Literature DB >> 31317178

Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.

Lien-Hsin Hu1,2, Julian Betancur1, Tali Sharir3,4, Andrew J Einstein5,6, Sabahat Bokhari5,6, Mathews B Fish7, Terrence D Ruddy8, Philipp A Kaufmann9, Albert J Sinusas10, Edward J Miller10, Timothy M Bateman11, Sharmila Dorbala12, Marcelo Di Carli12, Guido Germano1, Frederic Commandeur1, Joanna X Liang1, Yuka Otaki1, Balaji K Tamarappoo1, Damini Dey1, Daniel S Berman1, Piotr J Slomka1.   

Abstract

AIMS: To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting. METHODS AND
RESULTS: A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT scanners were included. All patients had invasive coronary angiography within 6 months after SPECT MPI. ML utilized 18 clinical, 9 stress test, and 28 imaging variables to predict per-vessel and per-patient ECR with 10-fold cross-validation. Area under the receiver operator characteristics curve (AUC) of ML was compared with standard quantitative analysis [total perfusion deficit (TPD)] and expert interpretation. ECR was performed in 958 patients (48%). Per-vessel, the AUC of ECR prediction by ML (AUC 0.79, 95% confidence interval (CI) [0.77, 0.80]) was higher than by regional stress TPD (0.71, [0.70, 0.73]), combined-view stress TPD (AUC 0.71, 95% CI [0.69, 0.72]), or ischaemic TPD (AUC 0.72, 95% CI [0.71, 0.74]), all P < 0.001. Per-patient, the AUC of ECR prediction by ML (AUC 0.81, 95% CI [0.79, 0.83]) was higher than that of stress TPD, combined-view TPD, and ischaemic TPD, all P < 0.001. ML also outperformed nuclear cardiologists' expert interpretation of MPI for the prediction of early revascularization performance. A method to explain ML prediction for an individual patient was also developed.
CONCLUSION: In patients with suspected CAD, the prediction of ECR by ML outperformed automatic MPI quantitation by TPDs (per-vessel and per-patient) or nuclear cardiologists' expert interpretation (per-patient). Published on behalf of the European Society of Cardiology. All rights reserved.
© The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  SPECT myocardial perfusion imaging; coronary artery disease; early coronary revascularization; explainable machine learning; new-generation cardiac camera

Mesh:

Year:  2020        PMID: 31317178      PMCID: PMC7167744          DOI: 10.1093/ehjci/jez177

Source DB:  PubMed          Journal:  Eur Heart J Cardiovasc Imaging        ISSN: 2047-2404            Impact factor:   6.875


  26 in total

1.  Clinical value of supine and upright myocardial perfusion imaging in obese patients using the D-SPECT camera.

Authors:  Simona Ben-Haim; Omar Almukhailed; Johanne Neill; Piotr Slomka; Rayjanah Allie; Dalia Shiti; Daniel S Berman; Jamshed Bomanji
Journal:  J Nucl Cardiol       Date:  2014-01-30       Impact factor: 5.952

2.  ASNC imaging guidelines for SPECT nuclear cardiology procedures: Stress, protocols, and tracers.

Authors:  Milena J Henzlova; W Lane Duvall; Andrew J Einstein; Mark I Travin; Hein J Verberne
Journal:  J Nucl Cardiol       Date:  2016-06       Impact factor: 5.952

3.  Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT).

Authors:  Piotr J Slomka; Julian Betancur; Joanna X Liang; Yuka Otaki; Lien-Hsin Hu; Tali Sharir; Sharmila Dorbala; Marcelo Di Carli; Mathews B Fish; Terrence D Ruddy; Timothy M Bateman; Andrew J Einstein; Philipp A Kaufmann; Edward J Miller; Albert J Sinusas; Peyman N Azadani; Heidi Gransar; Balaji K Tamarappoo; Damini Dey; Daniel S Berman; Guido Germano
Journal:  J Nucl Cardiol       Date:  2018-06-19       Impact factor: 5.952

4.  Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.

Authors:  Julian Betancur; Frederic Commandeur; Mahsaw Motlagh; Tali Sharir; Andrew J Einstein; Sabahat Bokhari; Mathews B Fish; Terrence D Ruddy; Philipp Kaufmann; Albert J Sinusas; Edward J Miller; Timothy M Bateman; Sharmila Dorbala; Marcelo Di Carli; Guido Germano; Yuka Otaki; Balaji K Tamarappoo; Damini Dey; Daniel S Berman; Piotr J Slomka
Journal:  JACC Cardiovasc Imaging       Date:  2018-03-14

5.  Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.

