusion and exclusion criteria. In all cases of conflict, a consensus could be reached. The exclusion criteria were as follows: preprints, case reports and case series, studies published other than in English, non-original articles and studies with unavailable full texts were excluded. Studies meeting the inclusion criteria were included in the final analysis (Table 1). We included any study that assessed and compared the diagnostic value of MFR, calculated as sMBF/rMBF measured by SPECT among patients with known or suspected CAD, to a well-established reference standard described above or assessed its prognostic value for predicting the occurrence of major adverse cardiac events (MACE). Both prospective and retrospective studies were accepted. A screening dedicated web tool (Rayyan.ai) was used for duplicate removal and screening [ 14 ]. Studies included in the final analysis were then grouped according to the type of used comparator. The first and last authors and institution names were checked for overlap in each study to ensure that all included patients were unique. Studies were categorized according to the reference standard used for comparison. Original studies using PET-MPI as a reference standard were further selected to be included in the meta-analysis. All studies in this group were included in the final statistical analysis, if the distribution of true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN) were reported. 2.2 Data extraction General data (authors, year of publication, number of participants) and specific data, including 1) the methodology of SPECT perfusion assessment (protocol, radiotracer, administered activity, type of SPECT, hyperaemic pharmaceutical) 2) outcome measures (type and cut-off of the reference variable, optimal cut-off point of global and regional MFR with the corresponding sensitivity and specificity value), 3) correlation of results, and 4) conclusions, were collected manually by one single reviewer (B.B.). For the purpose of statistical analysis, TP, FP, FN, and TN distributions were collected in a 2×2 contingency table only from studies included in the meta-analysis. 2.3. Bias Assessment Two independent reviewers (B.B. and S.B.) assessed all included studies for bias using the QUADAS-2 questionnaire, designed specifically for diagnostic accuracy studies ( Supplementary Material 2 )[ 15 ]. In cases of discordance, a consensus was achieved during joint reading. Bias and applicability were evaluated in the following domains: patient selection, index test, reference standard, and flow and timing and assessed as “low,” “unclear,” or “high.” Selection bias was considered low if consecutive patient enrolment and appropriate exclusion criteria were reported. In this review, quantitative SPECT-MPI was considered as the index test. As there are no broadly accepted cut-off values for SPECT MFR yet, studies evaluated low or unclear probability for index bias even if no pre-specified threshold was used. Reference test risk of bias was considered intermediate/unclear if more than one element was not reported. Flow and timing bias was evaluated as low if no more than 3 months passed between the index and the reference standard test. In the case of follow-up (FU), this question was tailored to the length of the FU period, which was considered appropriate if more than 1 year. Applicability concerns were deemed low if the studies fulfilled the inclusion criteria. 2.4. Statistical methods for the meta-analysis Based on the original studies, individual and pooled estimates of metrics such as Sensitivity, Specificity, Positive likelihood (PLR), Negative likelihood (NLR) and Diagnostic Odds ratio (DOR) were calculated. To stabilise variance, natural logarithms were applied to NLR, PLR and DOR and calculated for the ease of distribution. We anticipated heterogeneity and applied a random effects model. Random-effects models aided in accounting for heterogeneity and provided more generalizable estimates. We used rigorous statistical methods to account for variability between studies, providing clinically meaningful results while addressing the inherent zero-cell issue and heterogeneity. Given the limited number of studies (n = 6) eligible for meta-analysis, sensitivity analyses were not performed, as the exclusion of individual studies would have disproportionately affected the pooled estimates and undermined the validity of the synthesis[ 16 ]. To remove zero cells, a modified Haldane-Ascombe correction was used, and logarithms were applied to normalise the distribution [ 17 ]. Subsequently, studies were weighted using inverse variance. Between-study variance is calculated using the DerSimonian-Laird method. For Heterogeneity assessment, the Cochrane Q-test is implemented. The Python programming language was employed to generate Forest plots and a summary Receiver Operating Characteristic (sROC) curve. Due to the limited number of studies included in the meta-analysis, formal methods to assess reporting bias, such as funnel plot analysis or statistical tests for small-study effects, were not performed, as these approaches are considered unreliable with small sample sizes [ 18 ]. A formal certainty assessment (e.g., using the GRADE approach) was not performed either, as the primary objective of this review was to synthesize diagnostic performance metrics from a limited number of studies rather than to formulate clinical recommendations [ 19 , 20 ]. The heterogeneity and methodological diversity across studies further limited the applicability of such tools. The complete analysis workflow, including data and code, is openly accessible via GitHub repository at: https://github.com/SurendranathReddy1993/SPECTMFRMetaAnalysis 3. Results The study selection process can be seen in Figure 1. After removing duplicates from the initial search results, 701 articles remained for title-abstract selection. Full texts of 74 articles were screened. Thirty-two studies were included in the final analysis that were grouped into ICA (n=20), PET-MPI (n=9), and FU (n=4) groups. No eligible studies were found with CCTA as a comparator; therefore, no such group was created. One study by Agostini et al. 2018 used both ICA and PET-MPI as reference standards and was sorted into both groups[21]. There were no studies that fulfilled the inclusion criteria but were excluded for other reasons. From the 9 original studies of the PET-MPI group, only 6 were included in the meta-analysis, as 3 failed to report cut-off values and the distribution of TP, FP, TN and TN. 3.1. Bias assessment The results of the bias assessment are summarised in Figure 2. Applicability concerns were generally considered low. Patient selection risk of bias was considered unclear in most cases when the process of patient enrolment and/or the exclusion criteria were not reported clearly. Blinding of the index test was not reported in most studies. 3.2. Quantitative SPECT-MPI vs. Invasive Coronary Angiography (ICA) Twenty articles were found to be eligible for inclusion using ICA as a comparator to assess the diagnostic value of quantitative SPECT-MPI. Key characteristics are summarised in Table 2.a. and 2.b. [7, 21-38] Included studies enrolled a total of 1198 consecutive patients with suspected or known CAD. In most studies, myocardial perfusion was measured by novel CZT detector SPECTs (i.e., D-SPECT or Discovery NM530c) except in 4 articles using conventional NaI detector SPECTs with parallel low-energy high-resolution collimators. Generally In 18 of 20 studies (90%), only [⁹⁹ᵐTc]sestamibi or [⁹⁹ᵐTc]tetrofosmin was used as a radiotracer; however, Shiraishi et al. and Han et al. applied Tl-201. Studies were also heterogeneous regarding image processing softwares and attenuation correction (AC) applications. As some of the cardiac-specific SPECT systems are not equipped with CT, AC could only be performed using a dual-head SPECT/CT or a separate CT device in 5 studies. Adenosine, dipyridamole, and regadenoson were used to create the hyperaemic state for stress imaging. Image acquisition was performed using only a 1-day protocol in 16 studies, mostly with rest acquisition first and both 1-day and 2-day protocols in 3 studies. Different protocols require different doses of activity, but studies proved to be heterogeneous regarding the administered dose of the radiotracer, even when the same protocol was used. ICA was performed using the standard Judkins method in all cases. While all studies in this group compared quantitative SPECT results such as MFR to ICA, various reference variables, such as stenosis extent in one of the main coronary arteries (with various cut-offs), invasive fractional flow reserve (FFR, abnormal if ≤0,8), or composite endpoints comprising the two or, such as Gensini score, were used [39]. Djaileb et al. compared SPECT-derived MFR to a positive composite criterion of CFR≤2 and index of microcirculatory resistance (IMR)≥25, thus reflecting on both epicardial coronary artery and intramyocardial microvascular function [23]. We recorded the optimal MFR cut-off points from ROC analyses, as well as the corresponding sensitivity and specificity values, at both global (patient-level) and regional (vessel-level) scales for detecting abnormal ICA findings, as reported in the included studies. Based on ROC analyses with AUC values ranging from 0.7 to 0.94, global MFR (gMFR) cut-off points varied from 1.15 to 2.66, yielding sensitivities of 66.7% to 100% and specificities of 50% to 91%. Regional ROC analyses produced AUC values ranging from 0.73 to 0.97, with MFR (rMFR) cut-off points between 1.26 and 2.2, leading to sensitivities of 53.3% to 95.2% and specificities of 53.5% to 93.3%. Nineteen of the twenty studies (95%) in this group reported significant correlations between SPECT-derived MFR and ICA findings. One outlier study found insignificant correlation and could not point to an optimal threshold for MFR [28]. However, in that particular study, sMBF by quantitative SPECT was significantly correlated with coronary stenosis and proved superior to MFR and semi-quantitative SPECT findings, thus improving the detectability of multi-vessel CAD. Bai et al., J. Wang et al., and Kawaguchi et al. reported better diagnostic accuracy with higher AUC values for MFR compared to semi-quantitative scores.[22, 26, 30] 3.3. Quantitative SPECT-MPI vs. PET-MPI Nine prospective studies were found to be eligible for inclusion, comparing SPECT-derived MFR to its PET counterpart, with a total of 240 participants. Key characteristics of the included studies can be seen in Table 3.a. and 3.b. [4-6, 9, 21, 40-43] All studies used a 1-day protocol starting with the rest acquisition, apart from 2 studies with stress first. AC for the SPECT imaging was used in 4 out of 9 studies (44,4%), while all studies used AC for PET-MPI. [⁹⁹ᵐTc]sestamibi and [⁹⁹ᵐTc]tetrofosmin were utilised as a single-photon radio-tracer with varying activity, while [¹³N]ammonia, [⁸²Rb]rubidium chloride and [¹⁵O]water were used for PET imaging. Adenosine, dipyridamole or regadenoson was used to induce hyperaemia. Both CZT and NaI detector SPECT devices were used for imaging. When a cut-off point was used, PET-MFR was considered abnormal when <2.0 in most cases. The optimal cut-off point of SPECT-MFR for detecting abnormal MFR on PET based on ROC analyses was reported in 6 studies with gMFR varying from 1.26 to 2.5, with AUC values of 0.75-0.96, 68-89% sensitivity and 73-100% specificity on the patient level. In comparison, the ROC analyses revealed an optimal cut-off value for rMFR between 1.79 and 1.94 with a consequent 78-84% and 63-80% sensitivity and specificity, respectively, on the vessel level. Eight of the nine (88,89%) included studies reported a significant correlation between PET-MFR and SPECT-MFR. One study by de Souza et al. found a poor and non-significant correlation between PET-MPI and SPECT results [40]. Giubbini et al. measured regional SPECT-MFR both with and without AC and reported significantly better diagnostic accuracy when AC was applied [43]. However, Wells et al. tested motion correction (MC), AC and correction for tracer red blood cell binding (BB) and reported the highest global AUC values (0.975) in the case of -BB,-AC,+MC to predict abnormal PET-MFR. +BB,-AC,+MC resulted in the highest regional AUC value (0.866) [9]. 3.3.1. Results of the meta-analysis Six out of nine studies reported cut-off, sensitivity and specificity values, enabling the extraction of TP, FP, FN, and TN values, allowing inclusion in the meta-analysis ([4, 9, 21, 41-43]). Extracted patient characteristics and the individual study results are shown in Table 4. Analysed studies enrolled a total of 180 participants. The sensitivities and specificities of the included studies are summarised in Figure 3 and 4. To evaluate the diagnostic performance of SPECT-MPI-derived MFR in detecting PET-MPI-derived MFR ≤2.0, a pooled analysis of diagnostic value was conducted, resulting in a sensitivity of 0.785 (95% CI: 0.717-0.841) and a specificity of 0.893 (95% CI: 0.704-0.967) as shown on a summary ROC (sROC) curve (Figure 5.). Pooled DOR was 15.7 (95% CI: 6.270-39.269). PLR and NLR were 7.3 (95% CI: 2.385-22.418) and 0.24 (95% CI: 0.174-0.331), respectively, as summarised in Figures 6, 7, and 8. The analysis revealed moderate between-study heterogeneity for DOR (τ² = 0.4; Cochran’s Q = 7.32, df = 5, p = 0.198; I² = 31.7%) and sensitivity (τ² = 0.2; Cochran’s Q = 95.5, df = 5, p < 0.001). We observed substantial heterogeneity for specificity (τ² = 2.4; Cochran’s Q = 5590, df = 5, p < 0.001). These findings support the application of a random-effects model in the meta-analysis. 3.4. Predictive value of SPECT-derived MFR Four studies were found to evaluate the ability of SPECT-derived MFR to predict the occurrence of MACEs based on a median of at least 15 months of follow-up. The main characteristics of the included studies are summarised in Table 5.a. and 5.b. [8, 44-46] Follow-up was done by telephone and hospital history records. The primary endpoint in the included studies was the occurrence of a MACE, defined as one of the following: cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, heart failure, late or unplanned coronary revascularisation, or hospitalisation for unstable angina. Both CZT and NaI detector SPECT devices were used for SPECT imaging, either with 1- or 2-day protocols. Injected activities varied between studies. Abnormal global MFR cut-off value was pre-defined in three studies as <2.0, while Zhang et al. calculated an optimal threshold of <2.52 based on the ROC analysis [45]. In all studies, Kaplan-Meier curves showed significantly worse event-free survival rates for patients with impaired MFR. In multivariate analysis, MFR remained an independent predictor of MACEs, while semi-quantitative SPECT-MPI results did not, as reported by Sun et al. [46]. Li et al. enrolled 303 patients with available ICA results, sorting participants into obstructive CAD (OCAD) and ischaemia with non-obstructed coronary arteries (INOCA) groups. The two groups did not differ significantly in MACE probability after 16-21 months of follow-up, while patients with impaired MFR were reported to have a four-fold increased event rate in both groups compared to patients with normal MFR [44]. Zhang et al. included 118 INOCA patients and reported a sensitivity of 84.2% and a specificity of 77.8% (AUC: 0.83) of gMFR <2.52 to predict the onset of MACEs after a median of 15 months [45]. 