Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation

Clinical Applications of Fractional Flow Reserve Derived from Computed Tomography in Coronary Artery Disease

Computer tomography-derived fractional flow reserve (CT-FFR) represents a significant advancement in noninvasive cardiac functional assessment. This technology uses computer simulation and anatomical information from computer tomography of coronary angiogram to calculate the CT-FFR value at each point within the coronary vasculature. These values serve as a critical reference for cardiologists in making informed treatment decisions and planning. Emerging evidence suggests that CT-FFR has the potential to enhance the specificity of computer tomography of coronary angiogram, thereby reducing the need for additional diagnostic examinations such as invasive coronary angiography and cardiac magnetic resonance imaging. This could result in savings in financial cost, time, and resources for both patients and health care providers. However, it is important to note that although CT-FFR holds great promise, there are limitations to this technology. Users should be cautious of common pitfalls associated with its use. A comprehensive understanding of these limitations is essential for effectively applying CT-FFR in clinical practice.

Noninvasive machine-learning models for the detection of lesion-specific ischemia in patients with stable angina with intermediate stenosis severity on coronary CT angiography

This study proposed noninvasive machine-learning models for the detection of lesion-specific ischemia (LSI) in patients with stable angina with intermediate stenosis severity based on coronary computed tomography (CT) angiography. This single-center retrospective study analyzed 76 patients (99 vessels) with stable angina who underwent coronary CT angiography (CCTA) and had intermediate stenosis severity (40-69%) on invasive coronary angiography. LSI, defined as a resting full-cycle ratio < 0.86 or fractional flow reserve ≤ 0.80, was determined in 40 patients (46 vessels) using a hybrid resting full-cycle ratio-fractional flow reserve strategy. The resting full-cycle ratio and/or fractional flow reserve were measured using invasive coronary angiography as references for functional severity indices of coronary stenosis in the machine-learning models. LSI detection models were constructed using noninvasive machine-learning models that predicted the resting full-cycle ratio and fractional flow reserve by feeding machine-learning models with image features extracted from CCTA. The diagnostic performance of the proposed LSI detection models was assessed using a nested 10-fold cross-validation test. The LSI detection models with the highest diagnostic performance achieved an accuracy of 0.88 (95% CI: 0.81, 0.94), sensitivity of 0.78 (95% CI: 0.70, 0.86) and specificity of 0.96 (95% CI: 0.92, 1.00) on a vessel basis and 0.88 (95% CI: 0.81, 0.95), 0.80 (95% CI: 0.70, 0.86) and 0.97 (95% CI: 0.92, 1.00), respectively, on a patient basis. These findings suggest that LSI detection models with features extracted from CCTA can noninvasively detect LSI in patients with stable angina with intermediate stenosis severity.

Diagnostic performance of CCTA and CTP imaging for clinically suspected in-stent restenosis: A meta-analysis

AIMS

The objective of this study is to conduct a meta-analysis to assess the diagnostic performance of Coronary Computed Tomography Angiography (CCTA) and a hybrid approach that incorporates Computed Tomography Perfusion (CTP) in addition to CCTA (CCTA ​+ ​CTP) for the detection of in-stent restenosis (ISR), as defined by angiography.

METHODS

A comprehensive search of articles identified 18,513 studies. After removing duplicates, title/abstract screening, and full-text review, 17 CCTA and 3 CCTA ​+ ​CTP studies were included. Only studies using ≥64-slices multidetector computed tomography (CT) were considered eligible.

RESULTS

The per-patient ISR prevalence was 43 ​%, with 92 ​% of stents fully interpretable with CCTA. Meta-analysis exhibited a per-stent CCTA (n ​= ​2674) sensitivity of 90 ​% (95 ​% CI; 84-94 ​%), specificity of 89 ​% (95 ​% CI; 86-92 ​%), positive likelihood ratio of 7.17 (95 ​% CI; 5.24-9.61), negative likelihood ratio of 0.17 (95 ​% CI; 0.10-0.25), and diagnostic odds ratio of 45.7 (95 ​% CI; 22.71-82.43). Additional sensitivity analyses revealed no influence of stent diameter or strut thickness on the diagnostic yield of CCTA. The per-stent diagnostic performance of CCTA ​+ ​CTP (n ​= ​752) did not show differences compared to CCTA.

CONCLUSIONS

With currently utilized scanners, CCTA and CCTA ​+ ​CTP demonstrated high diagnostic performance for in-stent restenosis evaluation. Consequently, a history of previous stent implantation should not be an argument to preclude using these methods in clinically suspected patients.

