Background and Rationale
In recent years, cCTA has become a crucial diagnostic tool for suspected CAD, recommended
as the first-line test for patients with an intermediate pre-test probability of CAD by
the European Society of Cardiology (ESC) guidelines and the American HeartAssociation
(AHA) and Italian guidelines , because of the well-known high negative predictive value
of cCTA in ruling out obstructive CAD. However, most patients have non-obstructive CAD.
While the management of patients with obstructive CAD is established, as it revolves
around further diagnostic test for ischemia evaluation or upfront coronary artery
revascularization, this is not the case for patients with non-obstructive CAD. However,
these cohort of patients still has a significant risk of developing major adverse
cardiovascular events (MACEs) that could be prevented by implementing adequate medical
therapy.
To date, many approaches have been proposed to tackle this issue. However, these proposed
solutions lack the ability to provide quantitative and reproducible results with a
sufficiently strong predictive value, are often proposed as a stand-alone solution
without the integration with multiple prognosticator imaging and clinical parameters, and
are not delivered through platforms capable of providing external validation and easy
integration in the clinical workflow. Among the proposed prognostic approaches, some are
based on the qualitative evaluation of coronary artery plaque features, such as positive
remodeling, low attenuation of the plaque, presence of spotty calcification, and "napkin
ring" sign , which is subject to significant inter-reader variability.
Other approaches rely on quantitative methods for evaluating atherosclerotic burden based
on the extent of coronary artery plaques and their characteristics, such as calcium
density, number of lesions, regional distribution, plaque volume, non-calcified plaque
volume etc.
However, these approaches may be hampered by low reproducibility, especially among
different scanner vendors. Interestingly, a new research has also shown that, besides
coronary artery vessel wall characteristics, pericoronary adipose tissue attenuation
carries significant predictive value, as it reflects the state of coronary inflammation
that plays a key role in the development and progression of coronary atherosclerosis.
All these CAD characteristics are often analyzed independently from one to another,
reducing their potential synergistic prognostic value and creating redundant variables
that have negligible effect on prognosis. We propose an AI-based analysis that can
integrate all this data in order to select the most important determinant of CAD
progression and to discard futile features, thus creating an agile and clinically
valuable risk stratification model.Furthermore, we plan to create a novel imaging marker
of CAD with unfavorable outcome, to be integrated in the AI-based model, which will be
based on topological features of the coronary artery tree. In fact, data on the
association between coronary artery topology (e.g., vessel-length, coronary artery volume
index, cross-sectional area, curvature, and tortuositv) and prognosis is scarce. However,
it is known that vessel tortuosity influences wall shear stress and leads to disruption
of laminar flow, resulting in endothelial dysfunction and flow alterations that may lead
to atherosclerosis, eventually causing adverse cardiac events . Thus, this novel
biomarker may carry a significant prognostic role. Based on these premises, our research
aims to develop a novel clinical-imaging AI-based model to identify and categorize
patients at high risk of disease progression and provide a more personalized management
approach to improve patient outcomes.
However, besides the primary objective of creating an AI-based model for CAD risk
stratification, we aim to overcome some issues that currently hamper the widespread
clinical application of AI in cardiovascular care. In fact, it is recognized that the
integration of AI-based applications into the clinical workflow, which will increase
usability and decrease costs, is currently lacking.
We aim to tackle these issues with the help of the industrial partners involved in this
project that will build a platform capable of delivering the software solution to provide
external validation of the algorithm.
This platform will be characterized by state-of-the-art security measures,
interoperability with current clinical software, and easy-to-use interface.
