Efficacy of coronary computed tomography angiography and its fractional flow reserve in predicting myocardial ischemia in patients with obstructive coronary artery disease with positron emission tomography myocardial perfusion imaging as a reference standard

Introduction

Coronary artery disease (CAD) is one of the leading causes of death worldwide, with obstructive CAD being the most common clinical phenotype. The presence or absence of significant myocardial ischemia is an important reference for the decision to aggressively revascularize patients with obstructive CAD (1). Therefore, early identification and accurate assessment of the degree of risk of myocardial ischemia in patients with obstructive CAD is the key to their precise treatment. The criteria for coronary revascularization are also gradually shifting from a single anatomical assessment of stenosis to additional pathophysiological and functional judgments, which may be highly beneficial to developing individualized treatment plans (2,3). Invasive coronary angiography (CAG) is widely considered to be the gold standard for detecting epicardial coronary artery stenosis. However, the decision to perform revascularization based solely on the degree of stenosis determined by CAG does not consistently improve the patient prognosis (4). Meta-analyses (5) have shown that incorporating physiological indicators of coronary artery stenosis to guide therapeutic decisions significantly improves patient survival. Measurement of coronary fractional flow reserve (FFR) during CAG allows for the accurate assessment of the pathophysiological significance of the stenosis (6,7). Invasive FFR has become the gold standard for determining whether epicardial coronary artery stenosis is ischemic and for guiding decisions concerning revascularization; however, its widespread use in clinical practice has been limited by the invasive nature of the technique—which requires not only the application of vasodilators for stress but also relatively complex operations and expensive examinations—and by the potential for vascular access complications and contrast-associated nephropathy (8). Within this context, noninvasive imaging methods have become critical to diagnosing patients with a low-to-intermediate risk of coronary heart disease (9,10).

Noninvasive diagnostic imaging and evaluation techniques for CAD primarily include echocardiography, coronary computed tomography angiography (CCTA), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and cardiac magnetic resonance. Among these, PET myocardial perfusion imaging (MPI) is considered the gold standard for the noninvasive diagnosis of myocardial ischemia by virtue of its ability to assess myocardial perfusion accurately (11). CCTA is also a noninvasive imaging tool that is commonly used in diagnosing CAD and can clearly show the site and degree of stenosis, along with the plaque composition, burden, and distribution from an anatomical point of view. However, CCTA alone cannot directly assess the pathophysiological significance of stenosis (12). Therefore, intelligent computational software based on CCTA analysis of contrast hydrodynamics has been developed that can quantitatively measure coronary artery stenosis, further simulate coronary artery congestion and dilatation in the absence of vasodilator stress conditions, and quantitatively compute the FFR based on CCTA [i.e., computed tomography-derived fractional flow reserve (CT-FFR)]; in this way, it can obtain the hemodynamic information of the lesion and accurately identify ischemic lesions (13). Although a few studies have examined the diagnostic efficacy of CT-FFR (9,14) and large multicenter prospective trials—such as PACIFIC I/II and EVINCI study—have validated multimodality imaging approaches including CCTA, SPECT, and PET using invasive FFR as the reference standard (15-17), these studies have mainly focused on the hemodynamic significance of epicardial coronary lesions.

In contrast, our study used PET-MPI parameters as the reference standard, enabling an examination from a myocardial-perfusion perspective as to how the degree of anatomic stenosis assessed by CCTA and the hemodynamic changes derived from CT-FFR relate to myocardial perfusion abnormalities. This method could facilitate integrating the similarities and differences between vascular anatomy, hemodynamics, and functional ischemia. Specifically. we aimed to investigate the efficacy of visual and quantitative analyses of coronary artery stenosis and CT-FFR in diagnosing myocardial ischemia and in patients with obstructive CAD with PET-MPI serving as the reference standard. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-313/rc).

