Myocardial Fibrosis in Hypertrophic Cardiomyopathy: Volumetric Assessment of Late Enhancement Provided by Cardiac Computed Tomography

Hypertrophic Cardiomyopathy: Current Treatment and Future Options

Hypertrophic cardiomyopathy (HCM) is a disease involving the cardiac sarcomere. It is associated with various disease-causing gene mutations and phenotypic expressions, managed with different therapies with variable prognoses. The heterogeneity of the disease is evident in the fact that it burdens patients of all ages. HCM is the most prevalent cause of sudden death in athletes. However, several technological advancements and therapeutic options have reduced mortality in patients with HCM to 0.5% per year. In addition, rapid advances in our knowledge of the molecular defects accountable for HCM have strengthened our awareness of the disorder and recommended new approaches to the assessment of prognosis. Despite all these evolutions, a small subgroup of patients with HCM will experience sudden cardiac death, and risk stratification remains a critical challenge. This review provides a practical guide to the updated recommendations for patients with HCM, including clinical updates for diagnosis, family screening, clinical imaging, risk stratification, and management.

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.

A radiomic approach to predict myocardial fibrosis on coronary CT angiography in hypertrophic cardiomyopathy

BACKGROUND

Late gadolinium enhancement (LGE) derived from cardiac magnetic resonance (CMR) represents myocardial fibrosis (MF) and is associated with prognosis in hypertrophic cardiomyopathy (HCM). However, it cannot be evaluated when CMR is unavailable. Hence, we aimed to investigate the ability of radiomic features derived from coronary computed tomography angiography (CCTA) to detect the presence and extent of MF in HCM, with LGE as references.

METHODS

161 patients with HCM who underwent CCTA and CMR were retrospectively enrolled and randomly divided into training (107 patients, 1712 segments) and testing cohorts (54 patients, 864 segments). Segments were obtained according to AHA 17-segment method. Radiomic features were extracted from per-segment and entire myocardium regions, and multiple machine-learning algorithms were used for radiomic signatures (Rad-sig) generation and model building. Four models were established by multivariable logistic regression using Rad-sig (R-model), clinical characteristic (C-model), echocardiography parameters (E-model), and all features integrated (Integ-model) to identify LGE/left ventricular mass ≥ 15%.

RESULTS

The model achieved good diagnostic accuracy in both training (area under the curve [AUC]:0.81, 95% confidence interval [CI]: 0.78-0.83) and testing cohort (AUC: 0.78, 95%CI: 0.75-0.81) on a per-segment basis for the presence of MF. The Integ-model owned the highest discriminative ability for patients with LGE/left ventricular mass ≥ 15% in both training and testing cohorts with AUC of 0.94 (95%CI: 0.89-0.98) and 0.92 (95%CI: 0.85-0.99), respectively.

CONCLUSIONS

Our radiomic models were considered as useful and complementary biomarkers for the evaluation of the presence and extent of MF on CCTA, facilitating clinical decision-making and risk stratification in HCM patients.

Utility of Dual-Energy CT-based Monochromatic Imaging in the Assessment of Myocardial Delayed Enhancement in Patients with Cardiomyopathy

Purpose To investigate the diagnostic utility of dual-energy computed tomography (CT)-based monochromatic imaging for myocardial delayed enhancement (MDE) assessment in patients with cardiomyopathy. Materials and Methods The institutional review board approved this prospective study, and informed consent was obtained from all participants who were enrolled in the study. Forty patients (27 men and 13 women; mean age, 56 years ± 15 [standard deviation]; age range, 22-81 years) with cardiomyopathy underwent cardiac magnetic resonance (MR) imaging and dual-energy CT. Conventional (120-kV) and monochromatic (60-, 70-, and 80-keV) images were reconstructed from the dual-energy CT acquisition. Subjective quality score, contrast-to-noise ratio (CNR), and beam-hardening artifacts were compared pairwise with the Friedman test at post hoc analysis. With cardiac MR imaging as the reference standard, diagnostic performance of dual-energy CT in MDE detection and its predictive ability for pattern classification were compared pairwise by using logistic regression analysis with the generalized estimating equation in a per-segment analysis. The Bland-Altman method was used to find agreement between cardiac MR imaging and CT in MDE quantification. Results Among the monochromatic images, 70-keV CT images resulted in higher subjective quality (mean score, 3.38 ± 0.54 vs 3.15 ± 0.43; P = .0067), higher CNR (mean, 4.26 ± 1.38 vs 3.93 ± 1.33; P = .0047), and a lower value for beam-hardening artifacts (mean, 3.47 ± 1.56 vs 4.15 ± 1.67; P < .0001) when compared with conventional CT. When compared with conventional CT, 70-keV CT showed improved diagnostic performance for MDE detection (sensitivity, 94.6% vs 90.4% [P = .0032]; specificity, 96.0% vs 94.0% [P = .0031]; and accuracy, 95.6% vs 92.7% [P < .0001]) and improved predictive ability for pattern classification (subendocardial, 91.5% vs 84.3% [P = .0111]; epicardial, 94.3% vs 73.5% [P = .0001]; transmural, 93.0% vs 77.7% [P = .0018]; mesocardial, 85.4% vs 69.2% [P = .0047]; and patchy. 84.4% vs 78.4% [P = .1514]). For MDE quantification, 70-keV CT showed a small bias 0.1534% (95% limits of agreement: -4.7013, 5.0080). Conclusion Dual-energy CT-based 70-keV monochromatic images improve MDE assessment in patients with cardiomyopathy via improved image quality and CNR and reduced beam-hardening artifacts when compared with conventional CT images. ^© RSNA, 2017 Online supplemental material is available for this article.

The role of imaging in the diagnosis and management of hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy, affecting approximately 1:500 people. As the yield of genetic testing is only about 35-60%, the diagnosis of HCM is still clinical and based on the demonstration of unexplained and usually asymmetric left ventricular (LV) hypertrophy by imaging modalities. In the past, echocardiography was the sole imaging modality used for the diagnosis and management of HCM. However, in recent years other imaging modalities such as cardiac magnetic resonance have played a major role in the diagnosis, management and risk stratification of HCM, particularly when the location of left ventricular hypertrophy is atypical (apex, lateral wall) and when the echocardiographic imaging is sub-optimal. However, the most unique contribution of cardiac magnetic resonance is the quantification of myocardial fibrosis. Exercise stress echocardiography is the preferred provocative test for the assessment of LV outflow tract obstruction, which is detected only on provocation in one-third of the patients.