Patient Management Following Percutaneous Coronary Intervention

Percutaneous coronary intervention (PCI) is a mainstay procedure for the treatment of coronary artery disease. PCI techniques have evolved considerably since the advent of PCI in 1978, and with this evolution in techniques has come changes in the best practices for patient management following PCI. The objective of this review is to provide a comprehensive overview of key considerations in patient management following PCI. The long-term management of patients post-PCI should follow 3 main principles: 1) lifestyle modification and reduction of risk factors; 2) implementation of secondary prevention therapies; and 3) timely detection of restenosis. Best practices in achieving these principles include promotion of smoking cessation, regular physical activity, and a healthy diet, as well as blood pressure, diabetes mellitus, lipid, and weight management; prescription of secondary prevention therapies balancing ischemic and bleeding risk; and avoidance of routine surveillance for restenosis.

Scarpa Matuck B, Yang W, Qin L, Yan F, Lima JAC, Arbab-Zadeh A, Wolfe S. Prognostic Value of Coronary CT Angiography-Derived Quantitative Flow Ratio in Suspected Coronary Artery Disease

Background The prognostic value of coronary CT angiography (CTA)-derived quantitative flow ratio (CT-QFR) remains unknown. Purpose To determine the prognostic value of CT-QFR in predicting the long-term outcomes of patients with suspected coronary artery disease (CAD) in comparison with invasive coronary angiography (ICA)/SPECT and to determine the influence of prior percutaneous coronary intervention (PCI) on the prognostic value of CT-QFR. Materials and Methods In this secondary analysis of the prospective international CORE320 study, 379 participants who underwent coronary CTA and SPECT within 60 days before ICA between November 2009 and July 2011 were included for follow-up. The coronary CTA images were analyzed to determine CT-QFR. The primary outcome was major adverse cardiovascular events (MACEs) in the 5-year follow-up. Kaplan-Meier curves, multivariable Cox regression models adjusted for clinical variables, and areas under the receiver operating characteristic curves (AUCs) were used to assess and compare the predictive ability of CT-QFR and ICA/SPECT. Results CT-QFR computation and 5-year follow-up data were available for 310 participants (median age, 62 years), of whom 205 (66%) were male. CT-QFR (hazard ratio, 1.9 [95% CI: 1.0, 3.5]; P = .04) and prior myocardial infarction (hazard ratio, 2.5 [95% CI: 1.5, 4.0]; P < .001) were independent predictors of MACE occurrence in the 5-year follow-up. MACE-free survival rates were similar in participants with normal CT-QFR and ICA/SPECT (82% vs 80%; P = .45) and in participants with abnormal CT-QFR and ICA/SPECT findings (60% vs 57%; P = .40). In participants with prior PCI, CT-QFR had a lower AUC in predicting MACEs than in participants without prior PCI (0.44 vs 0.70; P < .001). Conclusion CT-QFR was an independent predictor of MACEs in the 5-year follow-up in participants with suspected CAD and showed similar 5-year prognostic value to ICA/SPECT; however, prior PCI affected CT-QFR ability to predict MACEs. Clinical trial registration no. NCT00934037 © RSNA, 2024 Supplemental material is available for this article.

Pre-PCI CT-FFR Predicts Target Vessel Failure After Stent Implantation

OBJECTIVES

To investigate the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) before percutaneous coronary intervention (PCI) to predict target vessel failure (TVF) after stent implantation.

METHODS

This retrospective study included 429 patients (429 vessels) who underwent PCI and stent implantation after CCTA within 3 months. All patients underwent coronary stent implantation between January 2012 and December 2019. A dedicated workstation (Syngo Via, Siemens) was used to analyze and measure the CT-FFR value. The cut-off values of pre-PCI CT-FFR for predicting TVF were defined as 0.80 and the value using the log-rank maximization method, respectively. The primary outcome was TVF, defined as a composite of cardiac death, target vessel myocardial infarction, and clinically driven target vessel revascularization (TVR), which was a secondary outcome.

RESULTS

During a median 64.0 months follow-up, the cumulative incidence of TVF was 7.9% (34/429). The cutoff value of pre-PCI CT-FFR based on the log-rank maximization method was 0.74, which was the independent predictor for TVF [hazard ratio (HR): 2.61 (95% CI: 1.13, 6.02); P =0.024] and TVR [HR: 3.63 (95%CI: 1.25, 10.51); P =0.018]. Compared with the clinical risk factor model, pre-PCI CT-FFR significantly improved the reclassification ability for TVF [net reclassification improvement (NRI), 0.424, P <0.001; integrative discrimination index (IDI), 0.011, P =0.022]. Adding stent information to the prediction model resulted in an improvement in reclassification for the TVF (C statistics: 0.711, P =0.001; NRI: 0.494, P <0.001; IDI: 0.020, P =0.028).

