Duchenne and Becker muscular dystrophy (DMD/BMD) are X-linked muscular diseases responsible for over 80% of all muscular dystrophies. Cardiac disease is a common manifestation, not necessarily related to the degree of skeletal myopathy; it may be the predominant manifestation with or without any other evidence of muscular disease. Death is usually due to ventricular dysfunction, heart block or malignant arrhythmias. Not only DMD/BMD patients, but also female carriers may present cardiac involvement. Clinically overt heart failure in dystrophinopathies may be delayed or absent, due to relative physical inactivity. The commonest electrocardiographic findings include conduction defects, arrhythmias (supraventricular or ventricular), hypertrophy and evidence of myocardial necrosis. Echocardiography can assess a marked variability of left ventricular dysfunction, independently of age of onset or mutation groups. Cardiovascular magnetic resonance (CMR) has documented a pattern of epicardial fibrosis in both dystrophinopathies' patients and carriers that can be observed even if overt muscular disease is absent. Recently, new CMR techniques, such as postcontrast myocardial T1 mapping, have been used in Duchenne muscular dystrophy to detect diffuse myocardial fibrosis. A combined approach using clinical assessment and CMR evaluation may motivate early cardioprotective treatment in both patients and asymptomatic carriers and delay the development of serious cardiac complications.

CMR may reveal severe cardiac involvement in early, asymptomatic diffuse SSc with normal routine cardiac evaluation, presenting either as myocardial inflammation or as severe reduction of MPRI and diffuse fibrosis with further deterioration in the long term follow up.

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Cardiac masses are space occupying lesions within the cardiac cavities or adjacent to the pericardium. They include frequently diagnosed clinical entities such as clots and vegetations, common benign tumors such as myxomas and papillary fibroelastomas and uncommon benign or malignant primary or metastatic tumors. Given their diversity, there are no guidelines or consensus statements regarding the best diagnostic or therapeutic approach. In the past, diagnosis used to be made by the histological specimens after surgery or during the post-mortem examination. Nevertheless, evolution and increased availability of cardiovascular imaging modalities has enabled better characterization of the masses and the surrounding tissue. Transthoracic echocardiography using contrast agents can evaluate the location, the morphology and the perfusion of the mass as well as its hemodynamic effect. Transesophageal echocardiography has increased spatial and temporal resolution; hence it is superior in depicting small highly mobile masses. Cardiac magnetic resonance and cardiac computed tomography are complementary providing tissue characterization. The scope of this review is to present the role of cardiovascular imaging in the differential diagnosis of cardiac masses and to propose a step-wise diagnostic algorithm, taking into account the epidemiology and clinical presentation of the cardiac masses, as well as the availability and the incremental value of each imaging modality.

Background: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRC ML ) and compares against statistically derived CVRC (CVRC Stat ) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors).Methods: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRC Stat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRC Stat , the patients were again risk-stratified using CVRC ML based on support vector machine (SVM) and finally;(III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events.Results: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRC Stat calculators were: AECRS2.0 (AUC 0.83, P<0.001), QRISK3 (AUC 0.72, P<0.001), WHO (AUC 0.70, P<0.001), ASCVD (AUC 0.67, P<0.001), FRS cardio (AUC 0.67, P<0.01), FRS stroke (AUC 0.64, P<0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P<0.001), NIPPON (AUC 0.63, P<0.001), PROCAM (AUC 0.59, P<0.001), RRS (AUC 0.57, P<0.001), UKPDS60 (AUC 0.53, P<0.001), and SCORE (AUC 0.45, P<0.001), while the AUC for the CVRC ML with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRC ML with integrated risk factors was 92.52% which was higher compared all the other CVRC Stat.