Epicardial Adipose Tissue Radiomics for Predicting Cardiac Outcomes After AMI

206 acute myocardial infarction patients were enrolled in a single-center prospective cohort study between January 2022 and June 2023. The dataset likely contains radiomics features extracted from cardiac CT scans, clinical parameters, and outcomes for ventricular remodeling and major adverse cardiovascular events. It was created by Zeyan Liu and is licensed under CC-BY-4.0.

Use Cases

• Predicting ventricular remodeling after acute myocardial infarction based on epicardial adipose tissue radiomics features.
• Developing models for major adverse cardiovascular events risk stratification based on combined imaging and clinical data.
• Investigating the relationship between epicardial adipose tissue volume and myocardial injury outcomes.
• Validating radiomics-based predictive models against clinical follow-up data.

Strengths

• Data is from a prospective cohort study with 206 patients, suggesting a structured collection process.
• Includes 12-month follow-up outcomes for two key clinical endpoints: ventricular remodeling (29.6%) and major adverse cardiovascular events (19.9%).
• Models were validated and showed superior predictive performance with reported AUC and C-index values.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data is from a single center, which may reflect geographic or institutional bias.

Provenance

Source

figshare

Collection Method

Radiomics features extracted from cardiac CT scans using Pyradiomics, with predictive models constructed using machine learning algorithms.

Time Range

Patients enrolled between January 2022 and June 2023, with a 12-month follow-up.

Freshness

Last updated 2026-05-18 05:31:24; freshness should be verified.

Geography

Single-center study at the Second Affiliated Hospital of Anhui Medical University; specific location is not stated.