The year 2017 in the European Heart Journal-Cardiovascular Imaging: Part I

Derivation and external validation of mass spectrometry-based proteomic model using machine learning algorithms to predict plaque rupture in patients with acute coronary syndrome

BACKGROUND

A poor prognosis is associated with atherosclerotic plaque rupture (PR) despite after conventional therapy for patients with acute coronary syndrome (ACS). Timely identification of PR improves the risk stratification and prognosis of ACS patients.

METHODS

A derivation cohort of 110 patients with ACS who underwent pre-intervention optical coherence tomography (OCT) were matched 1:1 to the PR and intact fibrous cap (IFC) groups according to traditional risk factors. Candidate PR proteins were identified via mass spectrometry (MS)-based proteomics using unbiased machine learning methods and were further validated by enzyme-linked immunosorbent assay (ELISA) in an external validation cohort of 85 patients with ACS. The performance of candidate biomakers was assessed using the receiver operating characteristic curve analysis.

RESULTS

1121 proteins were identified and 535 filtered proteins were used for analysis. Nine candidate proteins were screened by five machine learning algorithms. Three proteins (APOC3, RAB39A, and KNG1) were significantly different between the PR and IFC in validation cohort. The performance of plasm APOC3, RAB39A, and KNG1 for differentiating PR and IFC was superior to that of the conventional biomarkers and risk factors.

CONCLUSION

The proteins (APOC3, RAB39A, and KNG1) serve as a potential novel diagnostic tool to identify PR in ACS patients.

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease

OBJECTIVE

To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD).

MATERIALS AND METHODS

The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic.

RESULTS

Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model.

CONCLUSION

Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

Peri-coronary fat attenuation index combined with high-risk plaque characteristics quantified from coronary computed tomography angiography for risk stratification in new-onset chest pain individuals without acute myocardial infarction

This study aims to evaluate the role of the peri-coronary Fat Attenuation Index (FAI) and High-Risk Plaque Characteristics (HRPC) in the assessment of coronary heart disease risk. By conducting coronary CT angiography and coronary angiography on 217 patients with newly developed chest pain (excluding acute myocardial infarction), their degree of vascular stenosis, FAI, and the presence and quantity of HRPC were assessed. The study results demonstrate a correlation between FAI and HRPC, and the combined use of FAI and HRPC can more accurately predict the risk of major adverse cardiovascular events (MACE). Additionally, the study found that patients with high FAI were more prone to exhibit high-risk plaque characteristics, severe stenosis, and multiple vessel disease. After adjustment, the combination of FAI and HRPC improved the ability to identify and reclassify MACE. Furthermore, the study identified high FAI as an independent predictor of MACE in patients undergoing revascularization, while HRPC served as an independent predictor of MACE in patients not undergoing revascularization. These findings suggest the potential clinical value of FAI and HRPC in the assessment of coronary heart disease risk, particularly in patients with newly developed chest pain excluding acute myocardial infarction.

Development of a nomogram based on pericoronary adipose tissue histogram parameters to differentially diagnose acute coronary syndrome

PURPOSE

To develop a nomogram based on pericoronary adipose tissue (PCAT) histogram parameters to identify patients with acute coronary syndrome (ACS).

MATERIALS AND METHODS

This study retrospectively enrolled 114 and 383 eligible patients with ACS and stable coronary artery disease (CAD), respectively, and divided them into training and testing cohorts in a 7:3 ratio. A blinded radiologist obtained PCAT histogram parameters from the right coronary artery's proximal segment using fully automated software and compared clinical characteristics and PCAT histogram parameters between the two patient groups. The binary logistic regression included significant parameters (P < 0.05), and a nomogram was constructed.

RESULTS

In both the training and testing cohorts, the mean, 10th percentile, 90th percentile, median, and minimum values of PCAT were higher, and the interquartile range, skewness, and variance values of PCAT were lower in patients with ACS than in those with stable CAD (P ≤ 0.001). The mean (OR = 4.007), median (OR = 0.576), minimum (OR = 0.893), skewness (OR = 85,158.806) and variance (OR = 1.013) values of PCAT were independent risk factors for ACS and stable CAD in the training cohort. The nomogram was constructed using the five variables mentioned above with area under the curve values of 0.903 and 0.897, respectively, while the calibration and decision curves showed the nomogram's good clinical efficacy for the training and testing cohorts.

CONCLUSIONS

The constructed nomogram had good discrimination and accuracy and can be a noninvasive tool to intuitively and individually distinguish between ACS and stable CAD.

Identification of patients with acute myocardial infarction based on coronary CT angiography: the value of pericoronary adipose tissue radiomics

OBJECTIVE

To determine whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate acute myocardial infarction (MI) from unstable angina (UA).