CONCLUSIONS

Pre-PCI CT-FFR ≤0.74 was an independent predictor for TVF or TVR, and integration of clinical, pre-PCI CT-FFR, and stent information models can provide a better risk stratification model in patients with stent implantation.

Stent-specific fat attenuation index is associated with target vessel revascularization after PCI

OBJECTIVES

To explore the clinical relevance of stent-specific perivascular fat attenuation index (FAI) in patients with stent implantation.

METHODS

A total of 162 consecutive patients who underwent coronary computed tomography angiography (CCTA) following stent implantation were retrospectively included. The stent-specific FAI at 2 cm adjacent to the stent edge was calculated. The endpoints were defined as target vessel revascularization (TVR) on the stented vessel after CCTA and readmission times due to chest pain after stent implantation. Binary logistic regression analysis for TVR and ordinal regression models were conducted to identify readmission times (0, 1, and ≥ 2) with generalized estimating equations on a per-stent basis.

RESULTS

On a per-stent basis, 9 stents (4.5%) experienced TVR after PCI at a median 30 months' follow-up duration. Stent-specific FAI differed significantly among subgroups of patients with stent implantation and different readmission times (p = 0.002); patients with at least one readmission had higher stent-specific FAI than those without readmission (p < 0.001). Bifurcated stents (odds ratio [OR]: 11.192, p = 0.001) and stent-specific FAI (OR: 1.189, p = 0.04) were independently associated with TVR. With no readmission as a reference, stent-specific FAI (OR: 0.984, p = 0.007) was an independent predictor for hospital readmission times ≥ 2 (p = 0.003).

CONCLUSION

Non-invasive stent-specific FAI derived from CCTA was found to be associated with TVR, which was a promising imaging marker for functional assessment in patients who underwent stent implantation.

CLINICAL RELEVANCE STATEMENT

Noninvasive fat attenuation index adjacent to the stents edge derived from CCTA, an imaging marker reflecting the presence of inflammation acting on the neointimal tissue at the sites of coronary stenting, might be relevant clinically with target vessel revascularization.

KEY POINTS

• Non-invasive stent-specific FAI derived from CCTA was associated with TVR (OR: 1.189 [95% CI: 1.007-1.043], p = 0.04) in patients who underwent stent implantation. • Stent-specific FAI significantly differed among a subgroup of patients with chest pain after stent implantation and with different readmission times (p = 0.002); the patients with at least one readmission had higher stent-specific FAI than those without readmission (p < 0.001). • Non-invasive stent-specific FAI derived from CCTA could be used as an imaging maker for the functional assessment of patients following stent implantation.

Artificial Intelligence in Cardiovascular CT and MR Imaging

The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists' workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.

Construction and Validation of a Nomogram Clinical Prediction Model for Predicting Osteoporosis in an Asymptomatic Elderly Population in Beijing

BACKGROUND

Based on the high prevalence and occult-onset of osteoporosis, the development of novel early screening tools was imminent. Therefore, this study attempted to construct a nomogram clinical prediction model for predicting osteoporosis.

METHODS

Asymptomatic elderly residents in the training (n = 438) and validation groups (n = 146) were recruited. BMD examinations were performed and clinical data were collected for the participants. Logistic regression analyses were performed. A logistic nomogram clinical prediction model and an online dynamic nomogram clinical prediction model were constructed. The nomogram model was validated by means of ROC curves, calibration curves, DCA curves, and clinical impact curves.

RESULTS

The nomogram clinical prediction model constructed based on gender, education level, and body weight was well generalized and had moderate predictive value (AUC > 0.7), better calibration, and better clinical benefit. An online dynamic nomogram was constructed.

CONCLUSIONS

The nomogram clinical prediction model was easy to generalize, and could help family physicians and primary community healthcare institutions to better screen for osteoporosis in the general elderly population and achieve early detection and diagnosis of the disease.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

Cardiovascular Imaging in China: Yesterday, Today, and Tomorrow

The high prevalence and mortality of cardiovascular diseases in China's large population has increased the use of cardiovascular imaging for the assessment of conditions in recent years. In this study, we review the past 20 years of cardiovascular imaging in China, the increasingly important role played by cardiovascular computed tomography in coronary artery disease and pulmonary embolism assessment, magnetic resonance imaging's use for cardiomyopathy assessment, the development and application of artificial intelligence in cardiovascular imaging, and the future of Chinese cardiovascular imaging.

