MultiD4CAD: Multimodal Dataset composed of CT and Clinical Features for Coronary Artery Disease Analysis.
Authors
Affiliations (6)
Affiliations (6)
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127, Palermo, Italy.
- Institute for High-Performance Computing and Networking (ICAR-CNR), National Research Council, 90146, Palermo, Italy. [email protected].
- Radiology Unit, IRCCS ISMETT (Mediterranean Institute for Transplantation and Advanced Specialized Therapies), 90127, Palermo, Italy.
- Department of Health Promotion Sciences Maternal and Infantile Care, Internal Medicine and Medical Specialities (ProMISE), University of Palermo, 90127, Palermo, Italy.
- Department of Radiology, AOUP Paolo Giaccone, Via del Vespro 129, Palermo, 90127, Italy.
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127, Palermo, Italy. [email protected].
Abstract
Multimodal datasets offer valuable support for developing Clinical Decision Support Systems (CDSS), which leverage predictive models to enhance clinicians' decision-making. In this observational study, we present a dataset of suspected Coronary Artery Disease (CAD) patients - called MultiD4CAD - comprising imaging and clinical data. The imaging data obtained from Coronary Computed Tomography Angiography (CCTA) includes epicardial (EAT) and pericoronary (PAT) adipose tissue segmentations. These metabolically active fat tissues play a key role in cardiovascular diseases. In addition, clinical data include a set of biomarkers recognized as CAD risk factors. The validated EAT and PAT segmentations make the dataset suitable for training predictive models based on radiomics and deep learning architectures. The inclusion of CAD disease labels allows for its application in supervised learning algorithms to predict CAD outcomes. MultiD4CAD has revealed important correlations between imaging features, clinical biomarkers, and CAD status. The article concludes by discussing some challenges, such as classification, segmentation, radiomics, and deep training tasks, that can be investigated and validated using the proposed dataset.