4. Discussion In this study, we collected and reviewed all currently available original articles comparing the results of quantitative SPECT-MPI, specifically MFR, to a well-established reference standard to assess its diagnostic accuracy and predictive value. In addition, a targeted meta-analysis was performed on the subset of studies comparing SPECT-MPI to PET-MPI, the established reference standard for myocardial perfusion imaging. MFR is based on the quantitation of stress and rest MBF; as the ratio of the two, it reflects the ability of the myocardial vasculature to adapt to an increased blood flow demand. Absolute quantitation of myocardial blood flow has several advantages compared to conventional semi-quantitative MPI, with the theoretical superiority in diagnosing patients with 3-vessel disease or diffuse microvascular dysfunction and is a more objective and repeatable way of assessing myocardial perfusion [ 47 , 48 ]. Wells et al. 2023 proved the repeatability of SPECT MBF measurement across sites and readers in a multi-centric study[ 49 ]. Although it is reported to work better than sMBF alone, MFR has its known disadvantages. For one, its value is highly dependent on rMBF, which primarily depends on patient characteristics and poorly reflects the severity of CAD[ 22 ]. 4.1. Quantitative SPECT-MPI vs. ICA ICA is considered a reliable examination and the last step in the diagnostic management of patients with suspected CAD. Therefore, most of the studies used ICA as a comparator, thus assessing the ability of quantitative SPECT-MPI to non-invasively predict significant or flow-limiting stenosis on ICA and help patient management. However, it should be noted that ICA routinely only evaluates the status of the main coronary arteries, may they be measured by stenosis extent or FFR, and do not directly assess microvascular function or myocardial perfusion. Patients without macroscopic stenosis or abnormal FFR in the main coronary arteries suffering from ischaemic symptoms are considered INOCA. These symptoms may be caused by microvascular dysfunction or vasospasm, resulting in lowered perfusion parameters [ 50 ]. When using ICA as a gold standard, these patients might be falsely evaluated as false-positive on MPI, inadequately lowering specificity[ 50 ]. When a stenosis (especially on a distal segment) on a coronary artery is caused by a plaque built over the years, myocardial perfusion might not be impaired because of adaptational mechanisms like angiogenesis [ 51 ]. These cases might appear falsely as false negatives on MPI, wrongly lowering sensitivity. Despite this limitation, 19/20 (95%) of the included studies found a significant correlation between MFR and ICA results and reported sufficient sensitivity and specificity with excellent AUC values. The study of Djaileb et al. is unique as they assessed both epicardial coronary and microvascular function in their comparison [ 23 ]. Considering this, their results should be regarded as highly relevant despite the relatively low participant number. In 2021, Panjer et al. conducted a meta-analysis including nine studies. They reported a sensitivity of 79% and a specificity of 85% of MFR for predicting abnormal FFR on ICA or < 2.0 MFR on PET-MPI [ 52 ]. Some studies in our ICA group assessed semi-quantitative SPECT results and reported significantly lower correlations and diagnostic values than quantitative MFR. Consequently, quantitative SPECT-MPI may be superior to the conventional semi-quantitative technique and is a valuable tool in the diagnostic management of CAD. Various authors reported the best diagnostic results and AUC values when they combined MFR with conventional semi-quantitative SPECT-MPI parameters, suggesting that the two techniques should be utilised together[ 25 , 28 , 33 , 36 ]. 4.2. Quantitative SPECT-MPI vs. PET-MPI Although conventional PET tracers ([¹³N]ammonia, [¹⁵O]water and [⁸²Rb]rubidium chloride) differ in their pharmacokinetics from each other, PET-MPI is a well-established test to assess and quantify myocardial perfusion regardless of the radiotracer used[ 53 ]. PET-MPI has several advantages over SPECT-MPI and is generally considered a gold standard [ 54 ]. Because of their pharmacokinetics, PET radiotracers reflect on myocardial perfusion better than SPECT tracers, as their K 1 value is further from being linearly proportional to MBF. Without corrections, [⁹⁹ᵐTc]sestamibi and [⁹⁹ᵐTc]tetrofosmin, most commonly used for SPECT-MPI, tend to underestimate MBF, especially in higher flow values [ 53 ]. Therefore, a suitable conversion function, such as the Renkin-Crone equation, among other corrections, must be used [ 55 ]. The pooled PLR (7.3) and NLR (0.24) from our meta-analysis indicate that SPECT-derived MFR demonstrates a moderate capacity to confirm the presence of disease and to exclude cases deemed negative by PET-MPI, respectively. The DOR of 15.6 further supports the test’s moderate overall diagnostic performance. These findings underscore the clinical utility of MFR and suggest that, despite heterogeneity among the included studies, quantitative SPECT-MPI yields diagnostic performance comparable to that of PET-MPI. Although SPECT-MPI exhibits moderately lower diagnostic accuracy relative to PET-MPI, it remains a viable and more widely accessible alternative for clinical application. Despite the fact that the use of AC in PET-MPI is widely accepted, its clinical benefit in SPECT-MPI remains uncertain. Giubbini et al. analysed regional data with and without AC and reported a significantly better regional AUC when AC was applied [ 43 ], while Bailly et al. and Wells et al. did not prove the advantage of AC application [ 9 , 29 ]. In theory, with the morphological data obtained by CT, AC would resolve the heterogeneity of the thoracic attenuation, thus increasing precision for measuring MBF. However, myocardial TACs are a yield not only of the tracer's K 1 but also of the arterial input function. Since the attenuation of the arterial input function is, on average, similar to that of the myocardium, this effect is mostly cancelled out on the global scale. When assessing regional MBF, AC might increase precision. However, because MFR is defined as the ratio of sMBF to rMBF, regional alterations in attenuation may be nullified during this division, thereby diminishing the potential benefits of AC. In addition, AC can increase image noise, partially offsetting the improvements achieved by reducing regional attenuation heterogeneity; consequently, regional ROC analyses may not consistently demonstrate a significant advantage for AC.[ 9 ]. Image processing softwares used for PET-MPI has an automatic MC tool, while MC is mostly done manually for SPECT-MPI. This holds a possibility of error, thus lowering accuracy and increasing intra- and inter-observer variability [ 56 ]. Based on the results of our meta-analysis, SPECT is sufficiently capable of assessing quantitative perfusion parameters comparable to PET. Although its diagnostic accuracy seems to be slightly lower than PET-MPI, SPECT remains a viable, cost-effective alternative for myocardial perfusion assessment, whereas PET-MPI remains the reference standard. 4.3. Clinical application and predictive value of SPECT-derived MFR MFR proved to have an excellent prognostic ability in predicting MACEs and could be a valuable tool in the risk stratification of CAD. Li et al. showed a similar MACE probability in the OCAD and INOCA group, with a four-fold increase when MFR was impaired on SPECT. These results indicate that perfusion parameters, such as MFR, have higher predictive potential than macroscopic coronary obstruction as they reflect both microvascular and epicardial vessel function. The authors point out that risk stratification in CAD patients relying solely on coronary anatomical information is insufficient, and perfusion assessment should also be used [ 44 ]. Regardless of whether ICA or PET-MPI was used as a comparator when an ROC analysis was performed in studies, the optimal MFR cut-off point varied in a broad range. This could be explained by the heterogeneous study settings, patient characteristics, administered activity, acquisition protocol, processing software and whether AC or other corrections were applied. Most of the included studies used a 1-day protocol with rest acquisition first, but different settings were also utilised. In the case of the 1-day protocol, a considerable residual activity interferes with the second acquisition. This can partially be neutralised by the larger (usually almost three-fold) secondly injected activity and residual subtraction, but it might still impact the precise quantification of MBF. Quantitative SPECT-MPI, as a key tool in the diagnostic management of CAD, would be adapted more easily to everyday clinical practice with a well-defined diagnostic protocol and broadly accepted optimal MFR cut-off point. Multi-centred studies with united study protocols and large participant numbers are still needed. 5. Limitations This systematic review and meta-analysis has several limitations. First, it encompasses studies with considerable heterogeneity in patient populations, study settings, and other parameters, thereby complicating comparisons and impeding comprehensive analyses. Consequently, only a narrower-spectrum meta-analysis was feasible, which may be susceptible to publication bias given that only studies reporting complete, typically positive data were included. Furthermore, the review inherits the limitations of the included studies, such as small sample sizes, short follow-up periods (in the FU group), and the inherent difficulty of comparing a functional diagnostic test with a morphological one (in the ICA group). Despite our best efforts, some relevant studies may have remained unidentified (e.g., due to language restrictions, limited databases searched, or unpublished “grey literature” not captured). 6. Conclusion Dynamic SPECT-MPI-derived MFR aligns well with anatomical and functional ICA and PET-MPI findings and may predict cardiac events, supporting its incremental prognostic value and diagnostic accuracy in CAD management. Our meta-analysis highlights the clinical utility of SPECT-derived MFR, suggesting that it may serve as a viable alternative despite PET-MPI remains the gold standard for myocardial perfusion assessment. However, whether AC improves the precision of MBF measurement remains unclear. Large-scale, multicenter studies are needed to refine examination protocols, establish an MFR cut-off, and encourage broader clinical implementation. Abbreviations AC attenuation correction AUC area under the curve BB red blood cell binding CAD coronary artery disease CCTA coronary computed tomography angiography CFR coronary flow reserve CI confidence interval CZT cadmium-zink-telluride DOR diagnostic odds ratio FFR fractional flow reserve FN false negative FP false positive FU follow-up gMFR global myocardial flow reserve ICA invasive coronary angiography IMR index of microcirculatory resistance INOCA ischaemia with non-obstructed coronary arteries K 1 myocardial tissue uptake value MACE major adverse cardiac events MBF myocardial blood flow MFR myocardial flow reserve MPI myocardial perfusion imaging MC motion correction MPI myocardial perfusion imaging MPR myocardial perfusion reserve NLR negative likelihood ratio OCAD obstructive coronary artery disease PET positron emission tomography PLR positive likelihood ratio rMBF rest myocardial blood flow rMFR regional myocardial flow reserve ROC receiver operating characteristic sMBF stress myocardial blood flow SPECT single photon emission computed tomography sROC summary receiver operating characteristic TAC time activity curve TN true negative TP true positive Declarations Ethics approval This study is a systematic review and meta-analysis based exclusively on data extracted from previously published studies. All included primary studies received appropriate ethical approval from their respective institutional review boards or ethics committees. As no new data were collected and no individual patient-level data were used, further ethical approval was not required for the conduct of this review. Consent for publication This article does not report any individual person’s data in any form (including individual details, images, or videos). Therefore, consent for publication is not applicable. Availability of data and material Detailed statistical methodology and the corresponding custom Python programming code are available via GitHub repository at: https://github.com/SurendranathReddy1993/SPECTMFRMetaAnalysis. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Conflicts of interest The authors declare that they have no financial or other conflicts of interest related to this work. Funding This study did not receive any funding or grant from public, commercial, or not-for-profit sources. Authors’ contributions All authors contributed to the conceptualisation of the study. Methodology was developed by BB, SB, and SSR. Formal analysis was performed by SSR and BB. Investigation, including literature screening and data extraction, was carried out by BB and SB. Data curation was conducted by TG, SL, KN, SB and PMH. The original draft was prepared by BB, SB, and SSR. SL, TG, KN, SB and PMH contributed to the review and editing of the manuscript. TG and PMH provided supervision. All authors read and approved the final manuscript. Acknowledgements The authors thank the Medical Imaging Centre, Semmelweis University, for providing institutional support. We also gratefully acknowledge Brigitta Teutsch and Oszkár Pártos for their valuable methodological support and suggestions. References Sabharwal, N. and A. 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Inclusion criteria Systematic review Meta-analysis Population >18 yo. patients with known or suspected CAD, both genders >18 yo. patients with known or suspected CAD, both genders Intervention Quantitative SPECT-MPI with the assessment of MFR calculated as sMBF/rMBF Quantitative SPECT MPI with the assessment of MFR calculated as sMBF/rMBF Comparator ICA, PET-MPI, CCTA or FU PET-MPI Outcome and other ICA: diagnostic value of MFR and/or its correlation with stenosis extent and/or FFR assessed by ICA or other ICA-derived composite endpoint PET-MPI: diagnostic value of MFR and/or its correlation with PET-MPI-derived MFR CCTA: diagnostic value of MFR and/or its correlation with stenosis extent assessed by CCTA FU: predictive value of MFR for the occurance of MACEs PET-MPI: diagnostic value of MFR to predict PET-MPI-derived MFR >2.0 TP, FP, FN, TN values reported CAD = coronary artery disease, SPECT-MPI = single photon emission computed tomography myocardial perfusion imaging, MFR = myocardial flow reserve, sMBF = stress myocardial blood flow, rMBF = rest myocardial blood flow, ICA = invasive coronary angiography, PET-MPI = positron emission tomography myocardial perfusion imaging, CCTA = coronary computed tomography angiography, FU = follow-up, MACE = major adverse cardiac event, TP = true positive, FP = false positive, FN = false negative, TN = true negative Table 2.a . General characteristics of included studies, ICA group Authors Year of Publication Participants in the Final Analysis Patient Enrollment Gamma-camera Processing software AC Radio-tracer Acquisition protocol Hyperaemic Pharmaceutical Injected Activity (MBq) Scout for positioning First injection Second injection Bai et al. [22] 2024 93 retrospective Symbia T16 MyoFlowQ Yes [⁹⁹ᵐTc]sestamibi 1-day (rest first) adenosine N/A 555 1110 Djaïleb et al. [23] 2023 25 prospective Discovery NM530c Corridor4DM No [⁹⁹ᵐTc]sestamibi 1-day (stress first) dipyridamole N/A 291 ± 60 875 ± 180 Zhang et al. [24] 2023 180 prospective Discovery NM530c MyoFlowQ Yes [⁹⁹ᵐTc]sestamibi 2-day (rest first) adenosine 37 370-555 370-555 Fang et al. [25] 2023 24 retrospective D-SPECT QPS, Cedars-Sinai No [⁹⁹ᵐTc]sestamibi both adenosine N/A N/A 1150 Kawaguchi et al.[26] 2022 41 retrospective Discovery NM530c N/A No [⁹⁹ᵐTc]tetrofosmin 1-day (stress first) adenosine 0.3/kg 3/kg 9/kg L. Wang et al.[27] 2022 154 prospective Symbia T16 and Symbia T2 QPS, Cedars-Sinai Yes [⁹⁹ᵐTc]sestamibi 1-day (rest first) adenosine N/A 370 1110 Liu et al.[28] 2021 32 retrospective D-SPECT Corridor 4DM No [⁹⁹ᵐTc]sestamibi 1-day (rest first) dipyridamole 37 259 888 Bailly et al.[29] 2021 23 prospective Discovery NM530c Corridor 4DM No [⁹⁹ᵐTc]tetrofosmin 1-day (stress first) regadenoson or dipyridamole 27 250 500 J. Wang et al.[30] 2021 49 prospective Discovery NM530c MyoFlowQ Yes [⁹⁹ᵐTc]sestamibi both (rest first when 1-day) adenosine 1-day: 18.5-37 185-296 555-888 2-day: 18.5-37 185-296 185-296 Chen et al.