The Chinese Experience of Imaging in Cardiac Intervention: A Bird's Eye Review

Recent scientific and technological advances have greatly contributed to the development of medical imaging that could enable specific functions. It has become the primary focus of cardiac intervention in preoperative assessment, intraoperative guidance, and postoperative follow-up. This review provides a contemporary overview of the Chinese experience of imaging in cardiac intervention in recent years.

The Predictive Value of the Perivascular Adipose Tissue CT Fat Attenuation Index for Coronary In-stent Restenosis

Objectives

To investigate the association between the perivascular adipose tissue (PVAT) fat attenuation index (FAI) derived from coronary computed tomography angiography (CCTA) and the prevalence of in-stent restenosis (ISR) in patients with coronary stent implantation.

Methods

A total of 117 patients with previous coronary stenting referred for invasive coronary angiography (ICA) were enrolled in this retrospective observational analysis. All patients underwent CCTA between July 2016 and November 2021. The deep learning-based (DL-based) method was used to analyze and measure the peri-stent FAI value. Additionally, the relationship between hematological and biochemical parameters collected from all the patients was also explored. The least absolute shrinkage and selection operator (LASSO) method was applied to the most useful feature selection, and binary logistic regression was used to test the association between the selected features and ISR. The predictive performance for ISR of the identified subgroups was evaluated by calculating the area under the curve (AUC) of receiver operator curves plotted for each model. The Pearson correlation coefficient was used to assess the correlation of peri-stent FAI values with degrees of ISR.

Results

The peri-stent FAI values in the ISR group were significantly higher than those in the non-ISR group (−78.1 ± 6.2 HU vs. −87.2 ± 7.3 HU, p < 0.001). The predictive ISR features based on the LASSO analysis were peri-stent FAI, high-density lipoprotein cholesterol (HDL-C), apolipoprotein A1 (ApoA1), and high-sensitivity c-reactive protein (hs-CRP), with an AUC of 0.849, 0.632, 0.620, and 0.569, respectively. Binary logistic regression analysis determined that peri-stent FAI was uniquely and independently associated with ISR after adjusting for other risk factors (odds ratio [OR] 1.403; 95% CI: 1.211 to 1.625; p < 0.001). In the subgroup analysis, the AUCs of the left anterior descending coronary artery (LAD), left circumflex coronary artery (LCx), and right coronary artery (RCA) stents groups were 0.80, 0.87, and 0.96, respectively. The Pearson's correlation coefficient indicated a term moderately correlation between ISR severity and peri-stent FAI values (r = 0.579, P < 0.001).

Conclusions

The peri-stent FAI can be used as an independently non-invasive biomarker to predict ISR risk and severity after stent implantation.

Multimodality Imaging in Ischemic Chronic Cardiomyopathy

Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.

Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study

OBJECTIVES

To examine the diagnostic accuracy of machine learning-based coronary CT angiography-derived fractional flow reserve (FFR_CT) in diabetes mellitus (DM) patients.

METHODS

In total, 484 patients with suspected or known coronary artery disease from 11 Chinese medical centers were retrospectively analyzed. All patients underwent CCTA, FFR_CT, and invasive FFR. The patients were further grouped into mild (25~49 %), moderate (50~69 %), and severe (≥ 70 %) according to CCTA stenosis degree and Agatston score < 400 and Agatston score ≥ 400 groups according to coronary artery calcium severity. Propensity score matching (PSM) was used to match DM (n  = 112) and non-DM (n  = 214) groups. Sensitivity, specificity, accuracy, and area under the curve (AUC) with 95 % confidence interval (CI) were calculated and compared.

RESULTS

Sensitivity, specificity, accuracy, and AUC of FFR_CT were 0.79, 0.96, 0.87, and 0.91 in DM patients and 0.82, 0.93, 0.89, and 0.89 in non-DM patients without significant difference (all p > 0.05) on a per-patient level. The accuracies of FFR_CT had no significant difference among different coronary stenosis subgroups and between two coronary calcium subgroups (all p > 0.05) in the DM and non-DM groups. After PSM grouping, the accuracies of FFR_CT were 0.88 in the DM group and 0.87 in the non-DM group without a statistical difference (p > 0.05).

CONCLUSIONS

DM has no negative impact on the diagnostic accuracy of machine learning-based FFR_CT.

KEY POINTS

• ML-based FFR_CT has a high discriminative accuracy of hemodynamic ischemia, which is not affected by DM. • FFR_CT was superior to the CCTA alone for the detection of ischemia relevance of coronary artery stenosis in both DM and non-DM patients. • Coronary calcification had no significant effect on the diagnostic accuracy of FFR_CT to detect ischemia in DM patients.

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review

The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.