Machine-learning integration of CT histogram analysis to evaluate the composition of atherosclerotic plaques: Validation with IB-IVUS.
Aged
Computed Tomography Angiography
/ methods
Coronary Angiography
/ methods
Coronary Artery Disease
/ diagnostic imaging
Coronary Vessels
/ diagnostic imaging
Female
Humans
Machine Learning
Male
Multidetector Computed Tomography
/ methods
Plaque, Atherosclerotic
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
/ methods
Reproducibility of Results
Retrospective Studies
Ultrasonography, Interventional
Coronary CT angiography
Coronary plaque
Extreme gradient boosting (XGBoost)
Integrated backscatter intravascular ultrasound (IB-IVUS)
Machine learning
Journal
Journal of cardiovascular computed tomography
ISSN: 1876-861X
Titre abrégé: J Cardiovasc Comput Tomogr
Pays: United States
ID NLM: 101308347
Informations de publication
Date de publication:
Historique:
received:
15
02
2018
revised:
02
07
2018
accepted:
19
10
2018
pubmed:
12
12
2018
medline:
14
6
2019
entrez:
12
12
2018
Statut:
ppublish
Résumé
To determine whether machine learning with histogram analysis of coronary CT angiography (CCTA) yields higher diagnostic performance for coronary plaque characterization than the conventional cut-off method using the median CT number. We included 78 patients with 78 coronary plaques who had undergone CCTA and integrated backscatter intravascular ultrasound (IB-IVUS) studies. IB-IVUS diagnosed 32 as fibrous- and 46 as fatty or fibro-fatty plaques. We recorded the coronary CT number and 7 histogram parameters (minimum and mean value, standard deviation (SD), maximum value, skewness, kurtosis, and entropy) of the plaque CT number. We also evaluated the importance of each feature using the Gini index which rates the importance of individual features. For calculations we used XGBoost. Using 5-fold cross validation of the plaque CT number, the area under the receiver operating characteristic curve of the machine learning- (extreme gradient boosting) and the conventional cut-off method was compared. The median CT number was 56.38 Hounsfield units (HU, 8.00-95.90) for fibrous- and 1.15 HU (-35.8-113.30) for fatty- or fibro-fatty plaques. The calculated optimal threshold for the plaque CT number was 36.1 ± 2.8 HU. The highest Gini index was the coronary CT number (0.19) followed by the minimum value (0.17), kurtosis (0.17), entropy (0.14), skewness (0.11), the mean value (0.11), the standard deviation (0.06), and the maximum value (0.05), and energy (0.00). By validation analysis, the machine learning-yielded a significantly higher area under the curve than the conventional method (area under the curve 0.92 and 95%, confidence interval 0.86-0.92 vs 0.83 and 0.75-0.92, p = 0.001). The machine learning-was superior the conventional cut-off method for coronary plaque characterization using the plaque CT number on CCTA images.
Sections du résumé
BACKGROUND
BACKGROUND
To determine whether machine learning with histogram analysis of coronary CT angiography (CCTA) yields higher diagnostic performance for coronary plaque characterization than the conventional cut-off method using the median CT number.
METHODS
METHODS
We included 78 patients with 78 coronary plaques who had undergone CCTA and integrated backscatter intravascular ultrasound (IB-IVUS) studies. IB-IVUS diagnosed 32 as fibrous- and 46 as fatty or fibro-fatty plaques. We recorded the coronary CT number and 7 histogram parameters (minimum and mean value, standard deviation (SD), maximum value, skewness, kurtosis, and entropy) of the plaque CT number. We also evaluated the importance of each feature using the Gini index which rates the importance of individual features. For calculations we used XGBoost. Using 5-fold cross validation of the plaque CT number, the area under the receiver operating characteristic curve of the machine learning- (extreme gradient boosting) and the conventional cut-off method was compared.
RESULTS
RESULTS
The median CT number was 56.38 Hounsfield units (HU, 8.00-95.90) for fibrous- and 1.15 HU (-35.8-113.30) for fatty- or fibro-fatty plaques. The calculated optimal threshold for the plaque CT number was 36.1 ± 2.8 HU. The highest Gini index was the coronary CT number (0.19) followed by the minimum value (0.17), kurtosis (0.17), entropy (0.14), skewness (0.11), the mean value (0.11), the standard deviation (0.06), and the maximum value (0.05), and energy (0.00). By validation analysis, the machine learning-yielded a significantly higher area under the curve than the conventional method (area under the curve 0.92 and 95%, confidence interval 0.86-0.92 vs 0.83 and 0.75-0.92, p = 0.001).
CONCLUSION
CONCLUSIONS
The machine learning-was superior the conventional cut-off method for coronary plaque characterization using the plaque CT number on CCTA images.
Identifiants
pubmed: 30529218
pii: S1934-5925(18)30440-4
doi: 10.1016/j.jcct.2018.10.018
pii:
doi:
Types de publication
Comparative Study
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
163-169Informations de copyright
Copyright © 2018. Published by Elsevier Inc.