[31] 2020 82 prospective Intevo Excel MyoFlowQ Yes [⁹⁹ᵐTc]sestamibi 1-day (rest first) dipyridamole N/A 370 1110 Li et al.[32] 2020 34 prospective D-SPECT Corridor 4DM No [⁹⁹ᵐTc]sestamibi 1-day (rest first) adenosine 37 555 925 Pang et al.[33] 2020 57 prospective Discovery NM530c MyoFlowQ No [⁹⁹ᵐTc]sestamibi both (rest first when 1-day) adenosine 1-day: 18.5–37 185–296 3x(185–296) 2-day: 18.5–37 370–555 370–555 Zavadovsky et al.[34] 2019 23 prospective Discovery NM/CT 570c Corridor 4DM Yes [⁹⁹ᵐTc]sestamibi 1-day (rest first) adenosine N/A 245.3±42.3 736±127 Acampa et al.[35] 2019 91 prospective D-SPECT Corridor 4DM No [⁹⁹ᵐTc]sestamibi 1-day (rest first) dipyridamole 37 155 370 Shiraishi et al.[36] 2019 125 retrospective Discovery NM530c AZE VirtualPlace Hayabusa No 201 Tl 1-day (stress first) adenosine N/A 50-60 50-60 de Souza et al.[57] 2019 41 prospective Discovery NM530c Corridor4DM No [⁹⁹ᵐTc]sestamibi 1-day (rest first) dipyridamole 37 370 1110 Iguchi et al.[37] 2018 37 prospective D-SPECT Corridor4DM No [⁹⁹ᵐTc]sestamibi 1-day (rest first) adenosine N/A 208±23 (3,7/kg) 762±53 (11.1/kg) Han et al.[38] 2018 34 prospective Discovery NM530c Corridor4DM No stress: 201 Tl, rest: [⁹⁹ᵐTc]tetrofosmin 1-day (rest first) adenosine N/A 50.3±6.3 ( 201 Tl) 261.2±41.7 ( 99m Tc) Agostini et al. [21] 2018 30 prospective D-SPECT Corridor4DM No [⁹⁹ᵐTc]sestamibi 1-day (rest first) regadenoson 37 3/kg 9/kg Bouallegue et al. [7] 2015 23 prospective Discovery NM530c N/A No [⁹⁹ᵐTc]tetrofosmin 1-day (rest first) dipyridamole 35-60 185-220 645+730 Table 2.b. Results of the included studies, ICA group Authors Reference Variable Optimal MFR cut-off (global) AUC value Sensitivity (%) Specificity (%) Optimal MFR cut-off (regional) AUC value Sensitivity (%) Specificity (%) Bai et al.[22] ≥50% or ≥75% stenosis ≥50%: 2.0 ≥75%: 1.68 ≥50%: 0.939 ≥75%: 0.932 ≥50%: 87.23% ≥75%: 100% ≥50%: 89.13% ≥75%: 72.73% ≥50%: 1.95 ≥75%: 1.26 ≥50%: 0.933 ≥75%: 0.933 ≥50%: 95.15% ≥75%: 86.36% ≥50%: 78.41% ≥75%: 85.6% Djaïleb et al.[23] positive composite criterion on ICA: CFR: ≤2, IMR: ≥25 N/A N/A N/A N/A 1.8 0.844 87 92 Zhang et al. [24] ≥50% or ≥70% stenosis ≥50%: 1.96 ≥70%: 1.75 ≥50%: 0.91 ≥70%: 0,91 ≥50%: 80 ≥70%: 75 ≥50%: 84-90 ≥70%: 87-91 ≥50%: 1.7 ≥70%: 1.6 ≥50%: 0.81 ≥70%: 0.80 ≥50%: 74 ≥70%: 71 ≥50%: 85 ≥70%: 70-74 Fang et al. [25] ≥50% or ≥75% stenosis N/A N/A N/A N/A ≥50%: 2.16 ≥75%: 2.08 ≥50%: 0.731 ≥75%: 0.730 ≥50%: 61.8% ≥75%: 68.8% ≥50%: 86.8% ≥75%: 76.8% Kawaguchi et al.[26] FFR≤0.8 or ≥70% stenosis (both counted as significant CAD) N/A N/A N/A N/A 1.31 0.81 77 75 L. Wang et al.[27] ≥50% stenosis 1.95 0.77 72.4 79.5 1.95 0.756 75.9 67.3 Liu et al.[28] ≥50% stenosis N/A N/A N/A N/A N/A N/A N/A N/A Bailly et al.[29] ≥50% stenosis 2.28 0.94 88.9 80 1.92 0.79 84.4 56.76 J. Wang et al.[30] ≥ 50% or ≥ 75% stenosis ≥50%: 1.61 ≥75%: 1.15 ≥50%: 0.82 ≥75%: 0.75 ≥50%: 84.4 ≥75%: 78.9 ≥50%: 88.2 ≥75%: 63.3 N/A N/A N/A N/A Chen et al.[31] ≥50% or ≥70% stenosis 2 N/A ≥50%: 70, ≥70%: 73 ≥50%: 63, ≥70%: 61 N/A N/A N/A N/A Li et al.[32] FFR<0,8 N/A N/A N/A N/A 1.73 0.864 88.9 83.3 Pang et al.[33] ≥ 50% or ≥ 75% stenosis N/A N/A N/A N/A ≥50%: 1.77 ≥75%: 1.76 ≥50%: 0.772 ≥75%: 0.770 ≥50%: 85.7 ≥75%: 90.9 ≥50%: 58.6 ≥75%: 53.5% Zavadovsky et al.[34] FFR ≤0.8 N/A N/A N/A N/A 1.48 0,86 69.2 93.3 Acampa et al.[35] ≥70% stenosis 2.6 0.7 86 50 2.1 0.76 65 83 Shiraishi et al.[36] ≥50% (LM) or ≥70% (LAD, LCX, RCA) or ≤0.8 FFR (any vessel) 2.66 0.75 77 66 N/A N/A N/A N/A de Souza et al.[57] ≥70% stenosis in any major coronary artery or ≥50% in LM 2.08 0.755 66.7 84.6 2.2 N/A 63.2 74.1 Iguchi et al.[37] Gensini score N/A N/A N/A N/A N/A N/A N/A N/A Han et al.[38] FFR ≤0.8 N/A N/A N/A N/A 2 0.79 67 83 Agostini et al.[21] FFR ≤0.8 N/A N/A N/A N/A 2.1 N/A 53.3 84.6 Bouallegue et al.[7] ≥50% stenosis or FFR ≤0.8 N/A N/A N/A N/A 2 ≥50%: 0.85 ≤0.8 FFR: 0.97 ≥50%: 80 ≤0.8 FFR: 89 ≥50%: 85 ≤0.8 FFR: 82 MFR = myocardial flow reserve, AUC = area under the curve, ICA = invasive coronary angiography, CFR = coronary flow reserve, IMR = index of microvasculatory resistance, FFR = fractonal flow reserve, LM = left main, LAD = left anterior descending, RCA = right coronary artery, LCX = left circumflex Table 3. a. General Characteristics of included studies, PET-MPI group Studies included in the systematic review Studies included in the meta-analysis First Author Year of Publication Number of Participants Patient Enrollment Gamma-camera Processing software AC for SPECT PET radio-tracer SPECT radio-tracer Injected Activity (MBq) Acquisition protocol Hyperaemic Pharmaceutical Scout injection First injection Second Injection Yamamoto et al. [41] 2022 14 prospective D-SPECT Corridor4DM no [¹³N]ammonia [⁹⁹ᵐTc]sestamibi 30 155 555 1 day (rest first) adenosine Acampa et al.[42] 2020 25 prospective D-SPECT QGS/QPS, Cedars-Sinai no [⁸²Rb]rubidium chloride [⁹⁹ᵐTc]sestamibi 37 155 370 1 day (rest first) dipyridamole Giubbini et al.[43] 2019 54 prospective Discovery NM530c Corridor4DM yes and no [¹³N]ammonia [⁹⁹ᵐTc]tetrofosmin 10-15 185 370 1 day (stress first) regadenoson Agostini et al.[21] 2018 30 prospective D-SPECT Corridor4DM no [¹⁵O]water [⁹⁹ᵐTc]sestamibi 37 294 780 1 day (rest first) regadenoson Wells et al.[9] 2017 29 prospective Discovery NM530c FlowQuant yes and no [⁸²Rb]rubidium chloride and [¹³N]ammonia [⁹⁹ᵐTc]tetrofosmin N/A 316±71 1,122± 170 1 day (rest first) dipyridamole Nkoulou et al.[4] 2016 28 prospective Discovery NM570c Myovation for Alcyone yes [¹³N]ammonia [⁹⁹ᵐTc]tetrofosmin N/A 330±33 990± 99 1 day (stress first) adenosine de Souza et al.[40] 2022 34 prospective D-SPECT Corridor4DM no [¹³N]ammonia [⁹⁹ᵐTc]sestamibi 37 222 814 1 day (rest first) regadenoson Nose et al.[5] 2016 9 prospective Millennium VG N/A no [¹⁵O]water [⁹⁹ᵐTc]sestamibi N/A 370 740 1 day (rest first) adenosine Shrestha et al.[6] 2015 16 prospective Infinia Hawkeye 4 N/A yes [¹³N]ammonia [⁹⁹ᵐTc]tetrofosmin N/A 370 925 1 day (rest first) regadenoson Table 3. b. Results of the included studies, PET-MPI group Studies included in the systematic review Studies included in the meta-analysis First Author Year of Publication Reference Variable MFR cut-off (global) AUC value Sensitivity (%) Specificity (%) MFR cut-off (regional) AUC value Sensitivity (%) Specificity (%) Yamamoto et al.[41] 2022 PET MFR <2.0 N/A N/A N/A N/A 1.6 0.75 68 91 Acampa et al.[42] 2020 PET MFR <2.0 for global 2.5 0.85 86 73 N/A N/A N/A N/A Giubbini et al.[43] 2019 PET MFR <2.0 N/A N/A N/A N/A NAC: 1.94; AC: 1.96 NAC: 0.767 ; AC: 0.748 NAC: 81; AC: 84 NAC: 64; AC: 63 Agostini et al.[21] 2018 PET MFR <2.0 2.1 0.96 83.3 100 N/A N/A N/A N/A Wells et al.[9] 2017 PET MFR <2.0 2.15-1.53 (resulting in best AUC: 1.53) 0.866-0.975 78-100 (resulting in best AUC: 89) 70-100 (resulting in best AUC: 100) 1.64-2.26 (resulting in best AUC: 1.79) 0.770-0.866 65-91 (resulting in best AUC: 78) 66-86 (resulting in best AUC: 80) Nkoulou et al.[4] 2016 PET MFR <2.0 1.26 N/A 70 78 N/A N/A N/A N/A de Souza et al.[40] 2022 PET MFR N/A N/A N/A N/A N/A N/A N/A N/A Nose et al.[5] 2016 PET MFR N/A N/A N/A N/A N/A N/A N/A N/A Shrestha et al.[6] 2015 PET MFR N/A N/A N/A N/A N/A N/A N/A N/A MFR = myocardial flow reserve, AUC = area under the curve, NAC= non-attenuation corrected, AC = attenuation corrected Table 4. Extracted data and patient characteristics for the meta-analysis Authors Year of Publication Participants TP FP FN TN median age male (%) DM (%) obesity (%) HT (%) HCT (%) BMI prior CAD (%) prior MI (%) Yamamoto et al.[41] 2022 14 13 2 6 21 73 71 21 N/A 64 71 24 14 0 Acampa et al.[42] 2020 25 12 3 2 8 61 76 40 N/A 80 56 N/A N/A N/A Giubbini et al.[43] 2019 54 26 47 6 83 68 69 24 N/A N/A N/A N/A N/A 19 Agostini et al.[21] 2018 30 5 0 1 24 65 70 33 27 67 60 N/A N/A N/A Wells et al.[9] 2017 29 8 0 1 20 64 87 19 N/A N/A N/A 27,9 N/A N/A Nkoulou et al.[4] 2016 28 7 4 3 14 N/A N/A 32 N/A 61 64 N/A N/A N/A TP = true positive, FP = false positive, FN = false negative, TN = true negative, DM = diabetes mellitus, HT = hypertension, HCT = hypercholesterinaemia, BMI = body mass index, CAD = coronary artery disease, MI = myocardial infarction Table 5.a. General characteristics of included studies, FU group Authors Year of Publication Number of Participants Patient Enrollment Gamma-camera Image processing software AC SPECT Radio-tracer Acquisition protocol Hyperaemic Pharmaceutical Injected Activity (MBq) Scout injection First injection Second injection Li et al.[44] 2023 303 retrospective Discovery NM530c, GE Healthcare MyoflowQ yes [⁹⁹ᵐTc]sestamibi 1 day (rest first) adenosine or ATP 18,5-37 185-296 555-888 Sun et al.[46] 2023 119 prospective Symbia T16, Siemens MyoFlowQ yes [⁹⁹ᵐTc]sestamibi N/A ATP N/A N/A N/A Zhang et al.[45] 2023 118 retrospective D-SPECT, Spectrum Dynamics Corridor 4DM no [⁹⁹ᵐTc]sestamibi 1 day (rest first) adenosine 37 370 925 Daniele et al.[8] 2011 99 prospective E.CAM, Siemens Medical Systems N/A no [⁹⁹ᵐTc]sestamibi 2 day dypiridamole N/A 555 555 AC = attenuation correction Table 5.b. Results of included studies, FU group Authors Primary Endpoint Follow-up time Optimal MFR cut-off Sensitivity (%) Specificity (%) Li et al.[44] MACE 16-21 months <=2,0 (pre-defined) N/A N/A Sun et al.[46] MACE 1408 days (1297-1666 days) <=2,0 (pre-defined) N/A N/A Zhang et al.[45] MACE 15 months (11-20) 2,52 (AUC: 0.83) 84.2 77.8 Daniele et al.[8] MACE 5.8 ± 2.1 years <=2,0 (pre-defined) N/A N/A MFR = myocardial flow reserve, MACE = major adverse cardiac event, AUC = area under the curve Supplementary Files PRISMA2020checklist.docx SupplementaryMaterial1.docx SupplementaryMaterial2.xlsx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in EJNMMI Research → Version 1 posted Editorial decision: Minor Revision 15 Sep, 2025 Reviewers agreed at journal 29 Aug, 2025 Reviewers invited by journal 29 Aug, 2025 Editor assigned by journal 25 Aug, 2025 First submitted to journal 24 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7425841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507335877,"identity":"2c4a312f-621e-4557-a8d1-48c1f8eb7445","order_by":0,"name":"Barnabas Baksa","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0000-9397-6202","institution":"Semmelweis University: Semmelweis Egyetem","correspondingAuthor":true,"prefix":"","firstName":"Barnabas","middleName":"","lastName":"Baksa","suffix":""},{"id":507335878,"identity":"7fc73373-1fa6-4cb5-92e8-543867d5bae9","order_by":1,"name":"Sreeyapureddy Surendranath Reddy","email":"","orcid":"","institution":"Semmelweis University: Semmelweis Egyetem","correspondingAuthor":false,"prefix":"","firstName":"Sreeyapureddy","middleName":"Surendranath","lastName":"Reddy","suffix":""},{"id":507335879,"identity":"68491cd3-41a5-4c4a-97b2-41a210760f92","order_by":2,"name":"Sára Bundula","email":"","orcid":"","institution":"Hungarian Defence Forces Medical Centre: Magyar Honvedseg Egeszsegugyi Kozpont","correspondingAuthor":false,"prefix":"","firstName":"Sára","middleName":"","lastName":"Bundula","suffix":""},{"id":507335880,"identity":"9528249a-9339-4795-b76b-dc3b4a59ca66","order_by":3,"name":"Kristóf Nagy","email":"","orcid":"","institution":"Semmelweis University: Semmelweis Egyetem","correspondingAuthor":false,"prefix":"","firstName":"Kristóf","middleName":"","lastName":"Nagy","suffix":""},{"id":507335881,"identity":"943111d7-c52b-4df4-bcda-451db2a332a9","order_by":4,"name":"Samule Beke","email":"","orcid":"","institution":"Semmelweis University: Semmelweis Egyetem","correspondingAuthor":false,"prefix":"","firstName":"Samule","middleName":"","lastName":"Beke","suffix":""},{"id":507335882,"identity":"cd96a22d-3273-476f-b3de-f37424327899","order_by":5,"name":"Lili Száraz","email":"","orcid":"","institution":"Semmelweis University: Semmelweis Egyetem","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Száraz","suffix":""},{"id":507335883,"identity":"221eff18-8760-46c6-9ff0-bfe53bcc32bc","order_by":6,"name":"Tamás Györke","email":"","orcid":"","institution":"Semmelweis University: Semmelweis Egyetem","correspondingAuthor":false,"prefix":"","firstName":"Tamás","middleName":"","lastName":"Györke","suffix":""},{"id":507335884,"identity":"cf20617e-64b0-401a-9cf6-c6cb06054869","order_by":7,"name":"Pál Maurovich-Horvat","email":"","orcid":"","institution":"Semmelweis University: Semmelweis Egyetem","correspondingAuthor":false,"prefix":"","firstName":"Pál","middleName":"","lastName":"Maurovich-Horvat","suffix":""}],"badges":[],"createdAt":"2025-08-21 11:47:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7425841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7425841/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13550-025-01335-2","type":"published","date":"2025-11-28T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90879459,"identity":"312e5ba0-d7b8-4698-92c0-0ab5ee7c7b09","added_by":"auto","created_at":"2025-09-09 09:35:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182795,"visible":true,"origin":"","legend":"\u003cp\u003eStudy selection process from a comprehensive search to study inclusion. Note, that one study used ICA and PET-MPI as a comparator and was sorted into both groups. ICA = invasive coronary angiography, PET-MPI = positron emission tomography myocardial perfusion imaging, FU = follow-up\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/5b6b27a6e1c26c28e7d82a49.png"},{"id":90879460,"identity":"ea572f04-937e-404a-ab7c-7a0bf1c1d8c7","added_by":"auto","created_at":"2025-09-09 09:35:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":649965,"visible":true,"origin":"","legend":"\u003cp\u003eQuadas-2 risk of bias summary. The risk of bias is evaluated as “High”, “Some concerns”, or “Low”.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/2b937c407a84eeb9c6e6c169.png"},{"id":90879464,"identity":"aaa5ba52-5b6b-4196-859c-bdfa9852d11b","added_by":"auto","created_at":"2025-09-09 09:35:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88197,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for sensitivity values, CI = confidence interval\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/123774c1f744742541aa0cc9.png"},{"id":90882114,"identity":"021b31a7-6933-4941-abd1-60039ee639f7","added_by":"auto","created_at":"2025-09-09 09:51:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85317,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for specificity values, CI = confidence interval\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/b21f3e3de8d776053011d162.png"},{"id":90879474,"identity":"a95414ff-be66-441d-a31b-e5799717ab0a","added_by":"auto","created_at":"2025-09-09 09:35:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108845,"visible":true,"origin":"","legend":"\u003cp\u003eSummary Receiver Operating Curve (sROC) of the included studies.