OPTICAL COHERENCE TOMOGRAPHY (OCT) CHARACTERISTICS AND IMAGE ANALYSIS FOR IN-STENT RESTENOSIS USING SUPERVISED MACHINE LEARNING TECHNIQUES

Abstract

Despite advancements in stent technology and PCI techniques, in-stent restenosis (ISR) remains a concern. OCT provides an ample amount of data to characterize mechanisms of ISR. Multiple studies showed poor correlation between OCT findings and histological findings in pathological specimen studies. The purpose of this study is to characterize the OCT morphological features of ISR to neo-atherosclerosis (NA) versus neo-intimal hyperplasia (NI) and then study the utility of automated tools for identification of these plaque morphologies. All patients with ISR and OCT image obtained at the QEII health science center in Halifax were included in our study. All OCT images were analysed visually according to homogeneousity, back scatter and lumen characteristics by an expert observer and the mechanism of ISR were categorized as NA vs. NI. Subsequently, image analysis and RGB coding, using imaging software along with supervised machine learning tools, was utilized to test different classification algorithms and predictive models for ISR mechanism. For the purpose of this study we only selected patients characterized as having NA vs. NI as pathogenesis for ISR by visual assessment and compared them to the classification according to automated supervised machine learning. A total of 60 patients with OCT images available for analysis. 48 patients visually characterized with NA or NI were included in this analysis. There was significant difference between the groups in terms of tissue characteristics with 50% of plaques in NI are homogeneous versus 3.6% in NA (P= 0.001). High backscatter occurred in 100% of NI, but there was low backscatter in 78% of NA. Lumen shape was mainly regular in NI compared to irregular in NA (100% versus 14.3% p < 0.000). Plaque rupture was present in 64.3% of NA. There was no significant difference between of the groups in terms of diabetes, hypertension or dyslipidemia. However, there were more current smokers in the NA cohort compared to NI (57% versus 20% P=0.01). Using supervised machine learning techniques, tissue characteristics, backscatter and lumen shape were predictive for the pathogenesis of ISR with 97.9% accuracy. The positive predictive value for the model was 95% for NA versus 100% for NI. There are distinct morphological characteristics for pathogenesis of ISR. These distinct characteristics could be used as predictive models for identification of ISR pathogenesis. Future studies and validation are needed to determine whether automated imaging techniques may help predict the long-term outcomes for ISR with various treatments, according to tissue type.View Large Image Figure ViewerDownload Hi-res image Download (PPT)