Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology
Graphical abstract
Introduction
The assessment of coronary plaque composition is essential in treatment planning and predicting cardiovascular events. Calcific atherosclerotic plaques have been associated with a higher risk of procedural complications, stent underexpansion and suboptimal percutaneous coronary intervention (PCI) results [[1], [2]], [[,2] while lipid-rich lesions appear to be associated with non-reflow and peri-procedural myocardial infarction [[3], [4], [5]]. Furthermore, cumulative data have demonstrated that lipid-rich atherosclerotic plaques are vulnerable lesions and predict future adverse cardiovascular events [[6], [7], [8], [9]].
Over recent years several methodologies aiming to accurately characterize and quantify plaque components in intravascular imaging have been introduced. Echogenicity [10] and radiofrequency backscatter analysis [[11], [12], [13]] were the first approaches proposed for reliable detection of different tissue types in intravascular ultrasound (IVUS) while in optical coherence tomography (OCT), backscatter and attenuation coefficient analysis [[14], [15], [16]] of the reflected signal, as well as machine learning techniques [17,18] have been proposed. However, none of them have found broad application in current clinical practice due to a lack of widely available, user-friendly software incorporating these methodologies or robust and consistent histological evidence to support their accuracy.
To address this unmet need, near-infrared spectroscopy (NIRS)-IVUS has been introduced. NIRS-IVUS appears capable of overcoming the limitations of standalone intravascular imaging as NIRS can accurately identify necrotic core (NC) tissue, while IVUS can detect the presence of calcium [19]. However, while NIRS can estimate the circumferential arc of NC, it is unable to provide explicit depth information, differentiate superficial from deeply-embedded NC tissue and assess its area and volumetric burden. Similarly, there is no methodology today for the automated quantification of the calcific tissue in IVUS images.
The aim of this study is to investigate for the first time the efficacy of tissue echogenicity combined with NIRS in accurately detecting distribution of plaque components and quantifying their burden, using histology as reference standard.
Section snippets
Studied patients
We retrospectively analyzed NIRS-IVUS and histological data from excised cadaver hearts collected in a previously described study that aimed to examine the efficacy of NIRS imaging in detecting NC [International Institute for the Advancement of Medicine, Edison, NJ, and Asterand Bioscience (BioIVT), Detroit, MI] [20]. Donated hearts were received within 48 h of death, with intravascular imaging performed within 96 h. Totally occluded segments and those with minimum luminal diameter <1 mm were
Results
Fifteen coronary arteries from thirteen autopsied hearts were included in the analysis. Baseline heart donor demographics are shown in Supplementary Table 2 and baseline IVUS measurements in Supplementary Table 3. The mean lengths of the studied segments was 46.0 ± 12.5 mm for the training set and 51.3 ± 8.4 mm for the test set, providing 346 histological sections for matching (220 for the training set and 126 for the test set). From these, 42 histological sections were excluded because of
Discussion
In this study, we examined for the first time the efficacy of echogenicity combined with the information gleaned from NIRS in characterizing plaque composition in NIRS-IVUS images. We found that: 1) NIRS is very sensitive in detecting ENC and LNC tissue, 2) IVUS pixel intensity enables accurate detection of calcific ROIs but it is unable to differentiate F-PIT from ENC and has limited efficacy in detecting LNC ROIs, and 3) the information provided by NIRS improves the efficacy of echogenicity
CRediT authorship contribution statement
Retesh Bajaj: Methodology, Investigation, Data curation, Formal analysis, Writing – original draft, Validation. Jeroen Eggermont: Methodology, Software, Validation, Writing – review & editing, and. Stephanie J. Grainger: Resources, Data curation, Writing – review & editing. Lorenz Räber: Writing – review & editing. Ramya Parasa: Writing – review & editing. Ameer Hamid A. Khan: Writing – review & editing. Christos Costa: Writing – review & editing. Emrah Erdogan: Writing – review & editing.
Declaration of competing interests
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: MJH and SJG are employees of Infraredx. All other authors have no conflicts of interests to declare.
Acknowledgements
The authors wish to acknowledge the Cardiovascular Devices Hub at the Centre for Cardiovascular Medicine and Devices, Queen Mary University of London for supporting the present study. RB, RP, MA, AM, AB and CVB are funded by Barts NIHR Biomedical Research Centre, London, UK.
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