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/ba03fbb2f70c701c9179273a.png"},{"id":90881459,"identity":"95f0db00-74ea-4f6d-980d-c68b54125711","added_by":"auto","created_at":"2025-09-09 09:43:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":153628,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for diagnostic odds ratios (DOR) on a logarithmic scale, CI = confidence interval\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/baaccd9a89828481cbbcc278.png"},{"id":90884239,"identity":"d068bdbb-ab75-42fc-85d6-7daafd3b3be4","added_by":"auto","created_at":"2025-09-09 09:59:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":101043,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for positive likelihood ratios (PLR) on a logarithmic scale, CI = confidence interval\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/a4a7ec34c7cfd7abfba1fdc9.png"},{"id":90879467,"identity":"f139f952-bae7-473d-80b8-b58532bb8103","added_by":"auto","created_at":"2025-09-09 09:35:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":90588,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for negative likelihood ratios (DOR) on a logarithmic scale, CI = confidence interval\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/d1d84bf1c6bec2c298f81bff.png"},{"id":97178368,"identity":"b81f1ae1-1649-4b03-92bf-ce9546f03275","added_by":"auto","created_at":"2025-12-01 16:09:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2998115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/41fc2479-ad2b-4084-bc90-3c4e711a0e85.pdf"},{"id":90879462,"identity":"d1e938a1-b715-4ffc-9956-b4d6bdd31775","added_by":"auto","created_at":"2025-09-09 09:35:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":275348,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMA2020checklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/574969d3772418b923f09d03.docx"},{"id":90879461,"identity":"f5751ba6-aad3-478c-9cee-d05064ce7642","added_by":"auto","created_at":"2025-09-09 09:35:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18101,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/d99f036240dd6ff9e36558b5.docx"},{"id":90881457,"identity":"651c63e7-08c1-4da8-8759-740e172c1a05","added_by":"auto","created_at":"2025-09-09 09:43:51","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17990,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7425841/v1/5de677affdc62384ed17790b.xlsx"}],"financialInterests":"","formattedTitle":"Diagnostic and Prognostic Value of Myocardial Flow Reserve Quantification with Single Photon Emission Computed Tomography – a Systematic Review and Meta-Analysis","fulltext":[{"header":"1. Background","content":"\u003cp\u003eFunctional assessment of myocardial perfusion is critical for patients with coronary artery disease (CAD) of intermediate severity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Static myocardial perfusion imaging by SPECT (SPECT-MPI) is an accessible and informative diagnostic test that has been used in clinical practice for decades. However, the conventional technique has several limitations because of its semi-quantitative nature. Patients with 3-vessel disease or diffuse microvascular dysfunction who have equally impaired perfusion throughout the myocardium (\u0026ldquo;balanced ischaemia\u0026rdquo;) may be misdiagnosed on conventional SPECT-MPI, thus lowering the diagnostic value of the examination [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere have been successful attempts to quantify myocardial perfusion with SPECT previously, but the appearance of the high-sensitivity, solid-state cadmium-zinc-telluride (CZT) detector SPECT devices brought a new opportunity in the last decade for the quantification with single-photon radio-tracers [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These devices have the possibility of relatively high-resolution spatial and temporal image acquisition, thus assessing myocardial time-activity-curves (TACs). The tracer\u0026rsquo;s myocardial tissue uptake (K1) value is measured based on the stress and rest TACs. The Renkin-Crone model is then usually utilised to quantify myocardial blood flow (MBF) in rest (rMBF) and pharmacologically induced stress (sMBF) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Myocardial flow reserve (MFR), sometimes referred to as coronary flow reserve (CFR) or myocardial perfusion reserve (MPR), is calculated as sMBF/rMBF [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This parameter, also acquired by PET myocardial perfusion imaging (PET-MPI), reflects the ability of the coronary circulation to adapt to the myocardium\u0026rsquo;s increased blood flow demand and may bypass the disadvantages of semi-quantitative SPECT-MPI [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. PET-MPI is considered the gold standard of perfusion quantification and assessing MFR. However, the low accessibility and high price of this examination create a demand for a more available alternative, like SPECT.\u003c/p\u003e\u003cp\u003eThis article aims to systematically review all currently available studies in the literature, assessing the diagnostic or prognostic value of SPECT-derived MFR by comparing it to a well-established reference standard test. We also intend to conduct a meta-analysis on a smaller but more homogeneous group of original articles to evaluate the diagnostic accuracy of SPECT-MPI in measuring MFR in comparison to the gold-standard PET-MPI.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis systematic review and meta-analysis was conducted and reported in accordance with the PRISMA 2020 statement [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A comprehensive search was made in January 2025 in English in multiple scientific databases, including PubMed, Embase, Web of Science, Cochrane Library, and Scopus. The search strategy was designed to contain strings for the patient population, the index test, and the comparator reference standard/reference variable in MeSH terms and synonyms, divided by boolean operators and tailored to each database. Three different diagnostic tests (invasive coronary angiography (ICA), coronary CT angiography (CCTA), and PET myocardial perfusion imaging (PET-MPI)) and \u0026ldquo;patient follow-up\u0026rdquo; (FU) for at least 12 months were accepted as a reference standard. This review was pre-registered at the PROSPERO database (ID: \u003cem\u003eCRD42024507703\u003c/em\u003e). No amendments to the registered protocol were made after registration. The search strings tailored to each database are available in \u003cem\u003eSupplementary Material 1\u003c/em\u003e.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Screening\u003c/h2\u003e\u003cp\u003eTo reduce human error, screening was done independently by two researchers (B.B. and S.B.) based on the pre-defined inclusion and exclusion criteria. In all cases of conflict, a consensus could be reached. The exclusion criteria were as follows: preprints, case reports and case series, studies published other than in English, non-original articles and studies with unavailable full texts were excluded. Studies meeting the inclusion criteria were included in the final analysis (Table\u0026nbsp;1). We included any study that assessed and compared the diagnostic value of MFR, calculated as sMBF/rMBF measured by SPECT among patients with known or suspected CAD, to a well-established reference standard described above or assessed its prognostic value for predicting the occurrence of major adverse cardiac events (MACE). Both prospective and retrospective studies were accepted.\u003c/p\u003e\u003cp\u003eA screening dedicated web tool (Rayyan.ai) was used for duplicate removal and screening [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Studies included in the final analysis were then grouped according to the type of used comparator. The first and last authors and institution names were checked for overlap in each study to ensure that all included patients were unique. Studies were categorized according to the reference standard used for comparison.\u003c/p\u003e\u003cp\u003eOriginal studies using PET-MPI as a reference standard were further selected to be included in the meta-analysis. All studies in this group were included in the final statistical analysis, if the distribution of true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN) were reported.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data extraction\u003c/h2\u003e\u003cp\u003e General data (authors, year of publication, number of participants) and specific data, including 1) the methodology of SPECT perfusion assessment (protocol, radiotracer, administered activity, type of SPECT, hyperaemic pharmaceutical) 2) outcome measures (type and cut-off of the reference variable, optimal cut-off point of global and regional MFR with the corresponding sensitivity and specificity value), 3) correlation of results, and 4) conclusions, were collected manually by one single reviewer (B.B.). For the purpose of statistical analysis, TP, FP, FN, and TN distributions were collected in a 2\u0026times;2 contingency table only from studies included in the meta-analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Bias Assessment\u003c/h2\u003e\u003cp\u003e Two independent reviewers (B.B. and S.B.) assessed all included studies for bias using the QUADAS-2 questionnaire, designed specifically for diagnostic accuracy studies (\u003cem\u003eSupplementary Material 2\u003c/em\u003e)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In cases of discordance, a consensus was achieved during joint reading. Bias and applicability were evaluated in the following domains: patient selection, index test, reference standard, and flow and timing and assessed as \u0026ldquo;low,\u0026rdquo; \u0026ldquo;unclear,\u0026rdquo; or \u0026ldquo;high.\u0026rdquo; Selection bias was considered low if consecutive patient enrolment and appropriate exclusion criteria were reported. In this review, quantitative SPECT-MPI was considered as the index test. As there are no broadly accepted cut-off values for SPECT MFR yet, studies evaluated low or unclear probability for index bias even if no pre-specified threshold was used. Reference test risk of bias was considered intermediate/unclear if more than one element was not reported. Flow and timing bias was evaluated as low if no more than 3 months passed between the index and the reference standard test. In the case of follow-up (FU), this question was tailored to the length of the FU period, which was considered appropriate if more than 1 year. Applicability concerns were deemed low if the studies fulfilled the inclusion criteria.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistical methods for the meta-analysis\u003c/h2\u003e\u003cp\u003eBased on the original studies, individual and pooled estimates of metrics such as Sensitivity, Specificity, Positive likelihood (PLR), Negative likelihood (NLR) and Diagnostic Odds ratio (DOR) were calculated. To stabilise variance, natural logarithms were applied to NLR, PLR and DOR and calculated for the ease of distribution. We anticipated heterogeneity and applied a random effects model. Random-effects models aided in accounting for heterogeneity and provided more generalizable estimates. We used rigorous statistical methods to account for variability between studies, providing clinically meaningful results while addressing the inherent zero-cell issue and heterogeneity. Given the limited number of studies (n\u0026thinsp;=\u0026thinsp;6) eligible for meta-analysis, sensitivity analyses were not performed, as the exclusion of individual studies would have disproportionately affected the pooled estimates and undermined the validity of the synthesis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo remove zero cells, a modified Haldane-Ascombe correction was used, and logarithms were applied to normalise the distribution [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Subsequently, studies were weighted using inverse variance. Between-study variance is calculated using the DerSimonian-Laird method. For Heterogeneity assessment, the Cochrane Q-test is implemented. The Python programming language was employed to generate Forest plots and a summary Receiver Operating Characteristic (sROC) curve.\u003c/p\u003e\u003cp\u003eDue to the limited number of studies included in the meta-analysis, formal methods to assess reporting bias, such as funnel plot analysis or statistical tests for small-study effects, were not performed, as these approaches are considered unreliable with small sample sizes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A formal certainty assessment (e.g., using the GRADE approach) was not performed either, as the primary objective of this review was to synthesize diagnostic performance metrics from a limited number of studies rather than to formulate clinical recommendations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The heterogeneity and methodological diversity across studies further limited the applicability of such tools.\u003c/p\u003e\u003cp\u003eThe complete analysis workflow, including data and code, is openly accessible via GitHub repository at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/SurendranathReddy1993/SPECTMFRMetaAnalysis\u003c/span\u003e\u003cspan address=\"https://github.com/SurendranathReddy1993/SPECTMFRMetaAnalysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe study selection process can be seen in Figure 1. After removing duplicates from the initial search results, 701 articles remained for title-abstract selection. Full texts of 74 articles were screened. Thirty-two studies were included in the final analysis that were grouped into ICA (n=20), PET-MPI (n=9), and FU (n=4) groups. No eligible studies were found with CCTA as a comparator; therefore, no such group was created. One study by Agostini et al. 2018 used both ICA and PET-MPI as reference standards and was sorted into both groups[21].\u0026nbsp;There were no studies that fulfilled the inclusion criteria but were excluded for other reasons.\u003c/p\u003e\n\u003cp\u003eFrom the 9 original studies of the PET-MPI group, only 6 were included in the meta-analysis, as 3 failed to report cut-off values and the distribution of TP, FP, TN and TN.\u003c/p\u003e\n\u003ch2\u003e3.1. Bias assessment\u003c/h2\u003e\n\u003cp\u003eThe results of the bias assessment are summarised in Figure 2. Applicability concerns were generally considered low. Patient selection risk of bias was considered unclear in most cases when the process of patient enrolment and/or the exclusion criteria were not reported clearly. Blinding of the index test was not reported in most studies.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2. Quantitative SPECT-MPI vs. Invasive Coronary Angiography (ICA)\u003c/h2\u003e\n\u003cp\u003eTwenty articles were found to be eligible for inclusion using ICA as a comparator to assess the diagnostic value of quantitative SPECT-MPI. Key characteristics are summarised in Table 2.a. and 2.b. [7, 21-38]\u003c/p\u003e\n\u003cp\u003eIncluded studies enrolled a total of 1198 consecutive patients with suspected or known CAD. In most studies, myocardial perfusion was measured by novel CZT detector SPECTs (i.e., D-SPECT or Discovery NM530c) except in 4 articles using conventional NaI detector SPECTs with parallel low-energy high-resolution collimators.\u003cdel cite=\"mailto:Dr.%20Nagy%20Kristóf%20(klinikai%20orvos)\" datetime=\"2025-08-04T11:49\"\u003eGenerally\u003c/del\u003e In 18 of 20 studies (90%), only [⁹⁹ᵐTc]sestamibi or [⁹⁹ᵐTc]tetrofosmin was used as a radiotracer; however, Shiraishi et al. and Han et al. applied Tl-201. Studies were also heterogeneous regarding image processing softwares and attenuation correction (AC) applications. As some of the cardiac-specific SPECT systems are not equipped with CT, AC could only be performed using a dual-head SPECT/CT or a separate CT device in 5 studies. Adenosine, dipyridamole, and regadenoson were used to create the hyperaemic state for stress imaging. Image acquisition was performed using only a 1-day protocol in 16 studies, mostly with rest acquisition first and both 1-day and 2-day protocols in 3 studies. Different protocols require different doses of activity, but studies proved to be heterogeneous regarding the administered dose of the radiotracer, even when the same protocol was used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eICA was performed using the standard Judkins method in all cases. While all studies in this group compared quantitative SPECT results such as MFR to ICA, various reference variables, such as stenosis extent in one of the main coronary arteries (with various cut-offs), invasive fractional flow reserve (FFR, abnormal if\u0026nbsp;\u0026le;0,8), or composite endpoints comprising the two or, such as Gensini score, were used [39].\u0026nbsp;Djaileb et al. compared SPECT-derived MFR to a positive composite criterion of CFR\u0026le;2 and index of microcirculatory resistance (IMR)\u0026ge;25, thus reflecting on both epicardial coronary artery and intramyocardial microvascular function\u0026nbsp;[23]. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe recorded the optimal MFR cut-off points from ROC analyses, as well as the corresponding sensitivity and specificity values, at both global (patient-level) and regional (vessel-level) scales for detecting abnormal ICA findings, as reported in the included studies. Based on ROC analyses with AUC values ranging from 0.7 to 0.94, global MFR (gMFR) cut-off points varied from 1.15 to 2.66, yielding sensitivities of 66.7% to 100% and specificities of 50% to 91%. Regional ROC analyses produced AUC values ranging from 0.73 to 0.97, with MFR (rMFR) cut-off points between 1.26 and 2.2, leading to sensitivities of 53.3% to 95.2% and specificities of 53.5% to 93.3%.