Radiomics of Non-Contrast-Enhanced T1 Mapping: Diagnostic and Predictive Performance for Myocardial Injury in Acute ST-Segment-Elevation Myocardial Infarction

The Incremental Value of Native T1 Mapping-Derived Radiomics for The Diagnosis of Amyloid Light-Chain Cardiac Amyloidosis

RATIONALE AND OBJECTIVES

This study aimed to assess the incremental diagnostic value of non-contrast T1 mapping-derived radiomics in patients with amyloid light-chain cardiac amyloidosis (AL-CA).

METHODS

We retrospectively collected 86 patients with suspected AL-CA and 28 control patients who underwent cardiac MRI at 3.0 T in our institution, and the MRI data were divided into a training set and a test set. Radiomic features were extracted based on native T1 maps using a freely available software package. We applied LASSO logistic regression method to select radiomic features with high diagnostic value of AL-CA and develop a predictive model. The diagnostic performance of the radiomics model was evaluated using receiver operating characteristic curve analysis and compared to T1 values.

RESULTS

A total of 70 people were diagnosed with AL-CA, and cardiac involvement was observed in 202 myocardial slicers. The accuracy of T1 values for the diagnosis of myocardial involvement was 0.886, with a threshold value of 1375 ms. The radiomics score comprised a total of three features. The radiomics score demonstrated significantly higher sensitivity in detecting myocardial involvement compared to T1 values in both the training set (AUC 0.886 vs. 0.924, P = 0.025) and the test set (0.862 vs 0.915, P = 0.026). The combined model comprising T1 values and a radiomic feature named S(4,-4) Correlat showed higher diagnostic performance in comparison to T1 values alone both in the training and test sets, with AUC values of 0.929 and 0.909, respectively.

CONCLUSION

The radiomic features derived from native T1 mapping demonstrated incremental value for the diagnosis of AL-CA, which may be an alternative to T1-derived ECV to avoid the use of contrast in patients with suspected myocardial involvement in systemic amyloidosis.

Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction

OBJECTIVES

To use pericardial adipose tissue (PAT) radiomics phenotyping to differentiate existing and predict future heart failure (HF) cases in the UK Biobank.

METHODS

PAT segmentations were derived from cardiovascular magnetic resonance (CMR) studies using an automated quality-controlled model to define the region-of-interest for radiomics analysis. Prevalent (present at time of imaging) and incident (first occurrence after imaging) HF were ascertained using health record linkage. We created balanced cohorts of non-HF individuals for comparison. PyRadiomics was utilised to extract 104 radiomics features, of which 28 were chosen after excluding highly correlated ones (0.8). These features, plus sex and age, served as predictors in binary classification models trained separately to detect (1) prevalent and (2) incident HF. We tested seven modeling methods using tenfold nested cross-validation and examined feature importance with explainability methods.

RESULTS

We studied 1204 participants in total, 297 participants with prevalent (60 ± 7 years, 21% female) and 305 with incident (61 ± 6 years, 32% female) HF, and an equal number of non-HF comparators. We achieved good discriminative performance for both prevalent (voting classifier; AUC: 0.76; F1 score: 0.70) and incident (light gradient boosting machine: AUC: 0.74; F1 score: 0.68) HF. Our radiomics models showed marginally better performance compared to PAT area alone. Increased PAT size (maximum 2D diameter in a given column or slice) and texture heterogeneity (sum entropy) were important features for prevalent and incident HF classification models.

CONCLUSIONS

The amount and character of PAT discriminate individuals with prevalent HF and predict incidence of future HF.

CLINICAL RELEVANCE STATEMENT

This study presents an innovative application of pericardial adipose tissue (PAT) radiomics phenotyping as a predictive tool for heart failure (HF), a major public health concern. By leveraging advanced machine learning methods, the research uncovers that the quantity and characteristics of PAT can be used to identify existing cases of HF and predict future occurrences. The enhanced performance of these radiomics models over PAT area alone supports the potential for better personalised care through earlier detection and prevention of HF.

KEY POINTS

•PAT radiomics applied to CMR was used for the first time to derive binary machine learning classifiers to develop models for discrimination of prevalence and prediction of incident heart failure. •Models using PAT area provided acceptable discrimination between cases of prevalent or incident heart failure and comparator groups. •An increased PAT volume (increased diameter using shape features) and greater texture heterogeneity captured by radiomics texture features (increased sum entropy) can be used as an additional classifier marker for heart failure.