Methods

Study population

A retrospective study was conducted on the data of patients with suspected or confirmed obstructive CAD who underwent PET/computed tomography (CT) MPI between September 2022 and June 2024 at TEDA International Cardiovascular Hospital. The inclusion criteria were as follows: (I) clinically confirmed or suspected obstructive CAD; (II) CCTA data collected within 3 months before and after PET-MPI examination and no history of coronary reconstruction therapy during this period; and (III) no contraindication to adenosine stress and intolerance to the PET-MPI acquisition process. Meanwhile, the exclusion criteria were as follows: (I) a clear history of myocardial infarction; (II) a history of previous coronary revascularization; (III) suspected or confirmed myocarditis, cardiomyopathy, or valvular heart disease; (IV) an anomalous origin of the coronary artery; and (V) missing data. Patients’ baseline data, including gender, age, body mass index (BMI), medical history, smoking history, alcohol consumption history, and family history, were collected through an electronic medical record information system. Ultimately, 86 patients, comprising 49 (57.0%) males and 37 (43.0%) females, aged 34–80 (59.5±11.54) years, were finally included. A total of 241 coronary arteries were included in the study at the vessel level, and 17 vessels with myocardial bridges were excluded from this study to prevent cases of myocardial ischemia due to myocardial bridges from affecting the analysis results (18) (Figure 1). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Ethics Committee of TEDA International Cardiovascular Hospital (No. 2022-0429-1). Informed consent was obtained from all patients.

Figure 1 Flowchart of patient inclusion. CCTA, coronary computed tomography angiography; MPI, myocardial perfusion imaging; PET, positron emission tomography.

PET-MPI procedure

All patients discontinued relevant cardiovascular medications such as vasoactive drugs, beta-blockers, calcium antagonists, nitrates, adenosine, theophyllines, nicorandil, and dipyridamole prior to PET-MPI; they additionally also avoided tea, coffee, or caffeine-containing beverages for at least 24 hours. The imaging agent ¹³N-NH₃·H₂O was produced by our medical cyclotron (MINItrace Qilin, GE HealthCare, Chicago, IL, USA), and the imaging equipment was a Discovery Elite PET/CT (NM690; GE HealthCare). One-day rest/adenosine stress dynamic imaging was used, with an interval of at least 40 min between the two injections, and an intravenous access was pre-established in both upper arms and connected to a three-way tube. After CT location, low-dose CT attenuation correction scanning was carried out (tube voltage of 120 kV and tube current of 20 mA), and then PET/CT dynamic acquisition was performed. The imaging agent was rapidly injected via a three-way tube at rest or at the peak of stress, followed immediately by a 10 mL of saline flush. PET acquisition was prestarted 10–15 s before the injection of the imaging agent, and the conditions of dynamic acquisition were set as follows: 10 s/frame × 12 frames, 30 s/frame × 2 frames, 60 s/frame × 1 frame, and 360 s/frame × 1 frame. The rest or stress-gated tomography acquisition (acquisition of 8 frames for each cardiac cycle for 8 min) was carried out after the end of the dynamic acquisition. After the acquisition, all acquired data were transferred to the QPET workstation (Cedars-Sinai Medical Center, Los Angeles, CA, USA) and processed by a physician with more than 3 years of experience. After processing, we obtained tomographic reconstructed horizontal long-axis, vertical long-axis, and short-axis images of the left ventricle, as well as 17-segment target heart maps according to the American Heart Association (AHA) standards and semiquantitative parameters of each vascular branching region [including summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS)]. The semiquantitative parameter values of SSS ≥4 or SDS ≥2 were used as the cutoffs for diagnosing myocardial ischemia (19,20).

CCTA procedure

The contrast agent iohexol (containing iodine at a concentration of 350 mg/mL) was injected with a high-pressure syringe (ulrich medical, Ulm, Germany), and the scanning equipment was a Revolution 256-row wide-body detector CT scanner (GE HealthCare). The scanning parameters included scanning voltage of 100 kV and automatic milliamperage technique (400–740 mA), a layer thickness of 0.625 mm, a collimator width of 16 cm, a matrix of 512×512, a display field of view (FOV) of 25 cm, an adaptive statistical iterative reconstruction-V iterative algorithm of 50%, and use of the preset auto-gating to automatically set the milliamperage value and the scanning period phase. After scanning, the device automatically calculated and reconstructed the optimal phase. If the optimal phase was unsatisfactory, multiple phase reconstructions were performed manually at 5% intervals to select the optimal phase. After the optimal phase images were obtained, a radiologist with more than 3 years of experience performed a visual coronary analysis (VCA) of each vessel’s most significant stenotic lesion and graded it. The degree of luminal stenosis was classified in accordance with the Coronary Artery Disease Reporting and Data System (CAD-RADS) 2.0 standard as follows (21): no or minimal stenosis (0–24%), mild stenosis (25–49%), moderate stenosis (50–69%), and severe stenosis (70–100%). All CCTA raw data were transferred to a quantitative analysis software workstation (uAI Discover-CCTA, CTA Coronary Intelligence Analysis System, United Imaging Healthcare, Shanghai, China), with which a specially trained diagnostician performed quantitative coronary analysis (QCA) of the most stenotic lesion in each vessel. The software automatically provided quantitative results to obtain the percentage of stenosis in the stenotic lesion. CT-FFR computation was performed with a computational fluid dynamics (CFD)-based software platform (uCT-FFR version 1.5, United Imaging Healthcare) (14). This software first simulates hemodynamic parameters across the entire coronary tree using CFD and then applies a deep learning model trained on the CFD-derived data and estimated CT-FFR values. Two conditions of ≥50% or ≥70% of any stenosis site were used as positive QCA criteria for stenosis analysis, respectively (22,23), and CT-FFR ≤0.8 was considered the criterion for a positive diagnosis (24,25).

