Radiomics-based analysis by machine learning techniques improves characterization of functionally significant coronary lesions

Abstract Background Computed Tomography Coronary Angiography (CTCA) is an effective non-invasive imaging modality for anatomo-functional assessment of coronary artery disease (CAD). Radiomics features have been used diagnosis or outcome prediction, however, their potential value characterizing flow limiting lesions has not explored. Purpose To assess whether application novel radiomics and machine learning (ML) techniques on CTCA derived datasets improves characterization functionally significant lesions. Methods Consecutive patients with stable chest pain intermediate pre-test likelihood CAD, who underwent PET-or SPECT-Myocardial Perfusion Imaging (MPI) respectively, were prospectively evaluated included in the analysis. PET-MPI was considered abnormal when >1 contiguous segments showed both stress Myocardial Blood Flow ?2.3mL/g/min Reserve (MFR) ?2.5 15O-water <1.79 mL/g/min ?2.0 13N-ammonia respectively. Defect reversibility (DR) defined as a summed difference score (SDS) between rest images ?2. functional fused to assign each myocardial segment pertinent territory. Stenosis severity, plaque characteristics radiomic assessed total length 3 main vessels. In total, 1765 extracted from vessel feature reduction model creation pipeline constructed [Figure 1]. Two separate datasets: a) stenosis (?50%) + b) formulated compared terms AUCs accordingly. Results A 292 vessels (140 corresponding data 152 SPECT MPI data) analysed. Plaque burden severity only independent predictors impaired perfusion PET-MPI, AUC = 0.749, (95% CI: 0.658–0.826). kurtosis, contrast, interquartile range entropy result combination resulted 0.854, 0.775–0.914). The 2 models statistically (p-diff: 0.02, 95% 0.0165–0.194). predictor DR SPECT-MPI, 0.624 0.542–0.702). Small Dependence High Gray Level Emphasis, Cluster Prominence, Region Length, wavelet Median square positive result, 0.816, 0.745–0.875). two 0.006, 0.152–0.329) Conclusion Radiomic futures can be combined anatomical morphological provide valuable complementary information Funding Acknowledgement Type funding sources: Public grant(s) – EU funding. Main source(s): This work supported European Regional Development Fund, Operational Programme “Competitiveness, Entrepreneurship Innovation 2014-2022 (EPAnEK)”, titled: Greek Research Infrastructure Personalized Medicine (pMED-GR)