Recent Advances in Cardiac Magnetic Resonance for Imaging of Acute Myocardial Infarction

Acute myocardial infarction (AMI) is one of the primary causes of death worldwide, with a high incidence and mortality rate. Assessment of the infarcted and surviving myocardium, along with microvascular obstruction, is crucial for risk stratification, treatment, and prognosis in patients with AMI. Nonionizing radiation, excellent soft tissue contrast resolution, a large field of view, and multiplane imaging make cardiac magnetic resonance (CMR) a "one-stop" method for assessing cardiac structure, function, perfusion, and metabolism. Hence, this imaging technology is considered the "gold standard" for evaluating myocardial function and viability in AMI. This review critically compares the advantages and disadvantages of CMR with other cardiac imaging technologies, and relates the imaging findings to the underlying pathophysiological processes in AMI. A more thorough understanding of CMR technology will clarify their advanced clinical diagnosis and prognostic assessment applications, and assess the future approaches and challenges of CMR in the setting of AMI.

Late gadolinium enhanced cardiac MR derived radiomics approach for predicting all-cause mortality in cardiac amyloidosis: a multicenter study

OBJECTIVES

To evaluate the prognostic value of radiomics features based on late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images in patients with cardiac amyloidosis (CA).

METHODS

This retrospective study included 120 CA patients undergoing CMR at three institutions. Radiomics features were extracted from global and three different segments (base, mid-ventricular, and apex) of left ventricular (LV) on short-axis LGE images. Primary endpoint was all-cause mortality. The predictive performance of the radiomics features and semi-quantitative and quantitative LGE parameters were compared by ROC. The AUC was used to observe whether Rad-score had an incremental value for clinical stage. The Kaplan-Meier curve was used to further stratify the risk of CA patients.

RESULTS

During a median follow-up of 12.9 months, 30% (40/120) patients died. There was no significant difference in the predictive performance of the radiomics model in different LV sections in the validation set (AUCs of the global, basal, middle, and apical radiomics model were 0.75, 0.77, 0.76, and 0.77, respectively; all p > 0.05). The predictive performance of the Rad-score of the base-LV was better than that of the LGE total enhancement mass (AUC:0.77 vs. 0.54, p < 0.001) and LGE extent (AUC: 0.77 vs. 0.53, p = 0.004). Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone (AUC: 0.86 vs. 0.81, p = 0.03). Rad-score (≥ 0.66) contributed to the risk stratification of all-cause mortality in CA.

CONCLUSIONS

Compared to quantitative LGE parameters, radiomics can better predict all-cause mortality in CA, while the combination of radiomics and Mayo stage could provide higher predictive accuracy.

CLINICAL RELEVANCE STATEMENT

Radiomics analysis provides incremental value and improved risk stratification for all-cause mortality in patients with cardiac amyloidosis.

KEY POINTS

• Radiomics in LV-base was superior to LGE semi-quantitative and quantitative parameters for predicting all-cause mortality in CA. • Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone or radiomics alone. • Rad-score ≥ 0.66 was associated with a significantly increased risk of all-cause mortality in CA patients.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Non-Contrast Cine Cardiac Magnetic Resonance Derived-Radiomics for the Prediction of Left Ventricular Adverse Remodeling in Patients With ST-Segment Elevation Myocardial Infarction

OBJECTIVE

To investigate the predictive value of radiomics features based on cardiac magnetic resonance (CMR) cine images for left ventricular adverse remodeling (LVAR) after acute ST-segment elevation myocardial infarction (STEMI).

MATERIALS AND METHODS

We conducted a retrospective, single-center, cohort study involving 244 patients (random-split into 170 and 74 for training and testing, respectively) having an acute STEMI (88.5% males, 57.0 ± 10.3 years of age) who underwent CMR examination at one week and six months after percutaneous coronary intervention. LVAR was defined as a 20% increase in left ventricular end-diastolic volume 6 months after acute STEMI. Radiomics features were extracted from the one-week CMR cine images using the least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the selected features was evaluated using receiver operating characteristic curve analysis and the area under the curve (AUC).