Statistical analysis

All data were statistically analyzed with IBM SPSS version 29.0 (IBM Corp., Armonk, NY, USA) and RStudio version 2025.05.1+513 (Posit Software, Boston, MA, USA). Normally distributed continuous variables are expressed as the mean ± standard deviation, nonnormally distributed continuous variables are expressed as the median and interquartile range (IQR), and categorical variables are expressed as the frequency and composition ratio (%). Based on the vascular level, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for diagnosing myocardial ischemia were calculated for the degree of stenosis assessed visually by CCTA, quantitatively assessment via software, and CT-FFR, respectively. The semiquantitative parameters of PET-MPI served as the reference standard for myocardial ischemia. Because each patient could contribute up to three coronary vessels to the analysis, generalized estimating equations (GEEs) were applied to compare group differences and account for within-patient correlation among vessels. The area under the curve (AUC) was calculated, and the Youden index was calculated to determine the diagnostic threshold for CT-FFR in this study. The Delong test was used to compare the differences in the AUC between the three groups. The diagnostic consistency between CT-FFR and PET-MPI semiquantitative parameters for identifying myocardial ischemia was evaluated via Cohen’s kappa. A 2×2 contingency table was constructed based on dichotomous classification (ischemic vs. nonischemic), and κ values were interpreted as follows (26): ≤0, poor; 0.01–0.20, slight; 0.21–0.40, fair; 0.41–0.60, moderate; 0.61–0.80, substantial; and ≥0.81, almost perfect. We additionally evaluated the diagnostic efficacy of CT-FFR for detecting myocardial ischemia specifically in the subgroup of intermediate stenotic lesions (stenosis ≥50% and ≤70%). All statistical analyses were considered statistically significant at P<0.05.

Results

Study population

A total of 86 patients with suspected or confirmed obstructive CAD were included in this study, with 49 (57.0%) males and 37 (43.0%) females. The age range was 34–80 (mean 59.5±11.54) years, and the mean BMI was 25.63±3.39 kg/m². Among these patients, 52 (52/86, 60.5%) had a history of hypertension, 22 (22/86, 25.6%) had a history of hyperlipidemia, 15 (15/86, 17.4%) had a history of diabetes mellitus, 28 (28/86, 32.6%) had a history of tobacco use, 7 (7/86, 8.1%) had a history of alcohol use, and 11 (11/86, 12.8%) had a family history of coronary heart disease. A total of 241 vessels were included from the 86 patients (17 vessels with myocardial bridges were excluded), and all of them underwent PET-MPI semiquantitative analysis with VCA and QCA assessment of stenosis on CCTA. According to the VCA results, there were 75 (75/241, 31.1%) vessels with no or minimal stenosis, 58 (58/241, 24.1%) vessels with mild stenosis, 47 (47/241, 19.5%) vessels with moderate stenosis, and 61 (61/241, 25.3%) vessels with severe stenosis. According to the QCA results, there were 87 (87/241, 36.1%) vessels with no or minimal stenosis, 55 (55/241, 22.8%) with mild stenosis, 61 (61/241, 25.3%) with moderate stenosis, and 38 (38/241, 15.8%) with severe stenosis. The number of vessels on CCTA with ≥50% or ≥70% stenosis according to VCA was 108 and 61, respectively, while that according to QCA was 99 and 38, respectively. There were 36 vessels with FFR ≤0.8 and 48 vessels in the area of the positive semiquantitative PET-MPI index. The median diameter stenosis derived from CCTA-based QCA was 42% (range, 0–63%), the median CT-FFR was 0.91 (IQR, 0.84–1.00), and the median SSS and SDS was 0 (IQR, 0–1).

