Background: The diagnostic accuracy of fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA) (FFR-CT) needs to be further improved despite promising results available in the literature. While an innovative myocardial computed tomographic perfusion (CTP)-derived fractional flow reserve (CTP-FFR) model has been initially established, the feasibility of CTP-FFR to detect coronary artery ischemia in patients with suspected coronary artery disease (CAD) has not been proven. Methods: This retrospective study included 93 patients (a total of 103 vessels) who received CCTA and CTP for suspected CAD. Invasive coronary angiography (ICA) was performed within 2 weeks after CCTA and CTP. CTP-FFR, CCTA (stenosis ≥ 50% and ≥70%), ICA, FFR-CT and CTP were assessed by independent laboratory experts. The diagnostic ability of the CTP-FFR grouped by quantitative coronary angiography (QCA) in mild (30–49%), moderate (50–69%) and severe stenosis (≥70%) was calculated. The effect of calcification of lesions, grouped by FFR on CTP-FFR measurements, was also assessed. Results: On the basis of per-vessel level, the AUCs for CTP-FFR, CTP, FFR-CT and CCTA were 0.953, 0.876, 0.873 and 0.830, respectively (all p < 0.001). The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of CTP-FFR for per-vessel level were 0.87, 0.88, 0.87, 0.85 and 0.89 respectively, compared with 0.87, 0.54, 0.69, 0.61, 0.83 and 0.75, 0.73, 0.74, 0.70, 0.77 for CCTA ≥ 50% and ≥70% stenosis, respectively. On the basis of per-vessel analysis, CTP-FFR had higher specificity, accuracy and AUC compared with CCTA and also higher AUC compared with FFR-CT or CTP (all p < 0.05). The sensitivity and accuracy of CTP-FFR + CTP + FFR-CT were also improved over FFR-CT alone (both p < 0.05). It also had improved specificity compared with FFR-CT or CTP alone (p < 0.01). A strong correlation between CTP-FFR and invasive FFR values was found on per-vessel analysis (Pearson’s correlation coefficient 0.89). The specificity of CTP-FFR was higher in the severe calcification group than in the low calcification group (p < 0.001). Conclusions: A novel CTP-FFR model has promising value to detect myocardial ischemia in CAD, particularly in mild-to-moderate stenotic lesions.

To explore the diagnostic value of cardiac magnetic resonance feature tracking (CMR-FT) divided left atrial (LA) strain in differentiating constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patients with CP (n = 40) and RCM (n = 40), and another 40 normal control group were retrospectively enrolled over a period of 8 years at a tertiary cardiac centre. Left ventricular (LV) and biatrial strain and strain rate (SR) were measured. Atrial strain was used to differentiate between patients with CP and RCM. Then, patients were grouped according to their left ventricular ejection fraction (LVEF), either ≥50% or < 50%. A deeper analysis was done to evaluate the diagnostic value of atrial strain in these subgroups. Receiver operating characteristic curves (ROC) were used to assess the accuracy of myocardial strain based on CMR FT for the differential diagnosis of CP and RCM. LV and LA strain and SR were significantly lower in patients with CP and RCM than those in the normal controls (P < 0.05). LA strain and SR were significantly lower in the RCM group than in the CP group (P < 0.05). In patients with either LVEF≥50% or＜50%, LA strain were lower in the RCM group than in the CP group (P < 0.05). ROC analysis showed that LA stored strain (LA-εs) had a good differential diagnostic value for CP and RCM, with an area under the curve (AUC) of 0.811 and an optimal cutoff value of 6.98%, above this value it tends to develop CP. Further, an excellent differential diagnostic value was found in patients with LVEF＜50%, with an AUC of 0.955. LA strain analysis obtained by CMR-FT provides good differential diagnostic value for distinguishing CP from RCM, especially in patients with LVEF＜50%.

Background/Objectives: This study aimed to establish a CCTA-based radiomics model of perivascular adipose tissue (PCAT) to identify high-risk NCLs in patients with NSTE-ACS, potentially facilitating early risk stratification. Methods: In this prospective cohort of 542 NSTE-ACS patients, pericoronary adipose tissue (PCAT) radiomic features of non-culprit lesions (NCLs) were extracted from baseline coronary CTA. Patients were assigned to training (n = 379) and validation (n = 163) cohorts. Machine learning algorithms were applied to develop a radiomics signature (Rad model) to predict 4-year NCL-related major adverse cardiovascular events (MACEs). A combined clinic-radiomics model was constructed to enhance predictive performance. Additionally, the association between the baseline Rad model and longitudinal non-calcified plaque progression (ΔNCPV%/year) was evaluated in a subcohort (n = 60) with serial CCTA. Results: Over a median 4.0-year follow-up, NCL-related MACE occurred in 84 patients (15.5%). The Rad model (comprising nine features) independently predicted MACE (adjusted hazard ratio, 1.988; 95% CI, 1.753–2.254; p < 0.001). In the validation cohort, the combined model yielded higher discrimination for 4-year MACE than the clinical model alone (AUC, 0.793 vs. 0.703; p < 0.05). In the serial CCTA subgroup, a higher baseline Rad model was significantly associated with annualized non-calcified plaque volume progression (standardized β, 0.477; p < 0.001). Conclusions: A CCTA-based PCAT radiomics model is associated with future NCL-related MACE and accelerated plaque progression in patients with NSTE-ACS. This approach may serve as a non-invasive tool for individualized risk stratification.