Patients with cancer usually report limitations of their functional capacity, which may range from subclinical impairment of cardiopulmonary exercise reserve to poor quality of life with physical, cognitive, or psychosocial consequences. Exercise training is a potent multi-targeted approach to control pre-existing and new risk factors. Preliminary evidence shows that it is associated with a lower risk of cancer therapy-related cardiotoxicity and increased self-reported well-being in patients with cancer. Current evidence demonstrates that supervised exercise therapy, including high-intensity interval training, is safe and well-tolerated and reduces risk in subjects with cancer in the pre-, active-, and post-treatment settings. The present consensus document will discuss the role of exercise training in cardio-oncology, focusing on patients with cardiovascular diseases induced by cancer treatment and on those who received cardiotoxic therapies.

Cardiovascular diseases remain as a leading cause of mortality and morbidity worldwide, with coronary artery disease (CAD) and its complications, collectively referred to as major adverse cardiovascular events (MACEs), necessitating accurate risk stratification. Coronary computed tomography angiography (CCTA) has emerged as a valuable non-invasive imaging modality, and adipose tissue characteristics derived from CCTA have shown promise as imaging biomarkers for MACE prediction. This systematic review and meta-analysis aimed to evaluate the predictive performance of artificial intelligence (AI)-driven models incorporating CCTA-derived adipose tissue radiomic features for forecasting MACEs. A systematic review and random-effects meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies evaluating predictive models developed using different AI algorithms that utilized adipose tissue radiomics as the primary predictor of MACEs in patients undergoing CCTA were included. Model performance was assessed using pooled area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Eleven studies comprising 47 244 participants were included in the analysis. AI-based models integrating adipose tissue radiomic features with clinical data consistently outperformed conventional risk assessment tools, with pooled AUCs ranging from 82.2% to 87.9%. Among the evaluated approaches, deep learning models demonstrated superior predictive performance compared with traditional machine learning and logistic regression-based models. However, considerable heterogeneity (I[2] > 96%) was observed across studies, reflecting variations in study design, imaging protocols, and AI methodologies. While AI-enhanced CCTA-based adipose tissue characterization demonstrates considerable potential for improving MACE risk prediction, methodological heterogeneity and limited external validation currently restrict its clinical applicability. Future research should prioritize standardized imaging and analytical methodologies, rigorous clinical and external validation, and transparent reporting to facilitate reliable integration of these AI models into routine cardiovascular risk assessment.

We enrolled 10 patients(12 evaluations) undergoing atrial fibrillation ablation following WATCHMAN FLXPro implantation. We compared the degree of device endothelialization assessed by endoscopic imaging with patient characteristics. Device endothelialization was classified into four grades(eGrade 1-4). Effective endothelialization was defined as eGrade 3(Semi-opaque) or higher. All endoscopies were performed beyond 2 months following the implantation. The distribution of endothelialization was: eGrade 1(Absent, n = 2), eGrade 2(Translucent, n = 2), eGrade 3(Semi-opaque, n = 3), and eGrade 4(Opaque, n = 3). The endoscopic endothelialization grade tended to be associated with the interval since implantation, persistent atrial fibrillation, and prior discontinuation of oral anticoagulants. Endothelialization progressed from the atrial wall where the device was anchored. Although endothelialization at the central hub was delayed, solid tissue coverage was observed around the hub in a few cases.

Right-sided infective endocarditis (RSIE) commonly causes septic pulmonary embolism, whereas systemic manifestations typically suggest concomitant left-sided infective endocarditis or an intracardiac right-to-left shunt such as patent foramen ovale (PFO).