\u003c/p\u003e\n\u003cp\u003eNineteen of the twenty studies (95%) in this group reported significant correlations between SPECT-derived MFR and ICA findings. One outlier study found insignificant correlation and could not point to an optimal threshold for MFR [28]. However, in that particular study, sMBF by quantitative SPECT was significantly correlated with coronary stenosis and proved superior to MFR and semi-quantitative SPECT findings, thus improving the detectability of multi-vessel CAD. Bai et al., J. Wang et al., and Kawaguchi et al. reported better diagnostic accuracy with higher AUC values for MFR compared to semi-quantitative scores.[22, 26, 30]\u003c/p\u003e\n\u003ch2\u003e3.3. Quantitative SPECT-MPI vs. PET-MPI\u003c/h2\u003e\n\u003cp\u003eNine prospective studies were found to be eligible for inclusion, comparing SPECT-derived MFR to its PET counterpart, with a total of 240 participants. Key characteristics of the included studies can be seen in Table 3.a. and 3.b. [4-6, 9, 21, 40-43]\u003c/p\u003e\n\u003cp\u003eAll studies used a 1-day protocol starting with the rest acquisition, apart from 2 studies with stress first. AC for the SPECT imaging was used in 4 out of 9 studies (44,4%), while all studies used AC for PET-MPI. [⁹⁹ᵐTc]sestamibi and [⁹⁹ᵐTc]tetrofosmin were utilised as a single-photon radio-tracer with varying activity, while [\u0026sup1;\u0026sup3;N]ammonia, [⁸\u0026sup2;Rb]rubidium chloride and [\u0026sup1;⁵O]water were used for PET imaging. Adenosine, dipyridamole or regadenoson was used to induce hyperaemia. Both CZT and NaI detector SPECT devices were used for imaging.\u003c/p\u003e\n\u003cp\u003eWhen a cut-off point was used, PET-MFR was considered abnormal when \u0026lt;2.0 in most cases. The optimal cut-off point of SPECT-MFR for detecting abnormal MFR on PET based on ROC analyses was reported in 6 studies with gMFR varying from 1.26 to 2.5, with AUC values of 0.75-0.96, 68-89% sensitivity and 73-100% specificity on the patient level. In comparison, the ROC analyses revealed an optimal cut-off value for rMFR between 1.79 and 1.94 with a consequent 78-84% and 63-80% sensitivity and specificity, respectively, on the vessel level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEight of the nine (88,89%) included studies reported a significant correlation between PET-MFR and SPECT-MFR. One study by de Souza et al. found a poor and non-significant correlation between PET-MPI and SPECT results\u0026nbsp;[40]. Giubbini et al. measured regional SPECT-MFR both with and without AC and reported significantly better diagnostic accuracy when AC was applied [43]. However, Wells et al. tested motion correction (MC), AC and correction for tracer red blood cell binding (BB) and reported the highest global AUC values (0.975) in the case of -BB,-AC,+MC to predict abnormal PET-MFR. +BB,-AC,+MC resulted in the highest regional AUC value (0.866)\u0026nbsp;[9].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.3.1. Results of the meta-analysis\u003c/h2\u003e\n\u003cp\u003eSix out of nine studies reported cut-off, sensitivity and specificity values, enabling the extraction of TP, FP, FN, and TN values, allowing inclusion in the meta-analysis ([4, 9, 21, 41-43]). Extracted patient characteristics and the individual study results are shown in Table 4. Analysed studies enrolled a total of 180 participants. The sensitivities and specificities of the included studies are summarised in Figure 3 and 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo evaluate the diagnostic performance of SPECT-MPI-derived MFR in detecting PET-MPI-derived MFR \u0026le;2.0, a pooled analysis of diagnostic value was conducted, resulting in a sensitivity of 0.785 (95% CI: 0.717-0.841) and a specificity of 0.893 (95% CI: 0.704-0.967) as shown on a summary ROC (sROC) curve (Figure 5.).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePooled DOR was 15.7 (95% CI: 6.270-39.269). PLR and NLR were 7.3 (95% CI: 2.385-22.418) and 0.24 (95% CI: 0.174-0.331), respectively, as summarised in Figures 6, 7, and 8.\u003c/p\u003e\n\u003cp\u003eThe analysis revealed moderate between-study heterogeneity for DOR (\u0026tau;\u0026sup2; = 0.4; Cochran\u0026rsquo;s Q = 7.32, df = 5, p = 0.198; I\u0026sup2; = 31.7%) and sensitivity (\u0026tau;\u0026sup2; = 0.2; Cochran\u0026rsquo;s Q = 95.5, df = 5, p \u0026lt; 0.001). We observed substantial heterogeneity for specificity (\u0026tau;\u0026sup2; = 2.4; Cochran\u0026rsquo;s Q = 5590, df = 5, p \u0026lt; 0.001). These findings support the application of a random-effects model in the meta-analysis.\u003c/p\u003e\n\u003ch2\u003e3.4. Predictive value of SPECT-derived MFR\u003c/h2\u003e\n\u003cp\u003eFour studies were found to evaluate the ability of SPECT-derived MFR to predict the occurrence of MACEs based on a median of at least 15 months of follow-up. The main characteristics of the included studies are summarised in Table 5.a. and 5.b. [8, 44-46]\u003c/p\u003e\n\u003cp\u003eFollow-up was done by telephone and hospital history records. The primary endpoint in the included studies was the occurrence of a MACE, defined as one of the following: cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, heart failure, late or unplanned coronary revascularisation, or hospitalisation for unstable angina. Both CZT and NaI detector SPECT devices were used for SPECT imaging, either with 1- or 2-day protocols. Injected activities varied between studies. Abnormal global MFR cut-off value was pre-defined in three studies as \u0026lt;2.0, while Zhang et al. calculated an optimal threshold of \u0026lt;2.52 based on the ROC analysis [45]. In all studies, Kaplan-Meier curves showed significantly worse event-free survival rates for patients with impaired MFR. In multivariate analysis, MFR remained an independent predictor of MACEs, while semi-quantitative SPECT-MPI results did not, as reported by Sun et al.\u0026nbsp;[46].\u003c/p\u003e\n\u003cp\u003eLi et al. enrolled 303 patients with available ICA results, sorting participants into obstructive CAD (OCAD) and ischaemia with non-obstructed coronary arteries (INOCA) groups. The two groups did not differ significantly in MACE probability after 16-21 months of follow-up, while patients with impaired MFR were reported to have a four-fold increased event rate in both groups compared to patients with normal MFR\u0026nbsp;[44].\u003c/p\u003e\n\u003cp\u003eZhang et al. included 118 INOCA patients and reported a sensitivity of 84.2% and a specificity of 77.8% (AUC: 0.83) of gMFR \u0026lt;2.52 to predict the onset of MACEs after a median of 15 months [45].\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e In this study, we collected and reviewed all currently available original articles comparing the results of quantitative SPECT-MPI, specifically MFR, to a well-established reference standard to assess its diagnostic accuracy and predictive value. In addition, a targeted meta-analysis was performed on the subset of studies comparing SPECT-MPI to PET-MPI, the established reference standard for myocardial perfusion imaging.\u003c/p\u003e\u003cp\u003eMFR is based on the quantitation of stress and rest MBF; as the ratio of the two, it reflects the ability of the myocardial vasculature to adapt to an increased blood flow demand. Absolute quantitation of myocardial blood flow has several advantages compared to conventional semi-quantitative MPI, with the theoretical superiority in diagnosing patients with 3-vessel disease or diffuse microvascular dysfunction and is a more objective and repeatable way of assessing myocardial perfusion [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Wells et al. 2023 proved the repeatability of SPECT MBF measurement across sites and readers in a multi-centric study[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough it is reported to work better than sMBF alone, MFR has its known disadvantages. For one, its value is highly dependent on rMBF, which primarily depends on patient characteristics and poorly reflects the severity of CAD[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Quantitative SPECT-MPI vs. ICA\u003c/h2\u003e\u003cp\u003eICA is considered a reliable examination and the last step in the diagnostic management of patients with suspected CAD. Therefore, most of the studies used ICA as a comparator, thus assessing the ability of quantitative SPECT-MPI to non-invasively predict significant or flow-limiting stenosis on ICA and help patient management.\u003c/p\u003e\u003cp\u003eHowever, it should be noted that ICA routinely only evaluates the status of the main coronary arteries, may they be measured by stenosis extent or FFR, and do not directly assess microvascular function or myocardial perfusion. Patients without macroscopic stenosis or abnormal FFR in the main coronary arteries suffering from ischaemic symptoms are considered INOCA. These symptoms may be caused by microvascular dysfunction or vasospasm, resulting in lowered perfusion parameters [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. When using ICA as a gold standard, these patients might be falsely evaluated as false-positive on MPI, inadequately lowering specificity[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. When a stenosis (especially on a distal segment) on a coronary artery is caused by a plaque built over the years, myocardial perfusion might not be impaired because of adaptational mechanisms like angiogenesis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. These cases might appear falsely as false negatives on MPI, wrongly lowering sensitivity.\u003c/p\u003e\u003cp\u003eDespite this limitation, 19/20 (95%) of the included studies found a significant correlation between MFR and ICA results and reported sufficient sensitivity and specificity with excellent AUC values. The study of Djaileb et al. is unique as they assessed both epicardial coronary and microvascular function in their comparison [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Considering this, their results should be regarded as highly relevant despite the relatively low participant number.\u003c/p\u003e\u003cp\u003eIn 2021, Panjer et al. conducted a meta-analysis including nine studies. They reported a sensitivity of 79% and a specificity of 85% of MFR for predicting abnormal FFR on ICA or \u0026lt;\u0026thinsp;2.0 MFR on PET-MPI [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Some studies in our ICA group assessed semi-quantitative SPECT results and reported significantly lower correlations and diagnostic values than quantitative MFR. Consequently, quantitative SPECT-MPI may be superior to the conventional semi-quantitative technique and is a valuable tool in the diagnostic management of CAD. Various authors reported the best diagnostic results and AUC values when they combined MFR with conventional semi-quantitative SPECT-MPI parameters, suggesting that the two techniques should be utilised together[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Quantitative SPECT-MPI vs. PET-MPI\u003c/h2\u003e\u003cp\u003eAlthough conventional PET tracers ([\u0026sup1;\u0026sup3;N]ammonia, [\u0026sup1;⁵O]water and [⁸\u0026sup2;Rb]rubidium chloride) differ in their pharmacokinetics from each other, PET-MPI is a well-established test to assess and quantify myocardial perfusion regardless of the radiotracer used[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. PET-MPI has several advantages over SPECT-MPI and is generally considered a gold standard [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Because of their pharmacokinetics, PET radiotracers reflect on myocardial perfusion better than SPECT tracers, as their K\u003csub\u003e1\u003c/sub\u003e value is further from being linearly proportional to MBF. Without corrections, [⁹⁹ᵐTc]sestamibi and [⁹⁹ᵐTc]tetrofosmin, most commonly used for SPECT-MPI, tend to underestimate MBF, especially in higher flow values [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, a suitable conversion function, such as the Renkin-Crone equation, among other corrections, must be used [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe pooled PLR (7.3) and NLR (0.24) from our meta-analysis indicate that SPECT-derived MFR demonstrates a moderate capacity to confirm the presence of disease and to exclude cases deemed negative by PET-MPI, respectively. The DOR of 15.6 further supports the test\u0026rsquo;s moderate overall diagnostic performance. These findings underscore the clinical utility of MFR and suggest that, despite heterogeneity among the included studies, quantitative SPECT-MPI yields diagnostic performance comparable to that of PET-MPI. Although SPECT-MPI exhibits moderately lower diagnostic accuracy relative to PET-MPI, it remains a viable and more widely accessible alternative for clinical application.\u003c/p\u003e\u003cp\u003eDespite the fact that the use of AC in PET-MPI is widely accepted, its clinical benefit in SPECT-MPI remains uncertain. Giubbini et al. analysed regional data with and without AC and reported a significantly better regional AUC when AC was applied [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], while Bailly et al. and Wells et al. did not prove the advantage of AC application [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn theory, with the morphological data obtained by CT, AC would resolve the heterogeneity of the thoracic attenuation, thus increasing precision for measuring MBF. However, myocardial TACs are a yield not only of the tracer's K\u003csub\u003e1\u003c/sub\u003e but also of the arterial input function. Since the attenuation of the arterial input function is, on average, similar to that of the myocardium, this effect is mostly cancelled out on the global scale. When assessing regional MBF, AC might increase precision. However, because MFR is defined as the ratio of sMBF to rMBF, regional alterations in attenuation may be nullified during this division, thereby diminishing the potential benefits of AC. In addition, AC can increase image noise, partially offsetting the improvements achieved by reducing regional attenuation heterogeneity; consequently, regional ROC analyses may not consistently demonstrate a significant advantage for AC.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eImage processing softwares used for PET-MPI has an automatic MC tool, while MC is mostly done manually for SPECT-MPI. This holds a possibility of error, thus lowering accuracy and increasing intra- and inter-observer variability [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBased on the results of our meta-analysis, SPECT is sufficiently capable of assessing quantitative perfusion parameters comparable to PET. Although its diagnostic accuracy seems to be slightly lower than PET-MPI, SPECT remains a viable, cost-effective alternative for myocardial perfusion assessment, whereas PET-MPI remains the reference standard.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Clinical application and predictive value of SPECT-derived MFR\u003c/h2\u003e\u003cp\u003eMFR proved to have an excellent prognostic ability in predicting MACEs and could be a valuable tool in the risk stratification of CAD. Li et al. showed a similar MACE probability in the OCAD and INOCA group, with a four-fold increase when MFR was impaired on SPECT. These results indicate that perfusion parameters, such as MFR, have higher predictive potential than macroscopic coronary obstruction as they reflect both microvascular and epicardial vessel function. The authors point out that risk stratification in CAD patients relying solely on coronary anatomical information is insufficient, and perfusion assessment should also be used [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegardless of whether ICA or PET-MPI was used as a comparator when an ROC analysis was performed in studies, the optimal MFR cut-off point varied in a broad range. This could be explained by the heterogeneous study settings, patient characteristics, administered activity, acquisition protocol, processing software and whether AC or other corrections were applied. Most of the included studies used a 1-day protocol with rest acquisition first, but different settings were also utilised. In the case of the 1-day protocol, a considerable residual activity interferes with the second acquisition. This can partially be neutralised by the larger (usually almost three-fold) secondly injected activity and residual subtraction, but it might still impact the precise quantification of MBF. Quantitative SPECT-MPI, as a key tool in the diagnostic management of CAD, would be adapted more easily to everyday clinical practice with a well-defined diagnostic protocol and broadly accepted optimal MFR cut-off point. Multi-centred studies with united study protocols and large participant numbers are still needed.