Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis

Autor: Michiel Voskuil, Ivana Išgum, Max A. Viergever, Majd Zreik, Robbert W. van Hamersvelt, Tim Leiner
Rok vydání: 2018
Předmět:
Male
Artificial intelligence
Fractional Flow Reserve
Myocardial/physiology
Fractional flow reserve
Heart Ventricles/diagnostic imaging
Coronary Angiography
Coronary artery disease
Severity of Illness Index
Ventricular Function
Left
030218 nuclear medicine & medical imaging
0302 clinical medicine
Ventricular Function
Myocardial/physiology
Neuroradiology
Computed tomography angiography
Computed Tomography Angiography/methods
medicine.diagnostic_test
Left/physiology
Interventional radiology
General Medicine
Middle Aged
Fractional Flow Reserve
Fractional Flow Reserve
Myocardial
Radiology Nuclear Medicine and imaging
030220 oncology & carcinogenesis
Female
Radiology
Cardiac
Algorithms
medicine.medical_specialty
Myocardial ischemia
Heart Ventricles
Observational Study
03 medical and health sciences
Deep Learning
Multidetector Computed Tomography/methods
Multidetector Computed Tomography
Severity of illness
Journal Article
medicine
Humans
Radiology
Nuclear Medicine and imaging
Coronary Stenosis/diagnosis
Retrospective Studies
Coronary Angiography/methods
business.industry
Coronary Stenosis
Reproducibility of Results
Retrospective cohort study
medicine.disease
Stenosis
Ventricular Function
Left/physiology
business
Zdroj: European Radiology, 29(5), 2350. Springer Verlag
European Radiology
ISSN: 1432-1084
0938-7994
Popis: Objectives To evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis (DS), for identification of patients with functionally significant coronary artery stenosis. Methods Patients who underwent CCTA prior to an invasive fractional flow reserve (FFR) measurement were retrospectively selected. Highest DS from CCTA was used to classify patients as having non-significant (≤ 24% DS), intermediate (25–69% DS), or significant stenosis (≥ 70% DS). Patients with intermediate stenosis were referred for fully automatic DL analysis of the LVM. The DL algorithm characterized the LVM, and likely encoded information regarding shape, texture, contrast enhancement, and more. Based on these encodings, features were extracted and patients classified as having a non-significant or significant stenosis. Diagnostic performance of the combined method was evaluated and compared to DS evaluation only. Functionally significant stenosis was defined as FFR ≤ 0.8 or presence of angiographic high-grade stenosis (≥ 90% DS). Results The final study population consisted of 126 patients (77% male, 59 ± 9 years). Eighty-one patients (64%) had a functionally significant stenosis. The proposed method resulted in improved discrimination (AUC = 0.76) compared to classification based on DS only (AUC = 0.68). Sensitivity and specificity were 92.6% and 31.1% for DS only (≥ 50% indicating functionally significant stenosis), and 84.6% and 48.4% for the proposed method. Conclusion The combination of DS with DL analysis of the LVM in intermediate-degree coronary stenosis may result in improved diagnostic performance for identification of patients with functionally significant coronary artery stenosis. Key Points • Assessment of degree of coronary stenosis on CCTA has consistently high sensitivity and negative predictive value, but has limited specificity for identifying the functional significance of a stenosis. • Deep learning algorithms are able to learn complex patterns and relationships directly from the images without prior specification of which image features represent presence of disease, and thereby may be more sensitive to subtle changes in the LVM caused by functionally significant stenosis. • Addition of deep learning analysis of the left ventricular myocardium to the evaluation of degree of coronary artery stenosis improves diagnostic performance and increases specificity of resting CCTA. This could potentially decrease the number of patients undergoing invasive coronary angiography. Electronic supplementary material The online version of this article (10.1007/s00330-018-5822-3) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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