Analysis of PET-MPI semiquantitative parameters as a reference standard for myocardial ischemia based on the vessel level

When ≥50% was used as the narrow positive criterion for CCTA, the diagnostic sensitivity and NPV of CT-FFR was significantly lower than that of VCA and QCA of stenosis (both P values <0.05), while the difference between VCA and QCA was not statistically significant (both P values >0.05); the diagnostic specificity and accuracy of CT-FFR was significantly higher than that of VCA and QCA of stenosis (both P values <0.05), while the difference in diagnostic specificity and accuracy between VCA and QCA was not statistically significant (all P values >0.05). The difference in PPV between CT-FFR, VCA, and QCA was not significant (all P values >0.05) (Table 1).

When ≥70% was used as the criterion for stenosis positivity of CCTA, the diagnostic sensitivity of CT-FFR was significantly lower than that of VCA (P=0.014), while the difference between VCA and QCA and that between CT-FFR and QCA was were significant (all P values >0.05); the specificity of VCA was significantly lower than that of QCA (P=0.002) and CT-FFR (P=0.025), whereas the difference in specificity between the QCA and CT-FFR was not statistically significant (P=0.854); the differences between PPV, NPV, and accuracy were not statistically significant (all P values >0.05) (Table 2).

Receiver operating characteristic (ROC) analysis

The AUCs for VCA ≥50%, VCA ≥70%, QCA, and CT-FFR for predicting myocardial ischemia were 0.701 [95% confidence interval (CI): 0.621–0.782], 0.722 (95% CI: 0.634–0.810), 0.777 (95% CI: 0.699–0.855), and 0.780 (95% CI: 0.702–0.857) (Figure 2).

Figure 2 With PET semiquantitative parameters serving as a reference standard, the AUCs for VCA stenosis ≥70% (shown in red), VCA stenosis ≥50% (shown in blue), QCA of stenosis (shown in green), and CT-FFR (shown in purple) were compared. The AUCs for VCA ≥70%, VCA ≥50%, QCA, and CT-FFR were 0.722, 0.701, 0.777, 0.780, respectively. VCA ≥50% showed a significantly lower AUC than did QCA and CT-FFR (all P values <0.05), while VCA ≥70% showed no significant difference compared to QCA and CT-FFR (all P values >0.05). No significant difference was found between QCA and CT-FFR (P=0.878). AUC, area under the curve; CT-FFR, computed tomography-derived fractional flow reserve; PET, positron emission tomography; QCA, quantitative coronary analysis; ROC, receiver operating characteristic; VCA, visual coronary analysis.

When ≥50% was used as the positive criterion for CCTA stenosis, the AUC of CCTA VCA was significantly lower than that of QCA and CT-FFR (all P values <0.05). In contrast, the difference in AUC between CT-FFR and QCA was not statistically significant (P=0.878). When ≥70% was used as the positive criterion for CCTA stenosis, the difference in AUC between VCA, QCA, and CT-FFR was not statistically significant (all P values >0.05). A typical case is presented in Figure 3.

Figure 3 Typical case of ischemic lesions diagnosed by VCA, QCA, and CT-FFR. (A,B) CCTA showing a severely stenotic lesion (79%) of the LAD (shown in green markers), with both QCA and VCA indicating functional ischemia. (C) CT-FFR measurements showing distal blood flow restriction (CT-FFR =0.74). (D) PET semiquantitative parameters (LAD-SSS =4 and LAD-SDS =4) confirming the presence of myocardial ischemia in the LAD region. (E,F) PET myocardial perfusion imaging (short- and long-axis views and target heart maps) showing partial apical segment hypoperfusion under load, suggesting reversible myocardial ischemia. CCTA, coronary computed tomography angiography; CT-FFR, computed tomography-derived fractional flow reserve; LAD, left anterior descending artery; PET, positron emission tomography; QCA, quantitative coronary analysis; SDS, summed difference score; SSS, summed stress score; VCA, visual coronary analysis.

In the ROC analysis, a CT-FFR diagnostic threshold of 0.855 and a Youden index of 0.449 yielded the highest AUC value.

