Adoption of a new automated optical coherence tomography software to obtain a lipid plaque spread-out plot
Authors: Biccire F G , Budassi S , Isidori F , Lella E , Marco V , Ozaki Y , La Manna A , Bourantas C V , Paoletti G , Fabiocchi F , Gatto L , Burzotta F , Arbustini E , Alfonso F , Prati F ,   CLI foundation
Published: October 2021

Abstract

Background
Near infrared spectroscopy – intravascular ultrasound (NIRS-IVUS) imaging can provide a fully automated estimation of lipid burden, providing a two-dimensional spread-out plot, the Lipid Core Burden Index (LCBI), which has been associated with higher incidence of cardiac events. Optical coherence tomography (OCT) can identify lipid component with high accuracy and it is therefore potentially capable of measuring its longitudinal extension in a dedicated two-dimensional LCBI spread-out plot.

Purpose
The present study has been designed to validate a novel automated approach to assess OCT images, able of providing a dedicated LCBI spread-out plot plus other features of plaque vulnerability.

Methods
We compared the results obtained with a novel automated OCT alghorithm, developed utilising a convolutional neural network, with those obtained with conventional (manual) OCT and with NIRS-IVUS in a consecutive series of 40 patients with coronary artery disease. We tested and validated our new OCT algorithm to calculate the lipid core longitudinal extension in a dedicated two-dimensional LCBI spread-out plot. In each coronary plaque, the following measurements were obtained with NIRS-IVUS: 1) minimum lumen area (MLA), 2) vessel area at MLA site, 3) plaque burden (%) at MLA site, 4) NIRS-defined lipid pool arch and 5) maximum LCBI measurement within a 4 mm length. The following OCT features were obtained: 1) the MLA cross section, 2) the minimum fibrous cap thickness (FCT) in presence of lipid components and measured as the average of three measurements obtained in the same cross-section and 3) maximum LCBI within a 4 mm length.

Results
Three lesions groups were identified according to the studied lesions: 1) culprit lesions in patients with acute coronary syndrome (ACS, n=16), 2) non-culprit lesions in patients with ACS (n=12) and 3) lesions in patients with stable angina (n=12). OCT conventional assessment showed for the culprit ACS plaques a trend for a larger lipid arc and a significant thinner FCT (p=0.028). Consistently, NIRS-IVUS showed for culprit ACS plaques a more complex anatomy. A strong trend for increased maximum LPBI in 4mm segments was found in the culprit ACS group, regardless of the adopted imaging modality, either NIRS-IVUS or automated OCT (p=0.184 and p=0.066, respectively, figure 1). A fair correlation was obtained for the maximum 4 mm LCBI measured by NIRS-IVUS and automated OCT (r=0.75). The sensitivity and specificity of automated OCT to detect significant LCBI, applying a validated 400 cut off were 90.5 and 84.2 respectively.

Conclusions
We developed an automated approach, comparable to NIRS, to assess OCT images that can provide a dedicated lipid plaque spread-out plot to address plaque vulnerability. The automated OCT software can promote and improve OCT clinical applications for the identification of patients at risk of hard events.

Funding Acknowledgement
Type of funding sources: Foundation. Main funding source(s): CLI - Centro Lotta all'Infarto Spread-out plot by IVUS-NIRS and OCT