Abstract

BackgroundCoronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability.MethodsBaseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results.ResultsFor LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847.ConclusionThis feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

Highlights

  • Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available

  • We proposed a 3D-fluid– structure interaction (FSI) modeling approach combining intravascular ultrasound (IVUS) and optical coherence tomography (OCT) for more accurate morphological and mechanical quantifications [24]

  • Prediction of morphological indices using single‐factor predictor Using ΔLPI, ΔCTI and ΔMPVI as plaque vulnerability change, respectively, 13 key risk factors at baseline were used as predictors to feed four machine learning methods

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Summary

Introduction

Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Plaque rupture is a main cause of arterial thrombosis which could lead to stroke or heart attack [1]. Detection of rupture-prone plaques will be an important advance in atherosclerotic disease prevention. The AHA plaque classification scheme based on qualitative histology has been considered as the standard and guideline for plaque research for decades. A more quantitative classification of atherosclerotic plaques was given based on a large number of histological data and analysis [6, 7]. Kolodgie et al pointed out that plaque prone to rupture ( called thin-cap fibroatheroma) had three main characteristics: large lipid-rich necrotic core, higher prevalence of macrophage infiltration in fibrous cap, and a fibrous cap with thickness < 65 μm [8]. Naghavi et al indicated that the quantitative characteristics of vulnerability could contribute to assessment of vulnerable plaques [9]

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