Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Zhang, Lu; Sun, Jianqing; Jiang, Beibei; Wang, Lingyun; Zhang, Yaping; Xie, Xueqian

doi:10.1186/s41824-021-00107-0

Cited by 19 publications

(16 citation statements)

References 49 publications

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“…Since clinical decision-making should depend on the coronary functional significance, our study provides new evidence for the diagnosis and treatment of CAD by mining the functional correlation from anatomical CT. Radiomics can extract a large number of imaging features from CCTA from a computational point of view. Meanwhile, machine learning can effectively select valuable information from numerous features and establish predictive models (Zhang et al, 2021). In our study, the Rad-signature was a powerful predictor and independent influential factor of functional ischemia.…”

Section: Figurementioning

confidence: 72%

“…Radiomics can extract a large number of imaging features from CCTA from a computational point of view. Meanwhile, machine learning can effectively select valuable information from numerous features and establish predictive models ( Zhang et al, 2021 ). In our study, the Rad-signature was a powerful predictor and independent influential factor of functional ischemia.…”

Section: Discussionmentioning

confidence: 99%

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Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia

Feng

Zhang

et al. 2022

Front. Physiol.

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Objectives: To determine the association between radiomics signature (Rad-signature) of pericoronary tissue (PCT) in coronary computed tomography angiography (CCTA) and CT-derived fractional flow reserve (CT-FFR), and explore the influential factors of functional ischemia.Methods: We retrospectively included 350 patients who underwent CCTA from 2 centers, consisting of the training (n = 134), validation (n = 66), and testing (with CCTA and invasive coronary angiography, n = 150) groups. After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. Another 45 patients who underwent CCTA and invasive FFR were included to assure the performance of Rad-signature.Results: A total of 1046 vessels in 350 patients were analyzed, and functional ischemia was identified in 241/1046 (23.0%) vessels and 179/350 (51.1%) patients. From the 47 features highly relevant to functional ischemia, the top-8 contributive features were selected to establish Rad-signature. At the vessel level, the area under the curve (AUC) of Rad-signature to discriminate functional ischemia was 0.83, 0.82, and 0.82 in the training, validation, and testing groups, higher than 0.55, 0.55, and 0.52 of FAI (p < 0.001), respectively, and was higher than 0.72 of anatomical CT in the testing group (p = 0.017). The AUC of the combined model (Rad-signature + anatomical CT) was 0.86, 0.85, and 0.83, respectively, significantly higher than that of anatomical CT and FAI (p < 0.05). In the CCTA-invasive FFR group, using invasive FFR as the standard, the mean AUC of Rad-signature was 0.83 ± 0.02. At the patient level, multivariate logistic regression analysis showed that Rad-signature of left anterior descending (LAD) [odds ratio (OR) = 1.72; p = 0.012] and anatomical CT (OR = 3.53; p < 0.001) were independent influential factors of functional ischemia (p < 0.05). In the subgroup of nonobstructive (stenosis <50% in invasive coronary angiography) and obstructive (≥50%) cases of the testing group, the independent factor of functional ischemia was FAI of LAD (OR = 1.10; p = 0.041) and Rad-signature of LAD (OR = 2.45; p = 0.042), respectively.Conclusion: The machine-learning-derived Rad-signature of PCT in CCTA demonstrates significant association with functional ischemia.

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Section: Figurementioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia

Feng

Zhang

et al. 2022

Front. Physiol.

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“…Most previously published data from larger cohorts relies on measurements with manual annotations or semi-automatic techniques 6 , 7 , 13 , 32 – 36 . In a recent systematic review by Zhang et al 22 , seven studies reporting on model-based methods, all prior to 2017, and nine studies reporting on deep-learning based methods applied to non-contrast cardiac CT were included. Among these, the fully automatic model described by Commandeur et al is the only one developed and tested in a larger population sample, with an impressive Dice coefficient of 0.82 for EATV in the first report 21 , which was later improved to 0.87, when the model was adapted to multicenter use 23 .…”

Section: Discussionmentioning

confidence: 99%

“…In attempts to develop automatized techniques, intensity and region growing 16 , 17 , multi-atlas 18 , 19 and deep learning 20 , 21 based approaches have been evaluated. A recent systematic review of the field by Zhang et al 22 shows that research is trending towards the latter approach, with all of the seven methodological works on non-contrast CT images published after 2018 applying various aspects of deep learning. Five of the featured works comprise small samples, in the range of 20–53 individuals, while two works have studied larger populations, both by Commandeur et al They describe a fully automated model based on a trained convolutional neural network (CNN) 21 , which achieved a Dice coefficient of 0.82 for EATV.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

Molnár

Enqvist

Ulén³

et al. 2021

Sci Rep

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To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.

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“…Although studies have not used these techniques for PCAT, they have been extensively used for EAT assessment. 18 …”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for advanced analysis of coronary plaque

Assen

Doeberitz

Quyyumi

et al. 2023

European Heart Journal Supplements

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The field of coronary plaque analysis is advancing including more quantitative analysis of coronary artery diseases such as plaque burden, high-risk plaque features, computed tomography-derived fractional flow reserve, and radiomics. Although these biomarkers have shown great promise for the diagnosis and prognosis of cardiac patients in a research setting, many of these advanced analyses are labour and time intensive and therefore hard to implement in daily clinical practice. Artificial intelligence (AI) is playing an increasing role in supporting the quantification of these new biomarkers. AI offers the opportunity to increase efficiency, reduce human error and reader variability and to increase the accuracy of diagnosis and prognosis by automating many processing and supporting clinicians in their decision-making. With the use of AI these novel analysis approaches for coronary artery disease can be made feasible for clinical practice without increasing cost and workload and potentially improve patient care.

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Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

Cited by 19 publications

References 49 publications

Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia

Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies

Artificial intelligence for advanced analysis of coronary plaque

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