Evaluation of CTCA in Assessing Plaque Pathology and Physiology
Condition: Coronary Artery Disease
MeSH terms: Coronary Artery Disease
Study type: Interventional

Summary: 
      Computed tomographic coronary angiography (CTCA) has been recently introduced to
      non-invasively evaluate coronary artery pathology. Histology and intravascular ultrasound
      imaging studies have demonstrated that CTCA enables identification of plaque characteristics
      associated with increased vulnerability (i.e., plaque burden and composition) and allows
      assessment of vessel physiology (i.e., local haemodynamic forces), and reports have shown
      that CTCA can predict atherosclerotic evolution and detect lesions that will progress and
      cause cardiovascular events. Despite the wealth of data provided, CTCA has still a limited
      role in the study of atherosclerosis. Prior to unlocking the full potential of CTCA and
      enable its broad use, further work is needed to develop user-friendly processing tools that
      will allow fast and accurate analysis of CTCA, and examine in detail the accuracy of modern
      CTCA imaging in assessing plaque pathology. In this application, the investigators aim 1) to
      develop a CTCA analysis system that will enable fast segmentation, reliable coronary
      reconstruction and blood flow simulation in a user-friendly environment and 2) validate the
      efficacy of state-of-the-art CTCA for assessment of coronary plaque morphology and physiology
      against intravascular plaque imaging using hybrid near infrared spectroscopy-intravascular
      ultrasound.
    

Detailed description: 
      STUDY DESIGN

        1. Patent recruitment Seventy patients with typical angina symptoms who had elective
           coronary angiography showing at least one complex (i.e., bifurcation lesion, long
           lesion, calcified lesion) obstructive lesion (>70% diameter stenosis on coronary
           angiography, or a fractional flow reserve <0.80) that is considered suitable for
           percutaneous coronary intervention (PCI) under IVUS guidance will be included in the
           study. Exclusion criteria are: 1) age >75 years, 2) ACS within <3 months, 2) eGFR
           <60ml/min/1.73m², 3) previous coronary artery bypass surgery, 3) decompensated heart
           failure, or left ventricular ejection fraction ≤30%, 4) intravenous contrast allergy or
           inability to receive treatment with aspirin, heparin, or thienopyridines, 5) anticipated
           life expectancy <1 year, 6) history of heart transplantation, 7) patient that requires
           surgical revascularization, and 8) extensive coronary artery disease (i.e., multiple
           chronic total occlusions) or tortuous coronary anatomy that does not allow assessment of
           the coronary arteries with NIRS-IVUS imaging. The recruited patients will provide
           informed consent and undergo CTCA imaging using a dedicated 3rd generation CT
           dual-source scanner (Siemens Force). The first 4 months of the study effort will be made
           to optimise image acquisition protocols so as the obtained CTCA data to be suitable for
           automated segmentation. Efficient imaging matrixes will be used to improve in-plane
           spatial resolution and sharper reconstruction kernels and iterative reconstruction
           algorithms will be implemented to enhance image segmentation.

           Two weeks after CTCA imaging the patients will undergo planned PCI. During PCI effort
           will be made to study all the 3 epicardial coronary arteries - including the stenotic
           lesion - and some of their major side branches (i.e., large diagonals, obtuse marginals,
           the posterior descending artery or the left ventricular branch of the right coronary
           artery) with the combined NIRS-IVUS catheter. Following PCI the participants will be
           discharged on optimal medical treatment.

        2. Segmentation of the CTCA imaging data and reconstruction of coronary artery anatomy
           Imaging data will be anonymised and analysed blinded to clinical details by an expert
           operator using dedicated workstation. Anatomical landmarks (i.e., side branches) will be
           identified in the CTCA and NIRS-IVUS imaging data and will be used to define segments of
           interest.

           Analysis of the CTCA data will be performed using dedicated software that enables
           automated extraction of the luminal centreline, semi-automated detection of the lumen
           and outer vessel wall borders, and quantification of the plaque burden and incorporates
           a plaque characterisation algorithm that allows automated characterisation of the
           composition of the plaque. The plaque characterisation algorithm takes into account
           predefined fixed intensity cut-off values of the Hounsfield units and an adaptive
           approach that allows modification of these cut-off values according to image
           attenuation. Currently the segmentation process takes on average 3h per patient. In this
           project the investigators aim to optimise CTCA image acquisition and segmentation
           algorithms so as this process to become automated and reduce the time for CTCA
           segmentation to <1 hour.

        3. Segmentation of the NIRS-IVUS imaging data and reconstruction of coronary artery anatomy
           The NIRS-IVUS data portraying the segments of interest will be analysed by an expert
           operator, blinded to the clinical details and CTCA imaging data, with a 3-month interval
           from the analysis of the CTCA data using a software that enables detection of the lumen
           and outer vessel wall borders, quantification of plaque burden and annotation of the
           calcific tissue component in IVUS. The output of the analysis of the NIRS imaging data
           is the chemogram which is a colour coded map of the distribution of the lipid component
           along the vessel wall (yellow indicates increased probability and red low probability of
           lipid tissue). A metric of the lipid burden is the lipid core burden index (LCBI) which
           is computed as the fraction of the yellow pixels that correspond to lipid component
           divided by 1000. In addition, for each 2mm segments the block chemogram is generated
           that provides a summary of the chemogram for this segment and displays the probability
           of the presence of lipid tissue in a 2mm block of the coronary artery. The block
           chemogram has been validated against histology and it has been shown that it enables
           accurate detection of lipid-rich plaques.

           The segmented NIRS-IVUS data will be used to reconstruct the coronary anatomy using an
           established and well-validated methodology. Side branches with a diameter >1.5mm will be
           reconstructed from the angiographic data and fused with the main vessel geometry
           reconstructed from the NIRS-IVUS, since it has been shown that side branches affect ESS
           distribution.