RESULTS

Nine radiomics features with non-zero coefficients were included in the LASSO regression of the radiomics score (RAD score). Infarct size (odds ratio [OR]: 1.04 (1.00-1.07); P = 0.031) and RAD score (OR: 3.43 (2.34-5.28); P < 0.001) were independent predictors of LVAR. The RAD score predicted LVAR, with an AUC (95% confidence interval [CI]) of 0.82 (0.75-0.89) in the training set and 0.75 (0.62-0.89) in the testing set. Combining the RAD score with infarct size yielded favorable performance in predicting LVAR, with an AUC of 0.84 (0.72-0.95). Moreover, the addition of the RAD score to the left ventricular ejection fraction (LVEF) significantly increased the AUC from 0.68 (0.52-0.84) to 0.82 (0.70-0.93) (P = 0.018), which was also comparable to the prediction provided by the combined microvascular obstruction, infarct size, and LVEF with an AUC of 0.79 (0.65-0.94) (P = 0.727).

CONCLUSION

Radiomics analysis using non-contrast cine CMR can predict LVAR after STEMI independently and incrementally to LVEF and may provide an alternative to traditional CMR parameters.

Development and evaluation of a radiomics model of resting 13N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease

BACKGROUND

Coronary angiography (CAG) is usually performed in patients with coronary heart disease (CHD) to evaluate the coronary artery stenosis. However, patients with iodine allergy and renal dysfunction are not suitable for CAG. We try to develop a radiomics machine learning model based on rest 13N-ammonia (13N-NH3) positron emission tomography (PET) myocardial perfusion imaging (MPI) to predict coronary stenosis.

METHODS

Eighty-four patients were included with the inclusion criteria: adult patients; suspected CHD; resting MPI and CAG were performed; and complete data. Coronary artery stenosis >75% were considered to be significant stenosis. Patients were randomly divided into a training group and a testing group with a ratio of 1:1. Myocardial blood flow (MBF), perfusion defect extent (EXT), total perfusion deficit (TPD), and summed rest score (SRS) were obtained. Myocardial static images of the left ventricular (LV) coronary segments were segmented, and radiomics features were extracted. In the training set, the conventional parameter (MPI model) and radiomics (Rad model) models were constructed using the machine learning method and were combined to construct a nomogram. The models' performance was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, decision analysis curve (DCA), and calibration curves. Testing and subgroup analysis were performed.

RESULTS

MPI model was composed of MBF and EXT, and Rad model was composed of 12 radiomics features. In the training set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.795/0.778/0.937/0.511, 0.912/0.825/0.760/0.936 and 0.911/0.865/0.924/0.766 respectively. In the testing set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.798/0.722/0.659/0.841, 0.887/0.810/0.744/0.932 and 0.900/0.849/0.854/0.841 respectively. The AUC of Rad model and nomogram were significantly higher than that of MPI model. The DCA curve also showed that the clinical net benefit of the Rad model and nomogram was similar but greater than that of MPI model. The calibration curve showed good agreement between the observed and predicted values of the Rad model. In the subgroup analysis of Rad model, there was no significant difference in AUC between subgroups.

CONCLUSIONS

The Rad model is more accurate than the MPI model in predicting coronary stenosis. This noninvasive technique could help improve risk stratification and had good generalization ability.

Native T1 Mapping-Based Radiomics for Noninvasive Prediction of the Therapeutic Effect of Pulmonary Arterial Hypertension

(1) Background: Novel markers for predicting the short-term therapeutic effect of pulmonary arterial hypertension (PAH) to assist in the prompt initiation of tailored treatment strategies are greatly needed and highly desirable. The aim of the study was to investigate the role of cardiac magnetic resonance (CMR) native T1 mapping radiomics in predicting the short-term therapeutic effect in PAH patients; (2) Methods: Fifty-five PAH patients who received targeted therapy were retrospectively included. Patients were subdivided into an effective group and an ineffective group by assessing the therapeutic effect after ≥3 months of treatment. All patients underwent CMR examinations prior to the beginning of the therapy. Radiomics features from native T1 mapping images were extracted. A radiomics model was constructed using the support vector machine (SVM) algorithm for predicting the therapeutic effect; (3) Results: The SVM radiomics model revealed favorable performance for predicting the therapeutic effect with areas under the receiver operating characteristic curve of 0.955 in the training cohort and 0.893 in the test cohort, respectively. With the optimal cutoff value, the radiomics model showed accuracies of 0.909 and 0.818 in the training and test cohorts, respectively; (4) Conclusions: The CMR native T1 mapping-based radiomics model holds promise for predicting the therapeutic effect in PAH patients.

