Differential cardiac impacts of hematological malignancies and solid tumors: a histopathological and biomarker study.

Background: Cancer patients are known to be associated with increased risk of cardiovascular disease. However, no studies have examined the differential impact of hematologic malignancies (HMs) and solid tumors (STs) on cardiac morphology at the tissue level.

Objective: We aimed to examine histopathological features alongside cardiovascular biomarkers in patients with HMs and STs who underwent post-mortem evaluation.

Lightweight preprocessing and template matching facilitate streamlined ischemic myocardial scar classification.

Purpose: Ischemic myocardial scarring (IMS) is a common outcome of coronary artery disease that potentially leads to lethal arrythmias and heart failure. Late-gadolinium-enhanced cardiac magnetic resonance (CMR) imaging scans have served as the diagnostic bedrock for IMS, with recent advancements in machine learning enabling enhanced scar classification. However, the trade-off for these improvements is intensive computational and time demands.

Automation of ischemic myocardial scar detection in cardiac magnetic resonance imaging of the left ventricle using machine learning.

Purpose: Machine learning techniques can be applied to cardiac magnetic resonance imaging (CMR) scans in order to differentiate patients with and without ischemic myocardial scarring (IMS). However, processing the image data in the CMR scans requires manual work that takes a significant amount of time and expertise. We propose to develop and test an AI method to automatically identify IMS in CMR scans to streamline processing and reduce time costs.