Cohen’s kappa between CT-FFR and PET-MPI

Based on dichotomous ischemia classification, the agreement between CT-FFR and PET-MPI semiquantitative ischemia was considered fair (κ =0.31; 95% CI: 0.16–0.46; P<0.001). Among 241 vessels, 175 were classified as negative and 18 as positive by both CT-FFR and PET-MPI, 30 were positive on PET and negative on CT-FFR, and 18 were negative on PET and positive on CT-FFR-positive, indicating partial but statistically significant concordance.

Diagnostic efficacy of CT-FFR in predicting intermediate stenotic lesions

Based on the QCA results, 62 vessels were deemed to be intermediate stenotic lesions, and the sensitivity, specificity, NPV, PPV, and accuracy of CT-FFR in predicting myocardial ischemia for intermediate stenotic lesions were 7.7%, 83.7%, 77.4%, 11.1%, and 67.7%, respectively. To illustrate the high specificity of CT-FFR in diagnosing intermediate stenosis, a representative case is shown in Figure 4.

Figure 4 High specificity of CT-FFR in intermediate stenotic lesions. (A,B) Surface reconstruction images of LAD, with both VCA and QCA suggested intermediate stenosis of lesions (62%) (shown in green markers; stenosis measurement of CT-FFR =0.84). (C) PET semiquantitative analysis with SSS =1 and SDS =0, suggesting no ischemia. (D,E) PET myocardial perfusion imaging (short- and long-axis views and target heart maps) showing normal myocardial perfusion with no ischemic evidence. In this case, CCTA indicated intermediate stenosis, which might have been considered an ischemic risk by VCA and QCA; however, the CT-FFR >0.80 indicated no hemodynamically significant ischemia. This finding was further supported by normal PET semiquantitative parameters, verifying the high specificity of CT-FFR in ruling out functional ischemia. CCTA, coronary computed tomography angiography; CT-FFR, computed tomography-derived fractional flow reserve; LAD, left anterior descending artery; PET, positron emission tomography; QCA, quantitative coronary analysis; SDS, summed difference score; SSS, summed stress score; VCA, visual coronary analysis.

Discussion

In this study, we used PET-MPI semiquantitative parameters as a reference standard for myocardial ischemia and found that when stenosis of ≥50% on CCTA was used as a positive criterion, the diagnostic efficacy of CT-FFR and QCA in predicting myocardial ischemia was similar and higher than that of VCA of stenosis; however, the diagnostic specificity and accuracy of CT-FFR was higher than those of QCA. When stenosis of ≥70% on CCTA was used as a positive criterion, the diagnostic efficacy of VCA, QCA, and CT-FFR in predicting myocardial ischemia were similar, but the specificity of CT-FFR and QCA was higher than that of VCA.

This study suggests that as the degree of epicardial coronary artery stenosis increases, the correlation between the degree of stenosis and functional ischemia increases, leading to a consequent increase in the diagnostic efficacy of both VCA and QCA of CCTA. These could effectively predict myocardial ischemia at this time, but the diagnostic specificity and accuracy of CT-FFR in the prediction of myocardial ischemia were higher than those of the VCA and QCA of stenosis, especially in the case of stenosis with a positive diagnostic criterion of ≥50%, which is consistent with the results of previous studies (27).

The conventional CT-FFR threshold of 0.80 was originally derived from invasive FFR, reflecting pressure-based flow limitation. In this study, myocardial ischemia was defined by PET-MPI semiquantitative parameters, which reflect perfusion abnormalities influenced by both epicardial stenosis and microvascular dysfunction. Accordingly, the ROC-derived optimal cutoff of 0.855 in this study likely reflects this physiological difference, as perfusion impairment on PET may occur even when CT-FFR values remain slightly above 0.80.

In intermediate stenotic lesions, CT-FFR demonstrated high specificity but relatively low sensitivity for predicting PET-defined ischemia. This suggests that CT-FFR is valuable for ruling out ischemia but may underestimate perfusion abnormalities related to microvascular dysfunction (28). The Cohen’s kappa further revealed only a fair level of consistency between CT-FFR and PET-MPI (κ =0.31; 95% CI: 0.16–0.46; P<0.001), indicating that while their diagnostic results partially overlap, they are not interchangeable.

