Identification of non-calcified coronary plaque characteristics using machine learning radiomic analysis of non-contrast high-resolution CT

Abstract

Abstract Objective To explore whether machine learning (ML) radiomic analysis of low-dose, high-resolution, non-contrast, ECG gated cardiac CT scan allows identification of non-calcified coronary plaque characteristics. Background Novel imaging and analysis techniques may provide the ability to detect non-calcified or high risk coronary plaques on a non-contrast CT scan, advancing cardiovascular diagnostics. Methods We prospectively enrolled 125 patients with a non-calcified plaque and an adverse plaque characteristic (APC), and 25 controls without visible atherosclerosis on coronary CT angiography (CCTA). All patients underwent the non-contrast CT exam prior to CCTA. 419 radiomic features were calculated to identify: presence of any CAD, obstructive CAD (stenosis>50%), plaque with ≥2 APC, degree of calcification and specific APCs. ML models were trained on a training set (917 segmentations) and tested on a separate (validation) set (292 segmentations). Results Among the radiomic features 88.3% was associated with any plaque, 0.9% with obstructive CAD and 76.4% with presence of at least two APCs. Overall, 80.2%, 88.5% and 36.5%, of features were associated with calcified, partially calcified, and noncalcified plaques, respectively. Regarding APCs, 61.1%, 61.8%, 84.2%, and 61.3%, of features were associated with low attenuation (LAP), napkin-ring sign (NRS), spotty calcification (SC), and positive remodeling (PR), respectively. ML models outperformed conventional methods for the presence of plaque, obstructive stenosis, presence of 2 APC, as well as for noncalcified plaque and partially calcified plaque, but not for calcified plaque. ML models also significantly outperformed identification of LAP and PR, but neither NRS nor SC. Conclusions Radiomic analysis of non-contrast CT heart exams may allow identification of specific non-calcified coronary plaque characteristics which could aid cardiovascular risk stratification or pre-screening of individuals prior to contrast enhanced CCTA exam. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Science Center