Radiomics of Late Gadolinium Enhancement Reveals Prognostic Value of Myocardial Scar Heterogeneity in Hypertrophic Cardiomyopathy

Integration of Cine-cardiac Magnetic Resonance Radiomics and Machine Learning for Differentiating Ischemic and Dilated Cardiomyopathy

RATIONALE AND OBJECTIVES

This study aims to evaluate the capability of machine learning algorithms in utilizing radiomic features extracted from cine-cardiac magnetic resonance (CMR) sequences for differentiating between ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM).

MATERIALS AND METHODS

This retrospective study included 115 cardiomyopathy patients subdivided into ICM (n = 64) and DCM cohorts (n = 51). We collected invasive clinical (IC), noninvasive clinical (NIC), and combined clinical (CC) feature subsets. Radiomic features were extracted from regions of interest (ROIs) in the left ventricle (LV), LV cavity (LVC), and myocardium (MYO). We tested 10 classical machine learning classifiers and validated them through fivefold cross-validation. We compared the efficacy of clinical feature-based models and radiomics-based models to identify the superior diagnostic approach.

RESULTS

In the validation set, the Gaussian naive Bayes (GNB) model outperformed the other models in all categories, with areas under the curve (AUCs) of 0.879 for IC_GNB, 0.906 for NIC_GNB, and 0.906 for CC_GNB. Among the radiomics models, the MYO_LASSOCV_MLP model demonstrated the highest AUC (0.919). In the test set, the MYO_RFECV_GNB radiomics model achieved the highest AUC (0.857), surpassing the performance of the three clinical feature models (IC_GNB: 0.732; NIC_GNB: 0.75; CC_GNB: 0.786).

CONCLUSION

Radiomics models leveraging MYO images from cine-CMR exhibit promising potential for differentiating ICM from DCM, indicating the significant clinical application scope of such models.

CLINICAL RELEVANCE STATEMENT

The integration of radiomics models and machine learning methods utilizing cine-CMR sequences enhances the diagnostic capability to distinguish between ICM and DCM, minimizes examination risks for patients, and potentially reduces the duration of medical imaging procedures.

Cardiovascular Magnetic Resonance-Based Tissue Characterization in Patients With Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a common hereditable cardiomyopathy that affects between 1:200 to 1:500 of the general population. The role of cardiovascular magnetic resonance (CMR) imaging in the management of HCM has expanded over the past 2 decades to become a key informant of risk in this patient population, delivering unique insights into tissue health and its influence on future outcomes. Numerous mature CMR-based techniques are clinically available for the interrogation of tissue health in patients with HCM, inclusive of contrast and noncontrast methods. Late gadolinium enhancement imaging remains a cornerstone technique for the identification and quantification of myocardial fibrosis with large cumulative evidence supporting value for the prediction of arrhythmic outcomes. T1 mapping delivers improved fidelity for fibrosis quantification through direct estimations of extracellular volume fraction but also offers potential for noncontrast surrogate assessments of tissue health. Water-sensitive imaging, inclusive of T2-weighted dark blood imaging and T2 mapping, have also shown preliminary potential for assisting in risk discrimination. Finally, emerging techniques, inclusive of innovative multiparametric methods, are expanding the utility of CMR to assist in the delivery of comprehensive tissue characterization toward the delivery of personalized HCM care. In this narrative review we summarize the contemporary landscape of CMR techniques aimed at characterizing tissue health in patients with HCM. The value of these respective techniques to identify patients at elevated risk of future cardiovascular outcomes are highlighted.

Scanning the Imaging Horizon for Hypertrophic Cardiomyopathy

In this article some of the recent advances in the use of noninvasive imaging applied to patients with hypertrophic cardiomyopathy (HCM) are discussed. Echocardiography and cardiac computed tomography are briefly discussed with respect to their power to detect apical aneurysmal disease. Echocardiographic phenotype-genotype correlations and the use of echocardiography to characterize myocardial work are reviewed. Positron emission tomography is reviewed in the context of ischemia imaging and also in the context of the use of a new tracer that might allow for recognition of early activation of the fibrosis pathway. Next, the technical capabilities of cardiovascular magnetic resonance to measure myocardial perfusion, oxygenation, and disarray are discussed as they apply to HCM. The application of radiomics to improve prediction of sudden cardiac death is touched upon. Finally, a deep learning approach to the recognition of HCM vs phenocopies is presented as a potential future diagnostic aid in the not-too-distant future.

Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach

In hypertensive intracerebral hemorrhage (HICH) patients, while emergency surgeries effectively reduce intracranial pressure and hematoma volume, their significant risk of causing postoperative rehemorrhage necessitates early detection and management to improve patient prognosis. This study sought to develop and validate machine learning (ML) models leveraging clinical data and noncontrast CT radiomics to pinpoint patients at risk of postoperative rehemorrhage, equipping clinicians with an early detection tool for prompt intervention. The study conducted a retrospective analysis on 609 HICH patients, dividing them into training and external verification cohorts. These patients were categorized into groups with and without postoperative rehemorrhage. Radiomics features from noncontrast CT images were extracted, standardized, and employed to create several ML models. These models underwent internal validation using both radiomics and clinical data, with the best model's feature significance assessed via the Shapley additive explanations (SHAP) method, then externally validated. In the study of 609 patients, postoperative rehemorrhage rates were similar in the training (18.8%, 80/426) and external verification (17.5%, 32/183) cohorts. Six significant noncontrast CT radiomics features were identified, with the support vector machine (SVM) model outperforming others in both internal and external validations. SHAP analysis highlighted five critical predictors of postoperative rehemorrhage risk, encompassing three radiomics features from noncontrast CT and two clinical data indicators. This study highlights the effectiveness of an SVM model combining radiomics features from noncontrast CT and clinical parameters in predicting postoperative rehemorrhage among HICH patients. This approach enables timely and effective interventions, thereby improving patient outcomes.

Noninvasive Techniques for Tracking Biological Aging of the Cardiovascular System: JACC Family Series

Population aging is one of the most important demographic transformations of our time. Increasing the "health span"-the proportion of life spent in good health-is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging.

Correlation Between Cognitive Impairment and Lenticulostriate Arteries: A Clinical and Radiomics Analysis

Lenticulostriate arteries (LSA) are potentially valuable for studying vascular cognitive impairment. This study aims to investigate correlations between cognitive impairment and LSA through clinical and radiomics features analysis. We retrospectively included 102 patients (mean age 62.5±10.3 years, 60 males), including 58 with mild cognitive impairment (MCI) and 44 with moderate or severe cognitive impairment (MSCI). The MRI images of these patients were subjected to z-score preprocessing, manual regions of interest (ROI) outlining, feature extraction (pyradiomics), feature selection [max-relevance and min-redundancy (mRMR), least absolute shrinkage and selection operator (LASSO), and univariate analysis], model construction (multivariate logistic regression), and evaluation [receiver operating characteristic curve (ROC), decision curve analysis (DCA), and calibration curves (CC)]. In the training dataset (71 patients, 44 MCI) and the test dataset (31 patients, 17 MCI), the area under curve (AUC) of the combined model (training 0.88 [95% CI 0.78, 0.97], test 0.76 [95% CI 0.6, 0.93]) was better than that of the clinical model and the radiomics model. The DCA results demonstrated the highest net yield of the combined model relative to the clinical and radiomics models. In addition, we found that LSA total vessel count (0.79 [95% CI 0.08, 1.59], P = 0.038) and wavelet.HLH_glcm_MCC (-1.2 [95% CI -2.2, -0.4], P = 0.008) were independent predictors of MCI. The model that combines clinical and radiomics features of LSA can predict MCI. Besides, LSA vascular parameters may serve as imaging biomarkers of cognitive impairment.

Advances in Multi-Modality Imaging in Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is characterized by abnormal growth of the myocardium with myofilament disarray and myocardial hyper-contractility, leading to left ventricular hypertrophy and fibrosis. Where culprit genes are identified, they typically relate to cardiomyocyte sarcomere structure and function. Multi-modality imaging plays a crucial role in the diagnosis, monitoring, and risk stratification of HCM, as well as in screening those at risk. Following the recent publication of the first European Society of Cardiology (ESC) cardiomyopathy guidelines, we build on previous reviews and explore the roles of electrocardiography, echocardiography, cardiac magnetic resonance (CMR), cardiac computed tomography (CT), and nuclear imaging. We examine each modality's strengths along with their limitations in turn, and discuss how they can be used in isolation, or in combination, to facilitate a personalized approach to patient care, as well as providing key information and robust safety and efficacy evidence within new areas of research.

Contemporary review on pediatric hypertrophic cardiomyopathy: insights into detection and management

Hypertrophic cardiomyopathy is the most common genetic cardiac disorder and is defined by the presence of left ventricular (LV) hypertrophy in the absence of a condition capable of producing such a magnitude of hypertrophy. Over the past decade, guidelines on the screening, diagnostic, and management protocols of pediatric primary (i.e., sarcomeric) HCM have undergone significant revisions. Important revisions include changes to the appropriate screening age, the role of cardiac MRI (CMR) in HCM diagnosis, and the introduction of individualized pediatric SCD risk assessment models like HCM Risk-kids and PRIMaCY. This review explores open uncertainties in pediatric HCM that merit further attention, such as the divergent American and European recommendations on CMR use in HCM screening and diagnosis, the need for incorporating key genetic and imaging parameters into HCM-Risk Kids and PRIMaCY, the best method of quantifying myocardial fibrosis and its prognostic utility in SCD prediction for pediatric HCM, devising appropriate genotype- and phenotype-based exercise recommendations, and use of heart failure medications that can reverse cardiac remodeling in pediatric HCM.