Clinical Relevance of Coronary Computed Tomography Angiography Beyond Coronary Artery Stenosis

Abstract

Background The capabilities of coronary computed tomography angiography (CCTA) have advanced significantly in the past decade. Its capacity to detect stenotic coronary arteries safely and consistently has led to a marked decline in invasive diagnostic angiography. However, CCTA can do much more than identify coronary artery stenoses.

Method This review discusses applications of CCTA beyond coronary stenosis assessment, focusing in particular on the visual and quantitative analysis of atherosclerotic plaque.

Results Established signs of visually assessed high-risk plaque on CT include positive remodeling, low-attenuation plaque, spotty calcification, and the napkin-ring sign, which correlate with the histological thin-cap fibroatheroma. Recently, quantification of plaque subtypes has further improved the assessment of coronary plaque on CT. Quantitatively assessed low-attenuation plaque, which correlates with the necrotic core of the thin-cap fibroatheroma, has demonstrated superiority over stenosis severity and coronary calcium score in predicting subsequent myocardial infarction. Current research aims to use radiomic and machine learning methods to further improve our understanding of high-risk atherosclerotic plaque subtypes identified on CCTA.

Conclusion Despite rapid technological advances in the field of coronary computed tomography angiography, there remains a significant lag in routine clinical practice where use is often limited to lumenography. We summarize some of the most promising techniques that significantly improve the diagnostic and prognostic potential of CCTA.

Key Points: 

• In addition to its ability to determine severity of luminal stenoses, CCTA provides important prognostic information by evaluating atherosclerotic plaque.

• Simple scoring systems such as the segment involved score or the CT-adapted Leaman score can provide more prognostic information on major adverse coronary events compared to traditional risk factors such as presence of hypertension or diabetes.

• CT signs of high-risk plaque, including positive remodeling, low-attenuation plaque, spotty calcification, and the napkin-ring sign, are significantly more likely to predict acute coronary syndromes.

• Quantitative plaque assessment can provide precise description of volume and burden of plaque subtypes and have been found to predict subsequent myocardial infarction better than cardiovascular risk scores, calcium scoring and severity of coronary artery stenoses.

• Machine learning techniques have the potential to automate risk stratification and enhance health economy, even though present clinical applications are limited. In this era of “big data” they are an exciting avenue for future research.

Citation Format

• Meah MN, Williams MC. Clinical Relevance of Coronary Computed Tomography Angiography Beyond Coronary Artery Stenosis. Fortschr Röntgenstr 2021; 193: 1162 – 1170

Zusammenfassung

Hintergrund Die Möglichkeiten der CT-Angiographie der Koronargefäße (CCTA) haben sich in den letzten 10 Jahren erheblich weiterentwickelt. Ihre Fähigkeit, stenotische Koronararterien sicher und konsistent zu erkennen, hat zu einem deutlichen Rückgang der invasiven diagnostischen Angiografie geführt. Die CCTA kann jedoch viel mehr als nur Stenosen in den Koronararterien erkennen.

Methode In dieser Übersichtsarbeit werden Anwendungen der CCTA über die Beurteilung der Koronarstenose hinaus erörtert, wobei insbesondere die visuelle und quantitative Analyse von atherosklerotischen Plaques im Mittelpunkt steht.

Ergebnisse Zu den etablierten Zeichen einer visuell beurteilten Hochrisiko-Plaque in der CT gehören positives Remodeling, Plaque mit geringer Abschwächung, fleckige Verkalkung und das Napkin-Ring-Zeichen, die mit dem histologischen Thin-Cap-Fibroatherom korrelieren. In jüngster Zeit hat die Quantifizierung von Plaque-Subtypen die Beurteilung von Koronarplaque im CT weiter verbessert. Quantitativ bewertete Plaques mit geringer Abschwächung, die mit dem nekrotischen Kern des Thin-Cap-Fibroatheroms korrelieren, haben bei der Vorhersage des nachfolgenden Myokardinfarkts eine Überlegenheit gegenüber dem Schweregrad der Stenose und dem koronaren Kalzium-Score gezeigt. Die aktuelle Forschung zielt darauf ab, radiomische und maschinelle Lernmethoden zu nutzen, um unser Verständnis der mittels CCTA identifizierten atherosklerotischen Hochrisiko-Plaque-Subtypen weiter zu verbessern.

Schlussfolgerung Trotz des schnellen technologischen Fortschritts auf dem Gebiet der Koronar-Computertomografie-Angiografie gibt es in der täglichen klinischen Praxis, wo der Einsatz häufig auf die Lumenografie beschränkt ist, noch einen erheblichen Rückstand. Wir fassen einige der vielversprechendsten Techniken zusammen, die das diagnostische und prognostische Potenzial der CCTA deutlich verbessern.

Kernaussagen: 

• Neben der Fähigkeit, den Schweregrad von Luminalstenosen zu bestimmen, liefert die CCTA durch die Beurteilung atherosklerotischer Plaques wichtige prognostische Informationen.

• Einfache Scoring-Systeme wie der Segment-Involved-Score oder der CT-adaptierte Leaman-Score können im Vergleich zu herkömmlichen Risikofaktoren wie Bluthochdruck oder Diabetes mehr prognostische Informationen über schwerwiegende unerwünschte koronare Ereignisse liefern.

• CT-Zeichen einer Hochrisiko-Plaque, einschließlich positiver Remodellierung, Plaque mit geringer Abschwächung, fleckiger Verkalkung und dem Napkin-Ring-Zeichen, sagen signifikant häufiger akute Koronarsyndrome voraus.

• Die quantitative Plaque-Bewertung kann eine präzise Beschreibung des Volumens und der Belastung der Plaque-Subtypen liefern, und es wurde festgestellt, dass sie einen nachfolgenden Myokardinfarkt besser vorhersagen kann als kardiovaskuläre Risiko-Scores, Kalzium-Scoring und der Schweregrad der Koronararterienstenosen.

• Machine Learning hat das Potenzial, die Risikostratifizierung zu automatisieren und die Gesundheitsökonomie zu verbessern, auch wenn die derzeitigen klinischen Anwendungen begrenzt sind. In dieser Zeit der „Big Data“ sind sie ein aufregender Weg für die zukünftige Forschung.

Key words

cardiac - CT-angiography - angiography

Publication History

Received: 25 November 2020

Accepted: 07 February 2021

Article published online:
26 March 2021

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