Literature DB >> 26467104

Relationship Between Quantitative Adverse Plaque Features From Coronary Computed Tomography Angiography and Downstream Impaired Myocardial Flow Reserve by 13N-Ammonia Positron Emission Tomography: A Pilot Study.

Damini Dey1, Mariana Diaz Zamudio2, Annika Schuhbaeck2, Luis Eduardo Juarez Orozco2, Yuka Otaki2, Heidi Gransar2, Debiao Li2, Guido Germano2, Stephan Achenbach2, Daniel S Berman2, Aloha Meave2, Erick Alexanderson2, Piotr J Slomka2.   

Abstract

BACKGROUND: We investigated the relationship of quantitative plaque features from coronary computed tomography (CT) angiography and coronary vascular dysfunction by impaired myocardial flow reserve (MFR) by (13)N-Ammonia positron emission tomography (PET). METHODS AND
RESULTS: Fifty-one patients (32 men, 62.4±9.5 years) underwent combined rest-stress (13)N-ammonia PET and CT angiography scans by hybrid PET/CT. Regional MFR was measured from PET. From CT angiography, 153 arteries were evaluated by semiautomated software, computing arterial noncalcified plaque (NCP), low-density NCP (NCP<30 HU), calcified and total plaque volumes, and corresponding plaque burden (plaque volumex100%/vessel volume), stenosis, remodeling index, contrast density difference (maximum difference in luminal attenuation per unit area in the lesion), and plaque length. Quantitative stenosis, plaque burden, and myocardial mass were combined by boosted ensemble machine-learning algorithm into a composite risk score to predict impaired MFR (MFR≤2.0) by PET in each artery. Nineteen patients had impaired regional MFR in at least 1 territory (41/153 vessels). Patients with impaired regional MFR had higher arterial NCP (32.4% versus 17.2%), low-density NCP (7% versus 4%), and total plaque burden (37% versus 19.3%, P<0.02). In multivariable analysis with 10-fold cross-validation, NCP burden was the most significant predictor of impaired MFR (odds ratio, 1.35; P=0.021 for all). For prediction of impaired MFR with 10-fold cross-validation, receiver operating characteristics area under the curve for the composite score was 0.83 (95% confidence interval, 0.79-0.91) greater than for quantitative stenosis (0.66, 95% confidence interval, 0.57-0.76, P=0.005).
CONCLUSIONS: Compared with stenosis, arterial NCP burden and a composite score combining quantitative stenosis and plaque burden from CT angiography significantly improves identification of downstream regional vascular dysfunction.
© 2015 American Heart Association, Inc.

Entities:  

Keywords:  angiography; coronary stenosis; fractional flow reserve, myocardial; plaque, atherosclerotic; positron-emission tomography; tomography, X-ray computed

Mesh:

Substances:

Year:  2015        PMID: 26467104      PMCID: PMC4939903          DOI: 10.1161/CIRCIMAGING.115.003255

Source DB:  PubMed          Journal:  Circ Cardiovasc Imaging        ISSN: 1941-9651            Impact factor:   7.792


  34 in total

1.  Prediction error estimation: a comparison of resampling methods.

Authors:  Annette M Molinaro; Richard Simon; Ruth M Pfeiffer
Journal:  Bioinformatics       Date:  2005-05-19       Impact factor: 6.937

2.  Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography.

Authors:  Damini Dey; Victor Y Cheng; Piotr J Slomka; Ryo Nakazato; Amit Ramesh; Swaminatha Gurudevan; Guido Germano; Daniel S Berman
Journal:  J Cardiovasc Comput Tomogr       Date:  2009-10-01

3.  Comparison of clinical tools for measurements of regional stress and rest myocardial blood flow assessed with 13N-ammonia PET/CT.

Authors:  Piotr J Slomka; Erick Alexanderson; Rodrigo Jácome; Moises Jiménez; Edgar Romero; Aloha Meave; Ludovic Le Meunier; Magnus Dalhbom; Daniel S Berman; Guido Germano; Heinrich Schelbert
Journal:  J Nucl Med       Date:  2012-01-06       Impact factor: 10.057

4.  Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US.

Authors:  Damini Dey; Tiziano Schepis; Mohamed Marwan; Piotr J Slomka; Daniel S Berman; Stephan Achenbach
Journal:  Radiology       Date:  2010-09-09       Impact factor: 11.105

5.  Assessment of coronary artery stenosis severity and location: quantitative analysis of transmural perfusion gradients by high-resolution MRI versus FFR.

Authors:  Amedeo Chiribiri; Gilion L T F Hautvast; Timothy Lockie; Andreas Schuster; Boris Bigalke; Luca Olivotti; Simon R Redwood; Marcel Breeuwer; Sven Plein; Eike Nagel
Journal:  JACC Cardiovasc Imaging       Date:  2013-04-10

6.  Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm.

Authors:  Reza Arsanjani; Yuan Xu; Damini Dey; Matthews Fish; Sharmila Dorbala; Sean Hayes; Daniel Berman; Guido Germano; Piotr Slomka
Journal:  J Nucl Med       Date:  2013-03-12       Impact factor: 10.057

7.  Randomized comparison of 64-slice single- and dual-source computed tomography coronary angiography for the detection of coronary artery disease.

Authors:  Stephan Achenbach; Ulrike Ropers; Axel Kuettner; Katharina Anders; Tobias Pflederer; Sei Komatsu; Werner Bautz; Werner G Daniel; Dieter Ropers
Journal:  JACC Cardiovasc Imaging       Date:  2008-03

8.  Long-term prognostic value of 13N-ammonia myocardial perfusion positron emission tomography added value of coronary flow reserve.