METHODS

In a single-center retrospective case-control study, patients with acute MI (n = 105) were matched to patients with UA (n = 105) and all patients were randomly divided into training and validation cohorts with a ratio of 7:3. Fat attenuation index (FAI) and PCAT radiomics features selected by Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) around the proximal three major epicardial coronary vessels (LAD [left anterior descending artery], LCx [left circumflex artery], and RCA [right coronary artery]) were used to build logistic regression models. Finally, a FAI model, three radiomics models of PCAT (LAD, LCx, and RCA), and a combined model that used the scores of these independent models were constructed. The performance of the models was evaluated by identification, calibration, and clinical application.

RESULTS

In training and validation cohorts, compared with the FAI model (AUC = 0.53, 0.50), the combined model achieved superior performance (AUC = 0.97, 0.95) while there was a significant difference of AUC between two models (p < 0.05). The calibration curves of the combined model demonstrated the smallest Brier score loss. Decision curve analysis suggested that the combined model provided higher clinical benefit than the FAI model.

CONCLUSIONS

The CCTA-based radiomics phenotype of PCAT outperforms the FAI model in discriminating acute MI from UA. The combination of PCAT radiomics and FAI could further enhance the performance of acute MI identification.

KEY POINTS

• Fat attenuation index based on CCTA can detect inflammation-induced changes in the ratio of lipid to aqueous phase in pericoronary adipose tissue. • Fat attenuation index cannot distinguish acute MI patients from UA patients, suggesting that the two groups have the same degree of ratio of lipid to aqueous phase in pericoronary adipose tissue. • Radiomics features of PCAT have the potential to distinguish acute MI patients from UA patients.

The year 2018 in the European Heart Journal-Cardiovascular Imaging: Part II

European Heart Journal - Cardiovascular Imaging was launched in 2012 as a multimodality cardiovascular imaging journal. It has gained an impressive impact factor during its first 5 years and is now established as one of the top cardiovascular journals and has become the most important cardiovascular imaging journal in Europe. The most important studies from 2018 will be highlighted in two reports. Part I of the review has focused on studies about myocardial function and risk prediction, myocardial ischaemia, and emerging techniques in cardiovascular imaging, while Part II will focus on cardiomyopathies, congenital heart diseases, valvular heart diseases, and heart failure.

A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

BACKGROUND

Coronary inflammation induces dynamic changes in the balance between water and lipid content in perivascular adipose tissue (PVAT), as captured by perivascular Fat Attenuation Index (FAI) in standard coronary CT angiography (CCTA). However, inflammation is not the only process involved in atherogenesis and we hypothesized that additional radiomic signatures of adverse fibrotic and microvascular PVAT remodelling, may further improve cardiac risk prediction.

METHODS AND RESULTS

We present a new artificial intelligence-powered method to predict cardiac risk by analysing the radiomic profile of coronary PVAT, developed and validated in patient cohorts acquired in three different studies. In Study 1, adipose tissue biopsies were obtained from 167 patients undergoing cardiac surgery, and the expression of genes representing inflammation, fibrosis and vascularity was linked with the radiomic features extracted from tissue CT images. Adipose tissue wavelet-transformed mean attenuation (captured by FAI) was the most sensitive radiomic feature in describing tissue inflammation (TNFA expression), while features of radiomic texture were related to adipose tissue fibrosis (COL1A1 expression) and vascularity (CD31 expression). In Study 2, we analysed 1391 coronary PVAT radiomic features in 101 patients who experienced major adverse cardiac events (MACE) within 5 years of having a CCTA and 101 matched controls, training and validating a machine learning (random forest) algorithm (fat radiomic profile, FRP) to discriminate cases from controls (C-statistic 0.77 [95%CI: 0.62-0.93] in the external validation set). The coronary FRP signature was then tested in 1575 consecutive eligible participants in the SCOT-HEART trial, where it significantly improved MACE prediction beyond traditional risk stratification that included risk factors, coronary calcium score, coronary stenosis, and high-risk plaque features on CCTA (Δ[C-statistic] = 0.126, P < 0.001). In Study 3, FRP was significantly higher in 44 patients presenting with acute myocardial infarction compared with 44 matched controls, but unlike FAI, remained unchanged 6 months after the index event, confirming that FRP detects persistent PVAT changes not captured by FAI.

CONCLUSION

The CCTA-based radiomic profiling of coronary artery PVAT detects perivascular structural remodelling associated with coronary artery disease, beyond inflammation. A new artificial intelligence (AI)-powered imaging biomarker (FRP) leads to a striking improvement of cardiac risk prediction over and above the current state-of-the-art.

The year 2018 in the European Heart Journal - Cardiovascular Imaging: Part I

The European Heart Journal - Cardiovascular Imaging has become one of the leading multimodality cardiovascular imaging journal, since it was launched in 2012. The impact factor is an impressive 8.366 and it is now established as one of the top 10 cardiovascular journals. The journal is the most important cardiovascular imaging journal in Europe. The most important studies from 2018 will be highlighted in two reports. Part I of the review will focus on studies about myocardial function and risk prediction, myocardial ischaemia, and emerging techniques in cardiovascular imaging, while Part II will focus on valvular heart disease, heart failure, cardiomyopathies, and congenital heart disease.