Learning Curve for Coronary CT Angiography: What Constitutes Sufficient Training?

Pugliese, Francesca; Hunink, M. G. Myriam; Gruszczyńska, Katarzyna; Alberghina, Filippo; Malagò, Roberto; Pelt, Niels van; Mollet, Nico R.; Cademartiri, Filippo; Weustink, Annick C.; Meijboom, Willem B.; Witteman, Cilia; Feyter, Pim J. de; Krestin, Gabriël P.

doi:10.1148/radiol.2512080384

Cited by 81 publications

(49 citation statements)

References 27 publications

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“…[2][3][4][5][6] Beyond stenosis, CTA also permits noninvasive assessment of atherosclerotic plaque and coronary artery remodeling. [7][8][9] Current clinical assessment of CTA and lesion detection is based on visual analysis, which is time consuming and subject to observer variability, 10 although computer-aided extraction of the coronary arteries is often employed. 11 It was reported that acquiring expertise in CTA interpretation may take more than a year.…”

Section: Introductionmentioning

confidence: 99%

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

et al. 2015

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Abstract. Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

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Section: Introductionmentioning

confidence: 99%

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

et al. 2015

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“…Their ability to evaluate significant coronary stenoses can improve after one year of training with approximately 600 cases a year. An increase of sensitivity is seen after one year of training, ranging from 66%-75% (31). Published research about diagnostic performance was almost always carried out by physicians with 3 or more years of experience, resulting in a sensitivity of 90%.…”

Section: Discussionmentioning

confidence: 99%

Cost effectiveness of multi detector ct angiography of the coronary arteries for the diagnosis of suspected non-st eleva tion acute coronary syndrome (nste-acs) in the emergency department. mathematical analysis with a decision model

Beule¹,

Vanhoenacker²,

Booij³

et al. 2010

Journal of the Belgian Society of Radiology

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The purpose of our study is to model cost-effectiveness of MDCTA for the diagnosis of NSTE-ACS with initially negative enzymes, in the emergency department. In Belgium, the use of multi-detector computed tomography (MDCTA) is probably cost-effective in the diagnosis of NSTE-ACS in the acute setting A decision tree model was developed and a mathematical study was performed that included two hypothetical strategies: MDCTA and admission with classic clinical follow-up and treatment. Costeffectiveness for the Belgian situation was simulated with sensitivity analysis using known values for diagnostic performance and known costs for the different strategies or components of strategies.

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“…These results prove CCTA a viable alternative with very high negative predictive value. However, reading CCTA images requires substantial experience and only well-trained physicians are able to interpret CCTA reliably [12]. An automated system that can rule out clinically-relevant stenoses (grade> 50%) in the coronary arteries could be used as a second reader in the absence of an expert physician in the emergency department.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, coronary arteries follow long and treacherous paths, extending over only a few voxels in diameter in the distal parts. Even experts sometimes struggle to give a correct diagnosis due to these challenges [12].…”

Section: Introductionmentioning

confidence: 99%

Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography

Kelm

Mittal

Zheng

et al. 2011

Lecture Notes in Computer Science

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Abstract.Recently conducted clinical studies prove the utility of Coronary Computed Tomography Angiography (CCTA) as a viable alternative to invasive angiography for the detection of Coronary Artery Disease (CAD). This has lead to the development of several algorithms for automatic detection and grading of coronary stenoses. However, most of these methods focus on detecting calcified plaques only. A few methods that can also detect and grade non-calcified plaques require substantial user involvement. In this paper, we propose a fast and fully automatic system that is capable of detecting, grading and classifying coronary stenoses in CCTA caused by all types of plaques. We propose a four-step approach including a learning-based centerline verification step and a lumen crosssection estimation step using random regression forests. We show state-of-the-art performance of our method in experiments conducted on a set of 229 CCTA volumes. With an average processing time of 1.8 seconds per case after centerline extraction, our method is significantly faster than competing approaches.

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Learning Curve for Coronary CT Angiography: What Constitutes Sufficient Training?

Abstract: Increasing experience with coronary CT angiography improved the diagnostic performance of inexperienced physicians. However, acquiring expertise in coronary CT angiography was slow and may take more than 1 year.

Cited by 81 publications

References 27 publications

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

Cost effectiveness of multi detector ct angiography of the coronary arteries for the diagnosis of suspected non-st eleva tion acute coronary syndrome (nste-acs) in the emergency department. mathematical analysis with a decision model

Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography

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