Authors:  Bernhard A Herzog; Lars Husmann; Ines Valenta; Oliver Gaemperli; Patrick T Siegrist; Fabian M Tay; Nina Burkhard; Christophe A Wyss; Philipp A Kaufmann
Journal:  J Am Coll Cardiol       Date:  2009-07-07       Impact factor: 24.094

9.  A simplified method for quantification of myocardial blood flow using nitrogen-13-ammonia and dynamic PET.

Authors:  Y Choi; S C Huang; R A Hawkins; W G Kuhle; M Dahlbom; C K Hoh; J Czernin; M E Phelps; H R Schelbert
Journal:  J Nucl Med       Date:  1993-03       Impact factor: 10.057

10.  Accuracy of multidetector spiral computed tomography in identifying and differentiating the composition of coronary atherosclerotic plaques: a comparative study with intracoronary ultrasound.

Authors:  Alexander W Leber; Andreas Knez; Alexander Becker; Christoph Becker; Franz von Ziegler; Konstantin Nikolaou; Carsten Rist; Maximilian Reiser; Carl White; Gerhard Steinbeck; Peter Boekstegers
Journal:  J Am Coll Cardiol       Date:  2004-04-07       Impact factor: 24.094

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  23 in total

Review 1.  Reasons and implications of agreements and disagreements between coronary flow reserve, fractional flow reserve, and myocardial perfusion imaging.

Authors:  Manish Motwani; Mahsaw Motlagh; Anuj Gupta; Daniel S Berman; Piotr J Slomka
Journal:  J Nucl Cardiol       Date:  2015-12-29       Impact factor: 5.952

2.  Machine learning in the integration of simple variables for identifying patients with myocardial ischemia.

Authors:  Luis Eduardo Juarez-Orozco; Remco J J Knol; Carlos A Sanchez-Catasus; Octavio Martinez-Manzanera; Friso M van der Zant; Juhani Knuuti
Journal:  J Nucl Cardiol       Date:  2018-05-22       Impact factor: 5.952

Review 3.  Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review.

Authors:  Damini Dey; Piotr J Slomka; Paul Leeson; Dorin Comaniciu; Sirish Shrestha; Partho P Sengupta; Thomas H Marwick
Journal:  J Am Coll Cardiol       Date:  2019-03-26       Impact factor: 24.094

Review 4.  Updates on Stress Imaging Testing and Myocardial Viability With Advanced Imaging Modalities.

Authors:  Sandeep S Hedgire; Michael Osborne; Daniel J Verdini; Brian B Ghoshhajra
Journal:  Curr Treat Options Cardiovasc Med       Date:  2017-04

Review 5.  Cardiac imaging: working towards fully-automated machine analysis & interpretation.

Authors:  Piotr J Slomka; Damini Dey; Arkadiusz Sitek; Manish Motwani; Daniel S Berman; Guido Germano
Journal:  Expert Rev Med Devices       Date:  2017-03       Impact factor: 3.166

6.  Clinical Quantification of Myocardial Blood Flow Using PET: Joint Position Paper of the SNMMI Cardiovascular Council and the ASNC.

Authors:  Venkatesh L Murthy; Timothy M Bateman; Rob S Beanlands; Daniel S Berman; Salvador Borges-Neto; Panithaya Chareonthaitawee; Manuel D Cerqueira; Robert A deKemp; E Gordon DePuey; Vasken Dilsizian; Sharmila Dorbala; Edward P Ficaro; Ernest V Garcia; Henry Gewirtz; Gary V Heller; Howard C Lewin; Saurabh Malhotra; April Mann; Terrence D Ruddy; Thomas H Schindler; Ronald G Schwartz; Piotr J Slomka; Prem Soman; Marcelo F Di Carli; Andrew Einstein; Raymond Russell; James R Corbett
Journal:  J Nucl Cardiol       Date:  2018-02       Impact factor: 5.952

7.  Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.

Authors:  Evangelos K Oikonomou; Musib Siddique; Charalambos Antoniades
Journal:  Cardiovasc Res       Date:  2020-11-01       Impact factor: 10.787

8.  Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography.

Authors:  Markus Goeller; Balaji K Tamarappoo; Alan C Kwan; Sebastien Cadet; Frederic Commandeur; Aryabod Razipour; Piotr J Slomka; Heidi Gransar; Xi Chen; Yuka Otaki; John D Friedman; J Jane Cao; Moritz H Albrecht; Daniel O Bittner; Mohamed Marwan; Stephan Achenbach; Daniel S Berman; Damini Dey
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2019-06-01       Impact factor: 6.875

9.  Quantitative global plaque characteristics from coronary computed tomography angiography for the prediction of future cardiac mortality during long-term follow-up.

Authors:  Michaela M Hell; Manish Motwani; Yuka Otaki; Sebastien Cadet; Heidi Gransar; Romalisa Miranda-Peats; Jacob Valk; Piotr J Slomka; Victor Y Cheng; Alan Rozanski; Balaji K Tamarappoo; Sean Hayes; Stephan Achenbach; Daniel S Berman; Damini Dey
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2017-12-01       Impact factor: 6.875

10.  The tools are ready, are we?

Authors:  Sang-Geon Cho; Zeenat Jabin; Changho Lee; Henry Hee-Seung Bom
Journal:  J Nucl Cardiol       Date:  2017-08-21       Impact factor: 5.952

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