Abstract
AIMS: Near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) and optical coherence tomography (OCT) can assess coronary plaque pathology but are limited by time-consuming and expertise-driven image analysis. Recently introduced machine learning (ML)-classifiers have expedited image processing, but their performance in assessing plaque pathology against histological standards remains unclear. The aim of this study is to assess the performance of NIRS-IVUS-ML-based and OCT-ML-based plaque characterization against a histological standard. METHODS AND RESULTS: Matched histological and NIRS-IVUS/OCT frames from human cadaveric hearts were manually annotated and fibrotic (FT), calcific (Ca), and necrotic core (NC) regions of interest (ROIs) were identified. Near-infrared spectroscopy-intravascular ultrasound and OCT frames were processed by their respective ML classifiers to segment and characterize plaque components. The histologically defined ROIs were overlaid onto corresponding NIRS-IVUS/OCT frames and the ML classifier estimations were compared with histology. In total, 131 pairs of NIRS-IVUS/histology and 184 pairs of OCT/histology were included. The agreement of NIRS-IVUS-ML with histology [concordance correlation coefficient (CCC) 0.81 and 0.88] was superior to OCT-ML (CCC 0.64 and 0.73) for the plaque area and burden. Plaque compositional analysis showed a substantial agreement of the NIRS-IVUS-ML with histology for FT, Ca, and NC ROIs (CCC: 0.73, 0.75, and 0.66, respectively) while the agreement of the OCT-ML with histology was 0.42, 0.62, and 0.13, respectively. The overall accuracy of NIRS-IVUS-ML and OCT-ML for characterizing atheroma types was 83% and 72%, respectively. CONCLUSION: NIRS-IVUS-ML plaque compositional analysis has a good performance in assessing plaque components while OCT-ML performs well for the FT, moderately for the Ca, and has weak performance in detecting NC tissue. This may be attributable to the limitations of standalone intravascular imaging and to the fact that the OCT-ML classifier was trained on human experts rather than histological standards.