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eThis systematic review and meta-analysis has several limitations. First, it encompasses studies with considerable heterogeneity in patient populations, study settings, and other parameters, thereby complicating comparisons and impeding comprehensive analyses. Consequently, only a narrower-spectrum meta-analysis was feasible, which may be susceptible to publication bias given that only studies reporting complete, typically positive data were included.\u003c/p\u003e\u003cp\u003eFurthermore, the review inherits the limitations of the included studies, such as small sample sizes, short follow-up periods (in the FU group), and the inherent difficulty of comparing a functional diagnostic test with a morphological one (in the ICA group). Despite our best efforts, some relevant studies may have remained unidentified (e.g., due to language restrictions, limited databases searched, or unpublished \u0026ldquo;grey literature\u0026rdquo; not captured).\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eDynamic SPECT-MPI-derived MFR aligns well with anatomical and functional ICA and PET-MPI findings and may predict cardiac events, supporting its incremental prognostic value and diagnostic accuracy in CAD management. Our meta-analysis highlights the clinical utility of SPECT-derived MFR, suggesting that it may serve as a viable alternative despite PET-MPI remains the gold standard for myocardial perfusion assessment. However, whether AC improves the precision of MBF measurement remains unclear. Large-scale, multicenter studies are needed to refine examination protocols, establish an MFR cut-off, and encourage broader clinical implementation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"435\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003eattenuation correction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003earea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003ered blood cell binding\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003ecoronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eCCTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003ecoronary computed tomography angiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eCFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003ecoronary flow reserve\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003econfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eCZT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003ecadmium-zink-telluride\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eDOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003ediagnostic odds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eFFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003efractional flow reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eFN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003efalse negative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003efalse positive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eFU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003efollow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003egMFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003eglobal myocardial flow reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eICA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003einvasive coronary angiography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eIMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003eindex of microcirculatory resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eINOCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003eischaemia with non-obstructed coronary arteries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eK\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003emyocardial tissue uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003emajor adverse cardiac events\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eMBF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003emyocardial blood flow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eMFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003emyocardial flow reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eMPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003emyocardial perfusion imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003emotion correction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eMPI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003emyocardial perfusion imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eMPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003emyocardial perfusion reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003enegative likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eOCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003eobstructive coronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003ePET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003epositron emission tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003epositive likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003erMBF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003erest myocardial blood flow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003erMFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003eregional myocardial flow reserve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003ereceiver operating characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003esMBF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003estress myocardial blood flow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eSPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003esingle photon emission computed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003esROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003esummary receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eTAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003etime activity curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003etrue negative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.576%;\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74.424%;\"\u003e\n \u003cp\u003etrue positive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThis study is a systematic review and meta-analysis based exclusively on data extracted from previously published studies. All included primary studies received appropriate ethical approval from their respective institutional review boards or ethics committees. As no new data were collected and no individual patient-level data were used, further ethical approval was not required for the conduct of this review.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eThis article does not report any individual person\u0026rsquo;s data in any form (including individual details, images, or videos). Therefore, consent for publication is not applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eDetailed statistical methodology and the corresponding custom Python programming code are available via GitHub repository at:\u003c/p\u003e\n\u003cp\u003ehttps://github.com/SurendranathReddy1993/SPECTMFRMetaAnalysis. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no financial or other conflicts of interest related to this work.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study did not receive any funding or grant from public, commercial, or not-for-profit sources.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the conceptualisation of the study. Methodology was developed by BB, SB, and SSR. Formal analysis was performed by SSR and BB. Investigation, including literature screening and data extraction, was carried out by BB and SB. Data curation was conducted by TG, SL, KN, SB and PMH. The original draft was prepared by BB, SB, and SSR. SL, TG, KN, SB and PMH contributed to the review and editing of the manuscript. TG and PMH provided supervision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank the Medical Imaging Centre, Semmelweis University, for providing institutional support. We also gratefully acknowledge Brigitta Teutsch and Oszk\u0026aacute;r P\u0026aacute;rtos for their valuable methodological support and suggestions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSabharwal, N. and A. Lahiri, \u003cem\u003eRole of myocardial perfusion imaging for risk stratification in suspected or known coronary artery disease.\u003c/em\u003e Heart, 2003. \u003cstrong\u003e89\u003c/strong\u003e(11): p. 1291-1297.\u003c/li\u003e\n\u003cli\u003eAarnoudse, W.H., N.H.J. Botman Kj Fau - Pijls, and N.H. Pijls, \u003cem\u003eFalse-negative myocardial scintigraphy in balanced three-vessel disease, revealed by coronary pressure measurement.\u003c/em\u003e (1462-8848 (Print)).\u003c/li\u003e\n\u003cli\u003eLima, R.S., et al., \u003cem\u003eIncremental value of combined perfusion and function over perfusion alone by gated SPECT myocardial perfusion imaging for detection of severe three-vessel coronary artery disease.\u003c/em\u003e J Am Coll Cardiol, 2003(0735-1097 (Print)).\u003c/li\u003e\n\u003cli\u003eNkoulou, R., et al., \u003cem\u003eAbsolute Myocardial Blood Flow and Flow Reserve Assessed by Gated SPECT with Cadmium-Zinc-Telluride Detectors Using 99mTc-Tetrofosmin: Head-to-Head Comparison with [\u0026sup1;\u0026sup3;N]ammonia PET.\u003c/em\u003e J Nucl Med, 2016. \u003cstrong\u003e57\u003c/strong\u003e(12): p. 1887-1892.\u003c/li\u003e\n\u003cli\u003eNose, N., et al., \u003cem\u003eAssessment of coronary flow reserve using a combination of planar first-pass angiography and myocardial SPECT: Comparison with myocardial (15)O-water PET.\u003c/em\u003e Int J Cardiol, 2016. \u003cstrong\u003e222\u003c/strong\u003e: p. 209-212.\u003c/li\u003e\n\u003cli\u003eShrestha, U., et al., \u003cem\u003eMeasurement of absolute myocardial blood flow in humans using dynamic cardiac SPECT and (99m)Tc-tetrofosmin: Method and validation.\u003c/em\u003e J Nucl Cardiol, 2015. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 268-277.\u003c/li\u003e\n\u003cli\u003eBen Bouallegue, F., et al., \u003cem\u003eSPECT Myocardial Perfusion Reserve in Patients with Multivessel Coronary Disease: Correlation with Angiographic Findings and Invasive Fractional Flow Reserve Measurements.\u003c/em\u003e J Nucl Med, 2015. \u003cstrong\u003e56\u003c/strong\u003e(11): p. 1712-7.\u003c/li\u003e\n\u003cli\u003eDaniele, S., et al., \u003cem\u003eIncremental prognostic value of coronary flow reserve assessed with single-photon emission computed tomography.\u003c/em\u003e J Nucl Cardiol, 2011. \u003cstrong\u003e18\u003c/strong\u003e(4): p. 612-9.\u003c/li\u003e\n\u003cli\u003eWells, R.G., et al., \u003cem\u003eOptimization of SPECT Measurement of Myocardial Blood Flow with Corrections for Attenuation, Motion, and Blood Binding Compared with PET.\u003c/em\u003e J Nucl Med, 2017. \u003cstrong\u003e58\u003c/strong\u003e(12): p. 2013-2019.\u003c/li\u003e\n\u003cli\u003eMurthy, V.L., et al., \u003cem\u003eImproved cardiac risk assessment with noninvasive measures of coronary flow reserve.\u003c/em\u003e Circulation, 2011. \u003cstrong\u003e124\u003c/strong\u003e(20): p. 2215-24.\u003c/li\u003e\n\u003cli\u003eCamici, P.G. and F. 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A systematic review and meta-analysis.\u003c/em\u003e J Nucl Cardiol, 2022. \u003cstrong\u003e29\u003c/strong\u003e(4): p. 1686-1697.\u003c/li\u003e\n\u003cli\u003eDriessen, R.S., et al., \u003cem\u003eMyocardial perfusion imaging with PET.\u003c/em\u003e The international journal of cardiovascular imaging, 2017. \u003cstrong\u003e33\u003c/strong\u003e: p. 1021-1031.\u003c/li\u003e\n\u003cli\u003eQayyum, A.A. and J. Kastrup, \u003cem\u003eMeasuring myocardial perfusion: the role of PET, MRI and CT.\u003c/em\u003e Clin Radiol, 2015. \u003cstrong\u003e70\u003c/strong\u003e(6): p. 576-84.\u003c/li\u003e\n\u003cli\u003eCuddy-Walsh, S.G., et al., \u003cem\u003eImproved precision of SPECT myocardial blood flow using a net tracer retention model.\u003c/em\u003e Med Phys, 2023. \u003cstrong\u003e50\u003c/strong\u003e(4): p. 2009-2021.\u003c/li\u003e\n\u003cli\u003eCichocki, P., et al., \u003cem\u003eInter- and Intraobserver Repeatability of Myocardial Flow Reserve Values Determined with SPECT Study Using a Discovery NM530c Camera and Corridor 4DM Software.\u003c/em\u003e J Pers Med, 2021. \u003cstrong\u003e11\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003ede Souza, A., et al., \u003cem\u003eQuantification of myocardial flow reserve using a gamma camera with solid-state cadmium-zinc-telluride detectors: Relation to angiographic coronary artery disease.\u003c/em\u003e J Nucl Cardiol, 2021. \u003cstrong\u003e28\u003c/strong\u003e(3): p. 876-884.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 99.8445%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Inclusion criteria\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 387px;\"\u003e\n \u003cp\u003eSystematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMeta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026gt;18 yo. patients with known or suspected CAD, both genders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026gt;18 yo. patients with known or suspected CAD, both genders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eQuantitative SPECT-MPI with the assessment of MFR calculated as sMBF/rMBF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eQuantitative SPECT MPI with the assessment of MFR calculated as sMBF/rMBF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eComparator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eICA, PET-MPI, CCTA or FU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003ePET-MPI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eOutcome and other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003col\u003e\n \u003cli\u003eICA: diagnostic value of MFR and/or its correlation with stenosis extent and/or FFR assessed by ICA or other ICA-derived composite endpoint\u003c/li\u003e\n \u003cli\u003ePET-MPI: diagnostic value of MFR and/or its correlation with PET-MPI-derived MFR\u003c/li\u003e\n \u003cli\u003eCCTA: diagnostic value of MFR and/or its correlation with stenosis extent assessed by CCTA\u003c/li\u003e\n \u003cli\u003eFU: predictive value of MFR for the occurance of MACEs\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003col\u003e\n \u003cli\u003ePET-MPI: diagnostic value of MFR to predict PET-MPI-derived MFR \u0026gt;2.0\u003c/li\u003e\n \u003cli\u003eTP, FP, FN, TN values reported\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 643px;\"\u003e\n \u003cp\u003eCAD = coronary artery disease, SPECT-MPI = single photon emission computed tomography myocardial perfusion imaging, MFR = myocardial flow reserve, sMBF = stress myocardial blood flow, rMBF = rest myocardial blood flow, ICA = invasive coronary angiography, PET-MPI = positron emission tomography myocardial perfusion imaging, CCTA = coronary computed tomography angiography, FU = follow-up, MACE = major adverse cardiac event, TP = true positive, FP = false positive, FN = false negative, TN = true negative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"1026\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 869px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.a\u003c/strong\u003e. General characteristics of included studies, ICA group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eYear of Publication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eParticipants in the Final Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePatient Enrollment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eGamma-camera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eProcessing software\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eRadio-tracer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAcquisition protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHyperaemic Pharmaceutical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eInjected Activity (MBq)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eScout for positioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eFirst injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eSecond injection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eBai et al. [22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eretrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSymbia T16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMyoFlowQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eDja\u0026iuml;leb et al. [23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (stress first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003edipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e291 \u0026plusmn; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e875 \u0026plusmn; 180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eZhang et al. [24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMyoFlowQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e370-555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e370-555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eFang et al. [25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eretrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQPS, Cedars-Sinai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eboth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eKawaguchi et al.