Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

Minimising multi-centre radiomics variability through image normalisation: a pilot study.

Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features' variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification.

Prognosis of Paradoxical Low-Flow Low-Gradient Aortic Stenosis: A Severe, Non-critical Form, With Surgical Treatment Benefits.

Objectives: To determine the risk of mortality and need for aortic valve replacement (AVR) in patients with low-flow low-gradient (LFLG) aortic stenosis (AS). Methods: A longitudinal multicentre study including consecutive patients with severe AS (aortic valve area [AVA] < 1.0 cm2) and normal left ventricular ejection fraction (LVEF). Patients were classified as: high-gradient (HG, mean gradient ≥ 40 mmHg), normal-flow low-gradient (NFLG, mean gradient < 40 mmHg, indexed systolic volume (SVi) > 35 ml/m2) and LFLG (mean gradient < 40 mmHg, SVi ≤ 35 ml/m2). Results: Of 1,391 patients, 147 (10.5%) had LFLG, 752 (54.1%) HG, and 492 (35.4%) NFLG. Echocardiographic parameters of the LFLG group showed similar AVA to the HG group but with less severity in the dimensionless index, calcification, and hypertrophy. The HG group required AVR earlier than NFLG (p < 0.001) and LFLG (p < 0.001), with no differences between LFLG and NFLG groups (p = 0.358). Overall mortality was 27.7% (CI 95% 25.3-30.1) with no differences among groups (p = 0.319). The impact of AVR in terms of overall mortality reduction was observed the most in patients with HG (hazard ratio [HR]: 0.17; 95% CI: 0.12-0.23; p < 0.001), followed by patients with LFLG (HR: 0.25; 95% CI: 0.13-0.49; p < 0.001), and finally patients with NFLG (HR: 0.29; 95% CI: 0.20-0.44; p < 0.001), with a risk reduction of 84, 75, and 71%, respectively. Conclusions: Paradoxical LFLG AS affects 10.5% of severe AS, and has a lower need for AVR than the HG group and similar to the NFLG group, with no differences in mortality. AVR had a lower impact on LFLG AS compared with HG AS. Therefore, the findings of the present study showed LFLG AS to have an intermediate clinical risk profile between the HG and NFHG groups.

Antithrombotic therapies in aortic and peripheral arterial diseases in 2021: a consensus document from the ESC working group on aorta and peripheral vascular diseases, the ESC working group on thrombosis, and the ESC working group on cardiovascular pharmacotherapy.

The aim of this collaborative document is to provide an update for clinicians on best antithrombotic strategies in patients with aortic and/or peripheral arterial diseases. Antithrombotic therapy is a pillar of optimal medical treatment for these patients at very high cardiovascular risk. While the number of trials on antithrombotic therapies in patients with aortic or peripheral arterial diseases is substantially smaller than for those with coronary artery disease, recent evidence deserves to be incorporated into clinical practice. In the absence of specific indications for chronic oral anticoagulation due to concomitant cardiovascular disease, a single antiplatelet agent is the basis for long-term antithrombotic treatment in patients with aortic or peripheral arterial diseases. Its association with another antiplatelet agent or low-dose anticoagulants will be discussed, based on patient's ischaemic and bleeding risk as well therapeutic paths (e.g. endovascular therapy). This consensus document aims to provide a guidance for antithrombotic therapy according to arterial disease localizations and clinical presentation. However, it cannot substitute multidisciplinary team discussions, which are particularly important in patients with uncertain ischaemic/bleeding balance. Importantly, since this balance evolves over time in an individual patient, a regular reassessment of the antithrombotic therapy is of paramount importance.