Evidence map›Paper›PMID 23417447›Full record

ArticleThe international journal of cardiovascular imaging2013

Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology.

Michiel A de Graaf, Alexander Broersen, Pieter H Kitslaar, Cornelis J Roos, Jouke Dijkstra, Boudewijn P F Lelieveldt, J Wouter Jukema, Martin J Schalij, Victoria Delgado, Jeroen J Bax and 2 more

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In one paragraph

Article in The international journal of cardiovascular imaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.

0numbers the graph read from it
0cells of the map it votes in
94citing papers in PubMed
12.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

94 citing papers in PubMed, 232 citations in OpenAlex.

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34 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Michiel A de GraafDepartment of Cardiology, Leiden University Medical Center, Albinusdreef 2, Postal zone 2300 RC, 2333 ZA Leiden, The Netherlands. M.A.de_Graaf@lumc.nl
Alexander Broersen
Pieter H Kitslaar
Cornelis J Roos
Jouke Dijkstra
Boudewijn P F Lelieveldt
J Wouter Jukema
Martin J Schalij
Victoria Delgado
Jeroen J Bax
Johan H C Reiber
Arthur J Scholte
Leiden University Medical Center · NLCentre for Medical Systems Biology · NLUtrecht University · NLNetherlands Heart Institute · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plaque constitution on computed tomography coronary angiography (CTA) is associated with prognosis. At present only visual assessment of plaque constitution is possible. An accurate automatic, quantitative approach for CTA plaque constitution assessment would improve reproducibility and allows higher accuracy. The present study assessed the feasibility of a fully automatic and quantitative analysis of atherosclerosis on CTA. Clinically derived CTA and intravascular ultrasound virtual histology (IVUS VH) datasets were used to investigate the correlation between quantitatively automatically derived CTA parameters and IVUS VH. A total of 57 patients underwent CTA prior to IVUS VH. First, quantitative CTA quantitative computed tomography (QCT) was performed. Per lesion stenosis parameters and plaque volumes were assessed. Using predefined HU thresholds, CTA plaque volume was differentiated in 4 different plaque types necrotic core (NC), dense calcium (DC), fibrotic (FI) and fibro-fatty tissue (FF). At the identical level of the coronary, the same parameters were derived from IVUS VH. Bland-Altman analyses were performed to assess the agreement between QCT and IVUS VH. Assessment of plaque volume using QCT in 108 lesions showed excellent correlation with IVUS VH (r = 0.928, p < 0.001) (Fig. 1). The correlation of both FF and FI volume on IVUS VH and QCT was good (r = 0.714, p < 0.001 and r = 0.695, p < 0.001 respectively) with corresponding bias and 95 % limits of agreement of 24 mm(3) (-42; 90) and 7.7 mm(3) (-54; 70). Furthermore, NC and DC were well-correlated in both modalities (r = 0.523, p < 0.001) and (r = 0.736, p < 0.001). Automatic, quantitative CTA tissue characterization is feasible using a dedicated software tool. Fig. 1 Schematic illustration of the characterization of coronary plaque on CTA: cross-correlation with IVUS VH. First, the 3-dimensional centerline was generated from the CTA data set using an automatic tree extraction algorithm (Panel I). Using a unique registration a complete pullback series of IVUS images was mapped on the CTA volume using true anatomical markers (Panel II). Fully automatic lumen and vessel wall contour detection was performed for both imaging modalities (Panel III). Finally, fusion-based quantification of atherosclerotic lesions was based on the lumen and vessel wall contours as well as the corresponding reference lines (estimate of normal tapering of the coronary artery), as shown in panel IV. At the level of the minimal lumen area (MLA) (yellow lines), stenosis parameters, could be calculated for both imaging techniques. Additionally, plaque volumes and plaque types were derived for the whole coronary artery lesion, ranging from the proximal to distal lesion marker (blue markers). Fibrotic tissue was labeled in dark green, Fibro-fatty tissue in light green, dense calcium in white and necrotic core was labeled in red.

Indexed as

Multidetector Computed TomographyUltrasonography, InterventionalAgedAlgorithmsAutomation, LaboratoryCoronary AngiographyCoronary Artery DiseaseCoronary StenosisCoronary VesselsFeasibility StudiesFemaleFibrosisHumansMaleMiddle AgedNecrosis

Identifiers

PMID23417447
OpenAlexW1996344483

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.