Project Details
Projekt
Exploration of the Prognostic Value of Radiomics Analysis and Deep Learning of Coronary Plaques on Computed Tomography for Cardiovascular Events
Applicants
Professor Dr. Marc Dewey; Dr. Matthias Rief; Dr. Christian Wald
Subject Area
Nuclear Medicine, Radiotherapy, Radiobiology
Medical Physics, Biomedical Technology
Medical Physics, Biomedical Technology
Term
from 2019 to 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 428222922
Background and Objectives: Coronary artery plaques may lead to cardiovascular events and there is early evidence that noninvasive detection of vulnerable plaque features on computed tomography (CT) and subsequent changes in medical management may lead to improved outcomes. CT is an increasingly used test in patients with suspected coronary artery disease (CAD) and is the best noninvasive imaging test to capture three-dimensional information about plaques in all coronary artery segments in one examination. However, existing data is limited by variable definitions of plaque morphology, inconsistency in event collection, and mixed study designs. Moreover, there is limited prospective data on the prognostic value of coronary CT plaque features in stable chest pain patients. We propose radiomics analysis and deep learning (DL) of coronary plaques on CT to identify patients at high-risk of cardiovascular events. In this project, we will explore the capabilities of radiomics and DL to comprehensively characterise and quantify coronary artery plaques.Methods and Work Programme: We prospectively collected CT image data from more than 1700 patients in the multicentre DISCHARGE trial in the clinically relevant prognostic setting of suspected CAD and will conduct long-term clinical follow-up. In the proposed project, we will test the prognostic value of radiomics and DL using convolutional neural networks for coronary artery plaques analysis. First, we will test the prognostic value of the coronary artery calcium score in this prospective patient cohort. Second, we will analyze all CT datasets using conventional plaque segmentation and classification (e.g., non-calcified, partially calcified, calcified) and compare the prognostic value with high-risk plaque features such as low-attenuation, positive remodelling, the napkin ring sign and spotty calcifications. Third, we will adapt and implement existing radiomics- and DL-based image-analysis algorithms to extract features of vulnerable plaques. This includes the above high-risk coronary artery plaque features, radiomic features and DL-predictions to compare the accuracy in prognostication of prospectively defined long-term clinical endpoints such as myocardial infarction and coronary revascularisation. The quantitative results of image analysis will be made available as a database with cardiovascular events.Anticipated Gain of Knowledge: We anticipate novel insights into the association of coronary plaque features with prognosis of stable chest pain patients which will strengthen the clinical implications of radiomics analysis and deep learning of coronary CT. Ultimately, this will allow identifying patients prone to suffer myocardial infarction and testing the validity and generalisability of advanced coronary CT plaque analysis using data from the population-based SCAPIS project of more than 25000 asymptomatic individuals which will have long-term follow-up data for the second three-year funding period.
DFG Programme
Priority Programmes