        4. Blood flow simulation Identical boundary conditions will be applied to both IVUS-based
           and CTCA-based models. Blood will be considered to be a laminar and incompressible
           Newtonian fluid with a dynamic viscosity of 0.0035 Pa•s and a density of 1,050 kg/m3. A
           steady flow profile will be imposed at the inflow of the lumen as this reduces
           computation time and there is evidence that there is no significant difference in the
           estimated ESS when a steady or a pulsatile flow profile is used. Murray's theory of
           constant ESS will be used to derive boundary conditions in the main and side branches.
           The arterial wall will be considered to be rigid and no-slip conditions will be applied
           at the luminal surface. Flow velocity will be estimated from the angiographic data by
           measuring the number of frames required for the contrast agent to pass from the inlet to
           the outlet of the reconstructed segment, the volume of the segment at baseline, and the
           cine frame rate.

        5. Analysis of the NIRS-IVUS and CTCA imaging data It is anticipated that NIRS-IVUS imaging
           will be performed on average in 2.5 vessels per patient; from these 40 randomly selected
           vessels will be used to train the algorithms for CTCA segmentation and plaque
           characterisation (training dataset) and the remaining for validation purposes
           (validation dataset).

      In the training set, the segments of interest reconstructed from the CTCA and NIRS-IVUS data
      will be divided in 2mm segments and corresponding 2mm segments will be identified in the CTCA
      and NIRS-IVUS models. For each 2mm segment the following metrics will be estimated in the
      NIRS-IVUS models: mean lumen area, mean outer vessel wall area, mean plaque area, mean plaque
      burden (defined as: 100 x plaque area/vessel area), mean calcific area, the LCBI and the
      predominant ESS. In addition each segment will be classified as lipid-rich or non-lipid rich
      according to the block chemogram.

      Similarly, in the CTCA models the mean lumen area, outer vessel wall area, plaque area,
      plaque burden, calcific area and the mean predominant ESS will be estimated for every 2mm
      segment and compared with the estimations of NIRS-IVUS. Several approaches will be tested to
      optimise the segmentation of the vessel wall borders and the best will be adopted. Segments
      with increased calcific burden and blooming artifacts will be identified and in case of
      significant differences between CTCA and NIRS-IVUS annotations, machine learning techniques,
      that take advantage of the information provided by NIRS-IVUS, will be implemented to optimise
      CTCA segmentation. The adaptive Hounsfield unit cut-offs that best identify lipid and
      calcific tissue will be defined. Spread-out vessel plots portraying the distribution of the
      lipid tissue in the CTCA models will be created and in these the LCBICT will be estimated for
      each 2mm segment and compared with the output of NIRS. Area under the curve (AUC) analysis
      will be used to identify the best CT-derived plaque burden, LCBI and ESS cut-off values that
      correspond to the NIRS-IVUS cutoff values that indicate high-risk plaques (plaque burden:
      67%, LCBI: 178 and ESS: 1Pa). The block chemogram in NIRS-IVUS will be used to identify the
      2mm LCBICT cut-off that enables accurate classification of the 2mm segments in as lipid or
      non-lipid rich. The accuracy of these cut-offs will be tested in the validation dataset.

      In addition, in the validation dataset the NIRS-IVUS data will be used to identify coronary
      lesions - defined as segments with a plaque burden >40% in 3 consecutive frames. For each
      lesion its remodelling index will be estimated and used to classify them as lesions with a
      positive or negative remodelling. The NIRS-IVUS data will be used to characterise their
      phenotype and classify them as: pathological intimal thickening/fibrotic plaques,
      fibro-calcific plaques, fibroatheromas (FA), and calcified fibroatheromas. The NIRS-IVUS
      lesion classification will be used as reference standard in order to assess the accuracy of
      CTCA in characterising lesion phenotype.

      STATISTICAL ANALYSIS - POWER CALCULATION The primary endpoint of the study is the ability of
      CTCA in detecting FA. In a study of Garcia-Garcia that included 129 patients undergoing singe
      vessel IVUS imaging, 1.7 lesions were identified per patient. In the study of Puri et al.,
      45% of the lesions were FA on histology. In that study NIRS combined with IVUS enabled
      detection of FA with an excellent accuracy (c-index: 0.80). We anticipate that we will be
      able to perform NIRS-IVUS imaging in 2.5 coronary arteries per patient and that CTCA imaging
      quality will be optimal in 93% of the studied patients. If we recruit 70 patients we
      anticipate to successfully study with NIRS-IVUS and CTCA 162 vessels of which 120 (203
      lesions - 92 FA) will be used as a validation dataset. This dataset is anticipated to give an
      80% power to demonstrate using the 5% significance level, that the sensitivity of CTCA in
      identifying FA is not different from NIRS-IVUS (AUC of CTCA range: 0.89-0.71), assuming a
      true sensitivity of 0.80 for NIRS-IVUS.

      Secondary endpoints of the study are the accuracy of CTCA to identify: a) lipid-rich segments
      (using the block chemogram of NIRS-IVUS as gold standard), and b) segments exposed to low ESS
      (<1Pa, using the ESS estimated in the NIRS-IVUS models as reference standard).
    

Intervention name(s): CTCA imaging
Intervention type(s): Radiation
Intervention description(s): All the studied patients will have CTCA before percutaneous coronary intervention and 3 vessel NIRS-IVUS imaging during percutaneous coronary intervention
Other intervention name(s): NIRS-IVUS imaging
Intervention model: Single Group Assignment
Masking: None (Open Label)
Primary purpose: Diagnostic
Keywords: Near Infrared Spectroscopy; Computed Tomography Coronary Angiography; Atherosclerosis; Endothelial Shear Stress