The discordance between PET-MPI and CT-FFR findings is physiologically based and may arise from a combination of technical and patient-related factors. In intermediate coronary lesions, CT-FFR demonstrated low sensitivity and a higher proportion of false-negative results, suggesting its limited ability to detect microvascular dysfunction or diffuse subendocardial hypoperfusion. Conversely, in some cases where CT-FFR is positive but PET-MPI indicates no ischemia, adequate myocardial perfusion may be preserved through well-developed collateral circulation, which is not accounted for in CT-FFR computation since it reflects only local pressure-derived flow limitation. In addition, severe coronary calcification, image artifacts, and suboptimal image quality may lead to an underestimation of CT-FFR values, resulting in false-positive interpretations. These physiological and technical factors collectively influence the level of agreement between PET-MPI and CT-FFR and further underscore their complementary roles in the comprehensive assessment of coronary physiology.

Therefore, when CT-FFR measurements are negative, the risk of functional myocardial ischemia due to coronary artery stenosis will be significantly reduced, which will help clinicians formulate individualized treatment plans and effectively reduce unnecessary invasive CAG and FFR measurements, thereby reducing the risk of complications caused by invasive investigations and saving healthcare resources and reducing costs. Especially for patients with low-to-intermediate risk of CAD, CCTA-based CT-FFR can help clinicians effectively identify intermediate stenotic and high-risk lesions, optimize patient management, and prevent excessive and ineffective revascularization (29).

In our study, PET-MPI semiquantitative indices served as the functional reference, allowing for the myocardial perfusion-based assessment of the correspondence between anatomic stenosis and CT-FFR-derived hemodynamic alterations. This integrative approach provides complementary evidence linking structural, hemodynamic, and perfusion-based assessments of ischemia, supporting multimodal integration for a more comprehensive evaluation of coronary physiology.

CCTA has become a commonly used noninvasive imaging modality for the diagnosis of CAD due to its excellent high temporal and spatial resolution, precise anatomical imaging capability, and strong ability to exclude obstructive CAD, especially for patients with a low-to-moderate risk of CAD at the initial stage of diagnosis. In addition, the short imaging time and relatively low cost of CCTA make it particularly suitable for the rapid screening and risk stratification of patients with acute chest pain. However, a single CCTA examination has limitations, such as being less accurate than CAG in determining the degree of luminal stenosis—especially in the presence of significant calcified plaque—and not being able to directly determine the pathophysiological significance of the stenosis (30). With the rapid advancement of technology, CT-FFR emerged and was rapidly adopted for clinical use, eliminating the need for an additional injection of contrast agents and the use of vasodilators such as adenosine to induce coronary artery congestion, assisting in determining the pathophysiological significance of coronary artery stenosis, and providing better guidance for individualized treatment planning in patients with coronary artery lesions (23,27). However, CT-FFR can only be studied for a single vessel and is unable to provide information regarding the overall left ventricular blood flow for the patient as a whole (2).

There are certain limitations to this study that should be acknowledged. First, the sample was relatively small and from a single center. Second, the effect of microcirculatory function on myocardial blood flow was not considered in this study. Finally, the diagnostic performance of CT-FFR and PET-MPI might have been affected by differences in equipment, postprocessing algorithms, and the imaging agent used for PET, while the heterogeneity of the results could have been further exacerbated by the differences in technology between different institutions.

Conclusions

This study analyzed and compared the diagnostic efficacy between the degree of coronary artery stenosis as judged visually from CCTA and the quantitative analysis of stenosis by software and CT-FFR in the prediction of myocardial ischemia. We found that the diagnostic efficacy of CT-FFR and quantitative stenosis analysis was similar and higher than that of visual judgment, with the former showing higher diagnostic specificity for the prediction of myocardial ischemia in intermediate coronary lesions. With the greater degree of epicardial coronary artery stenosis, the correlation between the degree of stenosis and myocardial ischemia increased, as did the diagnostic efficacy of VCA, QCA, and CT-FFR, all which were effective in predicting myocardial ischemia. However, CT-FFR had the highest diagnostic specificity and accuracy in predicting myocardial ischemia.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-313/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-313/dss

Funding: This work was supported by Tianjin Key Medical Discipline (Specialty) Construction Project (grant No. TJYXZDXK-3-035C), Science and Technology Project of the Health Commission of Tianjin Binhai New Area (No. 2023BWKZ004) and Tianjin Binhai New Area Science and Technology Project (grant No. 2023BWKY022).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-313/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of TEDA International Cardiovascular Hospital (No. 2022-0429-1), and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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