The Relationship Between Quantitative Ischemia, Early Revascularization, and Major Adverse Cardiovascular Events: A Multicenter Study.

Background: Observational data have suggested that patients with moderate to severe ischemia benefit from revascularization. However, this was not confirmed in a large, randomized trial.

Objectives: Using a contemporary, multicenter registry, the authors evaluated differences in the association between quantitative ischemia, revascularization, and outcomes across important subgroups.

Global longitudinal strain in pre-symptomatic patients with mutation for transthyretin amyloidosis.

Background: Hereditary transthyretin (ATTRv) amyloidosis is rare, autosomal dominant disease with a fatal outcome if left untreated. Early stages detection is crucial for intervention. We aimed identifying early indexes of cardiac involvement and their eventual correlation with neurological indexes, in pre-symptomatic subjects with TTR gene mutation.

Prognostic Value of Coronary Flow Capacity by Rb PET in Patients With Suspected Coronary Artery Disease and Normal Myocardial Perfusion at Semiquantitative Imaging Analysis.

Background: Coronary flow capacity (CFC) is a measure that integrates hyperemic myocardial blood flow and myocardial flow reserve to quantify the pathophysiological impact of coronary artery disease on vasodilator capacity. We assessed the prognostic value of CFC derived from Rb positron emission tomography/computed tomography in patients with suspected coronary artery disease and normal myocardial perfusion imaging.

Methods: We studied 1967 patients with suspected coronary artery disease and normal myocardial perfusion at the semiquantitative analysis of stress/rest cardiac Rb positron emission tomography/computed tomography imaging.

Artificial Intelligence for Optimizing Cancer Imaging: User Experience Study.

Background: The need for increased clinical efficacy and efficiency has been the main force in developing artificial intelligence (AI) tools in medical imaging. The INCISIVE project is a European Union-funded initiative aiming to revolutionize cancer imaging methods using AI technology. It seeks to address limitations in imaging techniques by developing an AI-based toolbox that improves accuracy, specificity, sensitivity, interpretability, and cost-effectiveness.