Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography

Abstract

Objectives: The aim of this study was to precisely phenotype culprit and nonculprit lesions in myocardial infarction (MI) and lesions in stable coronary artery disease (CAD) using coronary computed tomography angiography (CTA)-based radiomic analysis.

Background: It remains debated whether any single coronary atherosclerotic plaque within the vulnerable patient exhibits unique morphology conferring an increased risk of clinical events.

Methods: A total of 60 patients with acute MI prospectively underwent coronary CTA before invasive angiography and were matched to 60 patients with stable CAD. For all coronary lesions, high-risk plaque (HRP) characteristics were qualitatively assessed, followed by semiautomated plaque quantification and extraction of 1,103 radiomic features. Machine learning models were built to examine the additive value of radiomic features for discriminating culprit lesions over and above HRP and plaque volumes.

Results: Culprit lesions had higher mean volumes of noncalcified plaque (NCP) and low-density noncalcified plaque (LDNCP) compared with the highest-grade stenosis nonculprits and highest-grade stenosis stable CAD lesions (NCP: 138.1 mm³ vs 110.7 mm³ vs 102.7 mm³; LDNCP: 14.2 mm³ vs 9.8 mm³ vs 8.4 mm³; both P_trend < 0.01). In multivariable linear regression adjusted for NCP and LDNCP volumes, 14.9% (164 of 1,103) of radiomic features were associated with culprits and 9.7% (107 of 1,103) were associated with the highest-grade stenosis nonculprits (critical P < 0.0007) when compared with highest-grade stenosis stable CAD lesions as reference. Hierarchical clustering of significant radiomic features identified 9 unique data clusters (latent phenotypes): 5 contained radiomic features specific to culprits, 1 contained features specific to highest-grade stenosis nonculprits, and 3 contained features associated with either lesion type. Radiomic features provided incremental value for discriminating culprit lesions when added to a machine learning model containing HRP and plaque volumes (area under the receiver-operating characteristic curve 0.86 vs 0.76; P = 0.004).

Conclusions: Culprit lesions and highest-grade stenosis nonculprit lesions in MI have distinct radiomic signatures compared with lesions in stable CAD. Within the vulnerable patient may exist individual vulnerable plaques identifiable by coronary CTA-based precision phenotyping.

Keywords: coronary computed tomography angiography; coronary plaque; machine learning; myocardial infarction; radiomics.

Figure 1.. Manhattan plot of p-values for associations of culprit lesions and highest-grade stenosis nonculprit lesions with radiomic features

P values are displayed on the y axis for each of the 1,103 radiomic parameters lined up on the x axis, color-coded by category. Upper panels show results from unadjusted linear regression analysis; bottom panels show results following adjustment for volumes of calcified, noncalcified, and low-density noncalcified plaque. Points above the red line (p=0.0007) indicate radiomic features significantly associated with either culprit or highest-grade stenosis nonculprit lesions. The highest-grade stenosis lesion in stable CAD was used as reference.

Figure 2.. Clustering dendrogram and heatmap of significant radiomic features associated with culprit vs. highest-grade stenosis nonculprit lesion

There were 9 distinct clusters (red boxes) among the 265 significant radiomic features when comparing the culprit lesion and highest-grade stenosis nonculprit lesion (middle panel). Of these, 5 clusters contained only radiomic features specific to culprit lesions (pink), 1 cluster contained only features specific to nonculprit lesions (green), and 3 clusters contained features associated with either lesion type (1 cluster contained 6 features associated with both lesion types, as shown in blue). The highest-grade stenosis stable CAD lesion was used as reference.

Figure 3.. Feature importance for machine learning identification of culprit lesions

Ranking of feature importance in the final ML model incorporating HRP, quantitative plaque parameters, and radiomic features. The solid bars and error bars represent the mean feature importance value and standard deviation, respectively.

Central Illustration.. Radiomics-based precision phenotyping in myocardial infarction

Plaque segmentation was performed on CCTA images (case example of a culprit lesion shown) using semiautomated software. Noncalcified plaque is displayed in red, low-density noncalcified plaque in orange, and vessel lumen in blue. Radiomic features were extracted from plaque-containing voxels and analyzed using hierarchical clustering. Nested machine learning models were built using conventional plaque parameters and cluster-derived radiomic features; the performance of these models in discriminating culprit lesions is shown on the right. AUC = area under the receiver operating characteristic curve; HRP = high-risk plaque.