Artificial intelligence and cardiovascular magnetic resonance imaging in myocardial infarction patients

Cardiovascular magnetic resonance (CMR) is an important cardiac imaging tool for assessing the prognostic extent of myocardial injury after myocardial infarction (MI). Within the context of clinical trials, CMR is also useful for assessing the efficacy of potential cardioprotective therapies in reducing MI size and preventing adverse left ventricular (LV) remodelling in reperfused MI. However, manual contouring and analysis can be time-consuming with interobserver and intraobserver variability, which can in turn lead to reduction in accuracy and precision of analysis. There is thus a need to automate CMR scan analysis in MI patients to save time, increase accuracy, increase reproducibility and increase precision. In this regard, automated imaging analysis techniques based on artificial intelligence (AI) that are developed with machine learning (ML), and more specifically deep learning (DL) strategies, can enable efficient, robust, accurate and clinician-friendly tools to be built so as to try and improve both clinician productivity and quality of patient care. In this review, we discuss basic concepts of ML in CMR, important prognostic CMR imaging biomarkers in MI and the utility of current ML applications in their analysis as assessed in research studies. We highlight potential barriers to the mainstream implementation of these automated strategies and discuss related governance and quality control issues. Lastly, we discuss the future role of ML applications in clinical trials and the need for global collaboration in growing this field.

Effect of Iron Deposition on Native T1 Mapping and Blood Oxygen Level Dependent for the Assessment of Liver Fibrosis in Rabbits With Carbon Tetrachloride Intoxication

RATIONALE AND OBJECTIVES

This study aimed to explore the effect of iron deposition on native T1 mapping and blood oxygen level-dependent (BOLD) imaging in detecting liver fibrosis (LF) in a rabbit model.

MATERIALS AND METHODS

An LF group (n = 100) was established by subcutaneously injecting 50% carbon tetrachloride (CCl₄) oil solution, and 20 normal rabbits composed a control group. Native T1 mapping and BOLD were performed, and the T1_native and R₂* quantitative parameters were analyzed by receiver operating characteristic (ROC) and multiple logistic regression analyses, with histopathological results and liver iron content (LIC) serving as reference standards.

RESULTS

In total, 18, 17, 16, 18, and 15 rabbits were histopathologically diagnosed with LF stages F0, F1, F2, F3, and F4, respectively. T1_native (r = 0.47), R₂* (r = 0.75) and LIC (r = 0.61) increased with LF stage progression (p < 0.001). Compared to T1_native values, R₂* performed better in diagnosing the LF stage, especially for distinguishing F1-F2 from F3-F4 (AUC = 0.66 vs. 0.91, p = 0.01). Combined with the LIC, both T1_native and R₂* showed improved diagnostic value in comparison to the individual imaging techniques, particularly for diagnosing F0 vs. F1-F2 and F0 vs. F1-F4, with AUC values of 0.90 vs. 0.70 (p = 0.01) and 0.93 vs. 0.77 (p = 0.01) for T1_native + LIC vs. LIC, respectively.

CONCLUSION

BOLD imaging performed better than native T1 mapping in predicting and diagnosing LF stage progression. The decrease in diagnostic accuracy caused by the deposition of liver iron is a potential pitfall in the assessment of LF with BOLD imaging and native T1 mapping.

The Road Toward Reproducibility of Parametric Mapping of the Heart: A Technical Review

Parametric mapping of the heart has become an essential part of many cardiovascular magnetic resonance imaging exams, and is used for tissue characterization and diagnosis in a broad range of cardiovascular diseases. These pulse sequences are used to quantify the myocardial T₁, T₂, T2*, and T_1ρ relaxation times, which are unique surrogate indices of fibrosis, edema and iron deposition that can be used to monitor a disease over time or to compare patients to one another. Parametric mapping is now well-accepted in the clinical setting, but its wider dissemination is hindered by limited inter-center reproducibility and relatively long acquisition times. Recently, several new parametric mapping techniques have appeared that address both of these problems, but substantial hurdles remain for widespread clinical adoption. This review serves both as a primer for newcomers to the field of parametric mapping and as a technical update for those already well at home in it. It aims to establish what is currently needed to improve the reproducibility of parametric mapping of the heart. To this end, we first give an overview of the metrics by which a mapping technique can be assessed, such as bias and variability, as well as the basic physics behind the relaxation times themselves and what their relevance is in the prospect of myocardial tissue characterization. This is followed by a summary of routine mapping techniques and their variations. The problems in reproducibility and the sources of bias and variability of these techniques are reviewed. Subsequently, novel fast, whole-heart, and multi-parametric techniques and their merits are treated in the light of their reproducibility. This includes state of the art segmentation techniques applied to parametric maps, and how artificial intelligence is being harnessed to solve this long-standing conundrum. We finish up by sketching an outlook on the road toward inter-center reproducibility, and what to expect in the future.