[26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eretrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]tetrofosmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (stress first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.3/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e9/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eL. Wang et al.[27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSymbia T16 and Symbia T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQPS, Cedars-Sinai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eLiu et al.[28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eretrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor 4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003edipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eBailly et al.[29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor 4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]tetrofosmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (stress first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eregadenoson or dipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eJ. Wang et al.[30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMyoFlowQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eboth (rest first when 1-day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1-day: 18.5-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e185-296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e555-888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2-day: 18.5-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e185-296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e185-296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eChen et al.[31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eIntevo Excel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMyoFlowQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003edipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eLi et al.[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor 4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePang et al.[33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMyoFlowQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eboth (rest first when 1-day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1-day: 18.5\u0026ndash;37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e185\u0026ndash;296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3x(185\u0026ndash;296)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2-day: 18.5\u0026ndash;37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e370\u0026ndash;555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e370\u0026ndash;555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eZavadovsky et al.[34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM/CT 570c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor 4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e245.3\u0026plusmn;42.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e736\u0026plusmn;127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAcampa et al.[35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor 4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003edipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eShiraishi et al.[36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eretrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eAZE VirtualPlace Hayabusa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003csup\u003e201\u003c/sup\u003eTl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (stress first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e50-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e50-60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ede Souza et al.[57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003edipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eIguchi et al.[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e208\u0026plusmn;23 (3,7/kg)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e762\u0026plusmn;53 (11.1/kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eHan et al.[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003estress: \u003csup\u003e201\u003c/sup\u003eTl,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003erest: [⁹⁹ᵐTc]tetrofosmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e50.3\u0026plusmn;6.3 (\u003csup\u003e201\u003c/sup\u003eTl)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e261.2\u0026plusmn;41.7 (\u003csup\u003e99m\u003c/sup\u003eTc)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAgostini et al. [21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eregadenoson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e9/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eBouallegue et al. [7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]tetrofosmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1-day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003edipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e35-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e185-220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e645+730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"999\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 999px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.b. Results of the included studies, ICA group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eReference Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eOptimal MFR cut-off (global)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eAUC value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eOptimal MFR cut-off (regional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAUC value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBai et al.[22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% or \u0026ge;75% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 2.0 \u0026nbsp;\u0026ge;75%: 1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 0.939 \u0026ge;75%: 0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 87.23% \u0026ge;75%: 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 89.13% \u0026ge;75%: 72.73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 1.95 \u0026ge;75%: 1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 0.933 \u0026ge;75%: 0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 95.15% \u0026ge;75%: 86.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 78.41% \u0026ge;75%: 85.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDja\u0026iuml;leb et al.[23]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003epositive composite criterion on ICA: CFR: \u0026le;2, IMR: \u0026ge;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eZhang et al. [24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% or \u0026ge;70% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 1.96 \u0026ge;70%: 1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 0.91 \u0026ge;70%: 0,91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 80 \u0026ge;70%: 75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 84-90 \u0026ge;70%: 87-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 1.7 \u0026ge;70%: 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 0.81 \u0026ge;70%: 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 74 \u0026ge;70%: 71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 85 \u0026ge;70%: 70-74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eFang et al. [25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% or \u0026ge;75% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 2.16 \u0026ge;75%: 2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 0.731 \u0026ge;75%: 0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 61.8% \u0026ge;75%: 68.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 86.8% \u0026ge;75%: 76.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eKawaguchi et al.[26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFFR\u0026le;0.8 or \u0026ge;70% stenosis (both counted as significant CAD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eL. Wang et al.[27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e72.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e75.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLiu et al.[28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% stenosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBailly et al.[29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e88.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e84.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e56.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eJ. Wang et al.[30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge; 50% or \u0026ge; 75% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 1.61 \u0026nbsp;\u0026ge;75%: 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 0.82 \u0026nbsp;\u0026ge;75%: 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 84.4 \u0026ge;75%: 78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 88.2 \u0026ge;75%: 63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eChen et al.[31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% or \u0026ge;70% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 70, \u0026ge;70%: 73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 63, \u0026ge;70%: 61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLi et al.[32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFFR\u0026lt;0,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e88.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePang et al.[33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge; 50% or \u0026ge; 75% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 1.77 \u0026ge;75%: 1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 0.772 \u0026ge;75%: 0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 85.7 \u0026ge;75%: 90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 58.6 \u0026ge;75%: 53.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eZavadovsky et al.[34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFFR \u0026le;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e69.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e93.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAcampa et al.[35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;70% stenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eShiraishi et al.[36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% (LM) or \u0026ge;70% (LAD, LCX, RCA) or \u0026le;0.8 FFR (any vessel)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ede Souza et al.[57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;70% stenosis in any major coronary artery or \u0026ge;50% in LM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e63.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e74.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eIguchi et al.[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eGensini score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eHan et al.[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFFR \u0026le;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAgostini et al.[21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFFR \u0026le;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBouallegue et al.[7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026ge;50% stenosis or FFR \u0026le;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 0.85\u003cbr\u003e\u0026nbsp;\u0026le;0.8 FFR: 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 80\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u0026le;0.8 FFR: 89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ge;50%: 85\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u0026le;0.8 FFR: 82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 999px;\"\u003e\n \u003cp\u003eMFR = myocardial flow reserve, AUC = area under the curve, ICA = invasive coronary angiography, CFR = coronary flow reserve, IMR = index of microvasculatory resistance, FFR = fractonal flow reserve, LM = left main, LAD = left anterior descending, RCA = right coronary artery, LCX = left circumflex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"1016\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100%;\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 3. a.\u003c/strong\u003e General Characteristics of included studies, PET-MPI group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"11\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eStudies included in the systematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eStudies included in the meta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eFirst Author\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eYear of Publication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eNumber of Participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003ePatient Enrollment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eGamma-camera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eProcessing software\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAC for SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003ePET radio-tracer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSPECT radio-tracer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eInjected Activity (MBq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAcquisition protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eHyperaemic Pharmaceutical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eScout injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eFirst injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSecond Injection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eYamamoto et al. [41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[\u0026sup1;\u0026sup3;N]ammonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eAcampa et al.