Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This work was supported in part from European Regional Development Fund, Operational Programme “Competitiveness, Entrepreneurship and Innovation 2014-2020 (EPAnEK)”, titled: The Greek Research Infrastructure for Personalized Medicine (pMED-GR) , no. GR 5002802 ,and by Greece and the European Union (European Social Fund-ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014-2020» in the context of the project “Assessment of coronary atherosclerosis: a new complete, anatomo-functional, morphological and biomechanical approach”, Project no. 504776 Background Computed Tomography Coronary Angiography (CTCA) is a non-invasive imaging modality, used effectively for anatomo-functional assessment of coronary artery disease (CAD). Machine learning (ML) processes can effectively allow the extraction of useful information from multidimensional feature spaces for evaluation of coronary lesions. Purpose To investigate the ability of ML for predicting impaired myocardial blood flow (MBF) by combining computational fluid dynamics (CFD) derived parameters with quantitative plaque burden, plaque morphology and anatomical characteristics obtained from CTCA. Methods 53 patients (31 male, mean age 64.7 ± 7.1 years) with intermediate pre-test likelihood of CAD who underwent CTCA and PET-MPI were included. PET was considered positive when > 1 contiguous segment demonstrated MBF ≤ 2.3 mL/g/min for 15O-water or ≤ 1.79 for 13N-ammonia respectively. CFD derived parameters such as a previously validated, virtual functional assessment index (vFAI), segmental endothelial shear stress (ESS), as well as anatomical and plaque characteristics were assessed. Seven classifiers were implemented and internally validated using 5-fold cross validation, repeated 1000 times. Using sequential forward selection (SFS), the highest rank features combination, based on appearances in the highest mean area under curve (AUC) classification scheme, was selected and features performance was evaluated following exhaustive search (ES). Results 92 coronary segments were analyzed and 34 features derived from CTCA were extracted. Classifiers performance are depicted in Figure A. k-NN was the best classifier with AUCmean = 0.791 (SENSmean= 0.622, SPECmean = 0.840, p < 0.05). Clusters of features and number of appearances are presented in Figure B. The combination of vFAI, stenosis severity and lumen area demonstrated the highest AUC (1473 times). ES results are depicted in Figure C. The combination of vFAI and lumen area was the best predictor among all the combinations (AUCmean = 0.830, SENSmean =0.61, SPECmean =0.83, p < 0.05) followed by vFAI and stenosis severity (AUCmean = 0.81, SENSmean =0.72, SPECmean = 0.87, p < 0.05) and vFAI alone (AUCmean = 0.806, SENSmean =0.61, SPECmean =0.87, p < 0.05). Conclusion ML analysis is feasible for predicting with reasonable specificity abnormal MBF by PET, using a combination of CFD derived parameters and anatomical features. vFAI as a single characteristic was a specific predictor of impaired MBF, whilst in combination with stenosis severity, maintained almost the same AUC and specificity values and resulted in improved sensitivity. On the other hand, addition of lumen area to vFAI, increases the AUC and provides a relatively good specificity but low  sensitivity. Abstract Figure 1

Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This work was supported in part from European Regional Development Fund, Operational Programme “Competitiveness, Entrepreneurship and Innovation 2014-2020 (EPAnEK)”, titled: The Greek Research Infrastructure for Personalized Medicine (pMED-GR) , no. GR 5002802 ,and by Greece and the European Union (European Social Fund-ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014-2020» in the context of the project “Assessment of coronary atherosclerosis: a new complete, anatomo-functional, morphological and biomechanical approach”, Project no. 504776 onbehalf EVINCI-SMARTOOL Background/Objectives: The relationship between biomechanical characteristics of a coronary lesion with myocardial blood flow has not been studied. We investigated the relationship between local endothelial shear stress (ESS) and computed tomography coronary angiography (CTCA)-derived anatomical and plaque characteristics data with impaired vasodilating capability assessed by positron emission tomography myocardial perfusion imaging (PET-MPI). Methods A total of 92 coronary vessels of 53 patients who have undergone both CTCA and PET-MPI with 15O-water or 13N-ammonia were analysed. PET was considered abnormal when > 1 contiguous segments showed both stress Myocardial Blood Flow (MBF) ≤2.3mL/g/min and Myocardial Flow Reserve (MFR) ≤2.5 for 15O-water or <1.79 mL/g/min and ≤2.0 for 13N-ammonia respectively.  CTCA images were used to assess stenosis severity, lesion specific total plaque volume (PV), non-calcified PV and calcified PV as well as plaque phenotype. ESS was calculated for the full length of a lesion (total), as well as in the proximal, minimum lumen area and distal lesion segments. Results ESS was weakly correlated with total PV (rho = 0.273, p = 0.008), non-calcified PV (rho = 0.247, p = 0.017) and the volume of necrotic core (rho = 0.242, p = 0.02). ESS increased progressively with stenosis severity (p ≤ 0.001). ΕSS was also higher in functionally significant vs. non-significant lesions (10.4 [8.04-54.4] Pa vs. 3.9 [2.32-7.29] Pa, p ≤0.001). Addition of ESS to stenosis severity improved prediction (Δ[AUC]:0.113, 95% CI: 0.055 to 0.171, p = 0.0001) of functionally significant lesions. Conclusion There is a weak positive association between lesion-specific ESS and plaque volume. ESS increases progressively with stenosis severity and is higher in functionally significant lesions by PET-MPI. The addition of ESS to CTCA-anatomical information improves prediction of an abnormal PET-MPI result.