A three-dimensional quantification of calcified and non-calcified plaques in coronary arteries based on computed tomography coronary angiography images: Comparison with expert's annotations and virtual histology intravascular ultrasound

Kigka, Vassiliki I.; Sakellarios, Antonis I.; Kyriakidis, Savvas; Rigas, George; Athanasiou, Lambros S.; Siogkas, Panagiotis K.; Tsompou, Panagiota; Loggitsi, Dimitra; Benz, Dominik C.; Buechel, Ronny R.; Lemos, Pedro A.; Pelosi, Gualtiero; Michalis, Lampros K.; Fotiadis, Dimitrios I.

doi:10.1016/j.compbiomed.2019.103409

Cited by 23 publications

(19 citation statements)

References 36 publications

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“…• the unbowed popularity of BA plots, as judged by the number of citations of the seminal papers [1,3] (and a pedagogical update [4]); • the limited extent to which BA plots are reported and interpreted in the literature (e.g., [15][16][17]40]); and • the associated need for standardization, as evidently expressed by the included proposals [13,22,23,[30][31][32][33].…”

Section: Meaning Of the Study: Possible Mechanisms And Implications For Clinicians Or Policymakersmentioning

confidence: 99%

“…The latter extended their work by offering a freely-available online implementation, accompanied by a formal description of the more advanced Bland-Altman comparison methods [14], and they proposed a standard format of reporting a BA agreement analysis. However, recent examples of Bland-Altman plots employing samples as small as 31 [15], 6 and 12 [16], or 14 [17] can be found easily in MEDLINE/PubMed (by searching for "Bland-Altman" and "Agreement" and limiting the search to publication dates in August 2019). These studies did not quantify the uncertainty of the estimated BA LoA by means of confidence intervals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reporting Standards for a Bland–Altman Agreement Analysis: A Review of Methodological Reviews

Gerke

2020

Diagnostics

108

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The Bland–Altman Limits of Agreement is a popular and widespread means of analyzing the agreement of two methods, instruments, or raters in quantitative outcomes. An agreement analysis could be reported as a stand-alone research article but it is more often conducted as a minor quality assurance project in a subgroup of patients, as a part of a larger diagnostic accuracy study, clinical trial, or epidemiological survey. Consequently, such an analysis is often limited to brief descriptions in the main report. Therefore, in several medical fields, it has been recommended to report specific items related to the Bland–Altman analysis. The present study aimed to identify the most comprehensive and appropriate list of items for such an analysis. Seven proposals were identified from a MEDLINE/PubMed search, three of which were derived by reviewing anesthesia journals. Broad consensus was seen for the a priori establishment of acceptability benchmarks, estimation of repeatability of measurements, description of the data structure, visual assessment of the normality and homogeneity assumption, and plotting and numerically reporting both bias and the Bland–Altman Limits of Agreement, including respective 95% confidence intervals. Abu-Arafeh et al. provided the most comprehensive and prudent list, identifying 13 key items for reporting (Br. J. Anaesth. 2016, 117, 569–575). An exemplification with interrater data from a local study accentuated the straightforwardness of transparent reporting of the Bland–Altman analysis. The 13 key items should be applied by researchers, journal editors, and reviewers in the future, to increase the quality of reporting Bland–Altman agreement analyses.

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Section: Meaning Of the Study: Possible Mechanisms And Implications For Clinicians Or Policymakersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reporting Standards for a Bland–Altman Agreement Analysis: A Review of Methodological Reviews

Gerke

2020

Diagnostics

108

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“…3D reconstruction of the coronary lumen and outer vessel wall was performed using an in-house software, which provided measurements of lumen area, plaque volume and plaque burden as previously described and validated 12,13 . Finally, baseline and follow-up CTCA scans were co-registered using landmarks such as the bifurcations and calcified objects.…”

Section: Methodsmentioning

confidence: 99%

Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data

Pleouras

Sakellarios

Tsompou

et al. 2020

Sci Rep

Self Cite

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Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for prevention strategies. In this work, a novel computational model is developed, which is used for simulation of plaque growth to 94 realistic 3D reconstructed coronary arteries. This model considers several factors of the atherosclerotic process even mechanical factors such as the effect of endothelial shear stress, responsible for the initiation of atherosclerosis, and biological factors such as the accumulation of low and high density lipoproteins (LDL and HDL), monocytes, macrophages, cytokines, nitric oxide and formation of foams cells or proliferation of contractile and synthetic smooth muscle cells (SMCs). The model is validated using the serial imaging of CTCA comparing the simulated geometries with the real follow-up arteries. Additionally, we examine the predictive capability of the model to identify regions prone of disease progression. The results presented good correlation between the simulated lumen area (P < 0.0001), plaque area (P < 0.0001) and plaque burden (P < 0.0001) with the realistic ones. Finally, disease progression is achieved with 80% accuracy with many of the computational results being independent predictors.

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“…While 3D reconstruction of plaque using CT slices would alert the physician to the presence of coronary artery disease at an early stage, it is advantageous that the investigator concentrates on hybrid feature extraction techniques when characterizing plaques using CAD based on CCTA images. There are challenges involved in the development of efficient CAD to characterize plaque [138][139][140]. These are discussed in detail below.…”

Section: Research Opportunities and Future Directionmentioning

confidence: 99%

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Gudigar

Nayak

Samanth

et al. 2021

IJERPH

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Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.

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A three-dimensional quantification of calcified and non-calcified plaques in coronary arteries based on computed tomography coronary angiography images: Comparison with expert's annotations and virtual histology intravascular ultrasound

Cited by 23 publications

References 36 publications

Reporting Standards for a Bland–Altman Agreement Analysis: A Review of Methodological Reviews

Reporting Standards for a Bland–Altman Agreement Analysis: A Review of Methodological Reviews

Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

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