[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eQGS/QPS, Cedars-Sinai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[⁸\u0026sup2;Rb]rubidium chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003edipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGiubbini et al.[43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eyes and no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[\u0026sup1;\u0026sup3;N]ammonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]tetrofosmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (stress first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eregadenoson\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eAgostini et al.[21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[\u0026sup1;⁵O]water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eregadenoson\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eWells et al.[9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eDiscovery NM530c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eFlowQuant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eyes and no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[⁸\u0026sup2;Rb]rubidium chloride and [\u0026sup1;\u0026sup3;N]ammonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]tetrofosmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e316\u0026plusmn;71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1,122\u0026plusmn;\u003c/p\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003edipyridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eNkoulou et al.[4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eDiscovery NM570c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eMyovation for Alcyone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[\u0026sup1;\u0026sup3;N]ammonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]tetrofosmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e330\u0026plusmn;33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e990\u0026plusmn;\u003c/p\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (stress first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003ede Souza et al.[40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eD-SPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eCorridor4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[\u0026sup1;\u0026sup3;N]ammonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eregadenoson\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eNose et al.[5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eMillennium VG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[\u0026sup1;⁵O]water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eShrestha et al.[6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eInfinia Hawkeye 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e[\u0026sup1;\u0026sup3;N]ammonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]tetrofosmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eregadenoson\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"882\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99.8783%;\" colspan=\"13\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 3. b.\u003c/strong\u003e Results of the included studies, PET-MPI group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eStudies included in the systematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eStudies included in the meta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eFirst Author\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eYear of Publication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eReference Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eMFR cut-off (global)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eAUC value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eMFR cut-off (regional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAUC value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eYamamoto et al.[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR \u0026lt;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAcampa et al.[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR \u0026lt;2.0 for global\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGiubbini et al.[43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR \u0026lt;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eNAC: 1.94; AC: 1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNAC: 0.767 ; AC: 0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNAC: 81; AC: 84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNAC: 64; AC: 63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAgostini et al.[21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR \u0026lt;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eWells et al.[9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR \u0026lt;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.15-1.53 (resulting in best AUC: 1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.866-0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e78-100 (resulting in best AUC: 89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e70-100\u003c/p\u003e\n \u003cp\u003e(resulting in best AUC: 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e1.64-2.26 (resulting in best AUC: 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.770-0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e65-91 (resulting in best AUC: 78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e66-86 (resulting in best AUC: 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNkoulou et al.[4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR \u0026lt;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ede Souza et al.[40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNose et al.[5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eShrestha et al.[6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePET MFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" valign=\"top\" style=\"width: 882px;\"\u003e\n \u003cp\u003eMFR = myocardial flow reserve, AUC = area under the curve, NAC= non-attenuation corrected, AC = attenuation corrected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"729\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" style=\"width: 729px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4. Extracted data and patient characteristics for the meta-analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of Publication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emedian age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emale (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eobesity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHT (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCT (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eprior CAD (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eprior MI (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eYamamoto et al.[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAcampa et al.[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGiubbini et al.[43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eAgostini et al.[21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eWells et al.[9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003e27,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNkoulou et al.[4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 77px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 29px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 30px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" valign=\"bottom\" style=\"width: 729px;\"\u003e\n \u003cp\u003eTP = true positive, FP = false positive, FN = false negative, TN = true negative, DM = diabetes mellitus, HT = hypertension, HCT = hypercholesterinaemia, BMI = body mass index, CAD = coronary artery disease, MI = myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"945\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" valign=\"top\" style=\"width: 945px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5.a. General characteristics of included studies, FU group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eYear of Publication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eNumber of Participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePatient Enrollment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eGamma-camera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eImage processing software\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSPECT Radio-tracer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eAcquisition protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eHyperaemic Pharmaceutical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 195px;\"\u003e\n \u003cp\u003eInjected Activity (MBq)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eScout injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eFirst injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSecond injection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eLi et al.[44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eretrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eDiscovery NM530c, GE Healthcare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMyoflowQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eadenosine or ATP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e18,5-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e185-296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e555-888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eSun et al.[46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSymbia T16, Siemens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMyoFlowQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eATP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eZhang et al.[45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eretrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eD-SPECT, Spectrum Dynamics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eCorridor 4DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1 day (rest first)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eDaniele et al.[8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eprospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eE.CAM,\u003cbr\u003e\u0026nbsp;Siemens Medical Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e[⁹⁹ᵐTc]sestamibi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2 day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003edypiridamole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" valign=\"top\" style=\"width: 945px;\"\u003e\n \u003cp\u003eAC = attenuation correction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 679px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5.b. Results of included studies, FU group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eAuthors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003ePrimary Endpoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003eFollow-up time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eOptimal MFR cut-off\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eLi et al.[44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e16-21 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026lt;=2,0 (pre-defined)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eSun et al.[46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e1408 days (1297-1666 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026lt;=2,0 (pre-defined)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eZhang et al.[45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e15 months (11-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e2,52 (AUC: 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e84.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e77.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eDaniele et al.[8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eMACE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 2.1 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026lt;=2,0 (pre-defined)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 679px;\"\u003e\n \u003cp\u003eMFR = myocardial flow reserve, MACE = major adverse cardiac event, AUC = area under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Myocardial Perfusion Imaging, Single-Photon Emission-Computed Tomography, Positron-Emission Tomography, Myocardial Blood Flow, Coronary Artery Disease, Coronary Angiography, Meta-Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7425841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7425841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Myocardial flow reserve (MFR), derived from dynamic single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), offers quantitative insight into coronary physiology and may overcome limitations of conventional semi-quantitative SPECT. We aimed to systematically review and meta-analyse the diagnostic accuracy and prognostic value of SPECT-derived MFR in comparison with invasive coronary angiography (ICA), PET-MPI, and long-term patient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A comprehensive literature search was done in scientific databases for studies comparing SPECT-derived MFR in patients with known or suspected coronary artery disease to ICA, PET-MPI, or ≥12-month follow-up for major adverse cardiac events. A meta-analysis was conducted using random-effects models for studies comparing SPECT-MFR with PET-MPI, and reporting diagnostic performance metrics including sensitivity and specificity. Thirty-two studies were included (n = 19\u003cstrong\u003e \u003c/strong\u003efor ICA; n = 8 for PET-MPI; 1 for both ICA and PET-MPI and n = 4 for follow-up). Thirty studies showed a significant correlation between SPECT-derived MFR and reference standards with excellent area under the curve values (AUC\u0026gt;0.7) reported. Six PET-MPI comparator studies (with a total number of participants, n = 180) were included in the meta-analysis, yielding a pooled sensitivity of 78.5% (95% CI: 71.7-84.1%) and specificity of 89.3% (95% CI: 70.4-96.7%) (diagnostic odds ratio = 15.7 (95% CI: 6.270-39.269)). MFR consistently predicted major adverse cardiac events in prognostic studies, independent of obstructive coronary status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eQuantitative MFR derived from dynamic SPECT-MPI correlates well with established diagnostic reference tests and independently predicts adverse outcomes. While PET remains the reference standard, SPECT-MPI offers a viable and more accessible alternative. Standardised protocols and large-scale prospective validation are needed to optimise its clinical implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePROSPERO Registration:\u003c/strong\u003e CRD42024507703\u003c/p\u003e","manuscriptTitle":"Diagnostic and Prognostic Value of Myocardial Flow Reserve Quantification with Single Photon Emission Computed Tomography – a Systematic Review and Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 09:35:46","doi":"10.21203/rs.3.rs-7425841/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revision","date":"2025-09-15T09:23:11+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-08-29T10:41:45+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-29T08:39:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-25T14:09:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"EJNMMI Research","date":"2025-08-24T10:57:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3684ab33-72dc-4d4c-ac6c-1170a4011c32","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:01:23+00:00","versionOfRecord":{"articleIdentity":"rs-7425841","link":"https://doi.org/10.1186/s13550-025-01335-2","journal":{"identity":"ejnmmi-research","isVorOnly":false,"title":"EJNMMI Research"},"publishedOn":"2025-11-28 15:57:37","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-09-09 09:35:46","video":"","vorDoi":"10.1186/s13550-025-01335-2","vorDoiUrl":"https://doi.org/10.1186/s13550-025-01335-2","workflowStages":[]},"version":"v1","identity":"rs-7425841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7425841","identity":"rs-7425841","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}