A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD

RATIONALE AND OBJECTIVES

To evaluate the role of radiomics based on Chest Computed Tomography (CT) in the identification and severity staging of chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

This retrospective analysis included 322 participants (249 COPD patients and 73 control subjects). In total, 1395 chest CT-based radiomics features were extracted from each participant's CT images. Three feature selection methods, including variance threshold, Select K Best method, and least absolute shrinkage and selection operator (LASSO), and two classification methods, including support vector machine (SVM) and logistic regression (LR), were used as identification and severity classification of COPD. Performance was compared by AUC, accuracy, sensitivity, specificity, precision, and F1-score.

RESULTS

38 and 10 features were selected to construct radiomics models to detect and stage COPD, respectively. For COPD identification, SVM classifier achieved AUCs of 0.992 and 0.970, while LR classifier achieved AUCs of 0.993 and 0.972 in the training set and test set, respectively. For the severity staging of COPD, the mentioned two machine learning classifiers can better differentiate less severity (GOLD1 + GOLD2) group from greater severity (GOLD3 + GOLD4) group. The AUCs of SVM and LR is 0.907 and 0.903 in the training set, and that of 0.799 and 0.797 in the test set.

CONCLUSION

The present study showed that the novel radiomics approach based on chest CT images that can be used for COPD identification and severity classification, and the constructed radiomics model demonstrated acceptable performance.

Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review

OBJECTIVES

To evaluate the quality of radiomics studies using cardiac magnetic resonance imaging (CMR) according to the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, and the standards defined by the Image Biomarker Standardization Initiative (IBSI) and identify areas needing improvement.

MATERIALS AND METHODS

PubMed and Embase were searched to identify radiomics studies using CMR until March 10, 2021. Of the 259 identified articles, 32 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and IBSI standards by two cardiac radiologists.

RESULTS

The mean RQS was 14.3% of the maximum (5.16 out of 36). RQS were low for the demonstration of validation (-60.6%), calibration statistics (1.6%), potential clinical utility (3.1%), and open science (3.1%) items. No study conducted a phantom study or cost-effectiveness analysis. The adherence to TRIPOD guidelines was 55.9%. Studies were deficient in reporting title (3.1%), stating objective in abstract and introduction (6.3% and 9.4%), missing data (0%), discrimination/calibration (3.1%), and how to use the prediction model (3.1%). According to the IBSI standards, non-uniformity correction, image interpolation, grey-level discretization, and signal intensity normalization were performed in two (6.3%), four (12.5%), six (18.8%), and twelve (37.5%) studies, respectively.

CONCLUSION

The quality of radiomics studies using CMR is suboptimal. Improvements are needed in the areas of validation, calibration, clinical utility, and open science. Complete reporting of study objectives, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps are necessary.

KEY POINTS

• The quality of science in radiomics studies using CMR is currently inadequate. • RQS were low for validation, calibration, clinical utility, and open science; no study conducted a phantom study or cost-effectiveness analysis. • In stating the study objective, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps, improvements are needed.

Artificial Intelligence—A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome

Acute coronary syndrome is the leading cause of cardiac death and has a significant impact on patient prognosis. Early identification and proper management are key to ensuring better outcomes and have improved significantly with the development of various cardiovascular imaging modalities. Recently, the use of artificial intelligence as a method of enhancing the capability of cardiovascular imaging has grown. AI can inform the decision-making process, as it enables existing modalities to perform more efficiently and make more accurate diagnoses. This review demonstrates recent applications of AI in cardiovascular imaging to facilitate better patient care.

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Purpose of Review

This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.

Recent Findings

Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.

Summary

The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.

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.

Magnetic Resonance Texture Analysis in Myocardial Infarction

Texture analysis (TA) is a newly arisen field that can detect the invisible MRI signal changes among image pixels. Myocardial infarction (MI) is cardiomyocyte necrosis caused by myocardial ischemia and hypoxia, becoming the primary cause of death and disability worldwide. In recent years, various TA studies have been performed in patients with MI and show a good clinical application prospect. This review briefly presents the main pathogenesis and pathophysiology of MI, introduces the overview and workflow of TA, and summarizes multiple magnetic resonance TA (MRTA) clinical applications in MI. We also discuss the facing challenges currently for clinical utilization and propose the prospect.

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.