Authors:  Reza Arsanjani; Damini Dey; Tigran Khachatryan; Aryeh Shalev; Sean W Hayes; Mathews Fish; Rine Nakanishi; Guido Germano; Daniel S Berman; Piotr Slomka
Journal:  J Nucl Cardiol       Date:  2014-12-06       Impact factor: 5.952

6.  Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population.

Authors:  Reza Arsanjani; Yuan Xu; Damini Dey; Vishal Vahistha; Aryeh Shalev; Rine Nakanishi; Sean Hayes; Mathews Fish; Daniel Berman; Guido Germano; Piotr J Slomka
Journal:  J Nucl Cardiol       Date:  2013-05-24       Impact factor: 5.952

7.  Comparison of the short-term survival benefit associated with revascularization compared with medical therapy in patients with no prior coronary artery disease undergoing stress myocardial perfusion single photon emission computed tomography.

Authors:  Rory Hachamovitch; Sean W Hayes; John D Friedman; Ishac Cohen; Daniel S Berman
Journal:  Circulation       Date:  2003-05-27       Impact factor: 29.690

8.  Cardiac hybrid SPECT/CTA imaging to detect "functionally relevant coronary artery lesion": a potential gatekeeper for coronary revascularization?

Authors:  Wei Dong; Qian Wang; Shanshan Gu; Hang Su; Jian Jiao; Ying Fu
Journal:  Ann Nucl Med       Date:  2013-12-17       Impact factor: 2.668

9.  Relationship between myocardial perfusion-gated SPECT and the performance of coronary revascularization in patients with ischemic cardiomyopathy.

Authors:  Guillermo Romero-Farina; Jaume Candell-Riera; Santiago Aguadé-Bruix; Ignacio Ferreira-Gonzalez; Albert Igual; David García-Dorado
Journal:  Clin Nucl Med       Date:  2012-10       Impact factor: 7.794

10.  Attenuation artifact, attenuation correction, and the future of myocardial perfusion SPECT.

Authors:  Bhupinder Singh; Timothy M Bateman; James A Case; Gary Heller
Journal:  J Nucl Cardiol       Date:  2007-04       Impact factor: 3.872

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  19 in total

Review 1.  Artificial Intelligence and Machine Learning in Cardiovascular Imaging.

Authors:  Karthik Seetharam; James K Min
Journal:  Methodist Debakey Cardiovasc J       Date:  2020 Oct-Dec

2.  Leveraging latest computer science tools to advance nuclear cardiology.

Authors:  Piotr Slomka
Journal:  J Nucl Cardiol       Date:  2019-09-05       Impact factor: 5.952

Review 3.  Artificial Intelligence in Cardiovascular Imaging for Risk Stratification in Coronary Artery Disease.

Authors:  Andrew Lin; Márton Kolossváry; Manish Motwani; Ivana Išgum; Pál Maurovich-Horvat; Piotr J Slomka; Damini Dey
Journal:  Radiol Cardiothorac Imaging       Date:  2021-02-25

4.  Exploration of the efficacy of radiomics applied to left ventricular tomograms obtained from D-SPECT MPI for the auxiliary diagnosis of myocardial ischemia in CAD.

Authors:  Junpeng Wang; Xin Fan; ShanShan Qin; Kuangyu Shi; Han Zhang; Fei Yu
Journal:  Int J Cardiovasc Imaging       Date:  2021-09-30       Impact factor: 2.357

5.  Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry.

Authors:  Richard Rios; Robert J H Miller; Nipun Manral; Tali Sharir; Andrew J Einstein; Mathews B Fish; Terrence D Ruddy; Philipp A Kaufmann; Albert J Sinusas; Edward J Miller; Timothy M Bateman; Sharmila Dorbala; Marcelo Di Carli; Serge D Van Kriekinge; Paul B Kavanagh; Tejas Parekh; Joanna X Liang; Damini Dey; Daniel S Berman; Piotr J Slomka
Journal:  Comput Biol Med       Date:  2022-03-25       Impact factor: 6.698

Review 6.  Quantitative clinical nuclear cardiology, part 2: Evolving/emerging applications.

Authors:  Piotr J Slomka; Jonathan B Moody; Robert J H Miller; Jennifer M Renaud; Edward P Ficaro; Ernest V Garcia
Journal:  J Nucl Cardiol       Date:  2020-10-16       Impact factor: 5.952

7.  Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images.

Authors:  A Chiba; T Kudo; R Ideguchi; M Altay; S Koga; T Yonekura; A Tsuneto; M Morikawa; S Ikeda; H Kawano; Y Koide; M Uetani; K Maemura
Journal:  Int J Cardiovasc Imaging       Date:  2021-03-11       Impact factor: 2.357

8.  Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT.

Authors:  Riemer H J A Slart; Michelle C Williams; Luis Eduardo Juarez-Orozco; Christoph Rischpler; Marc R Dweck; Andor W J M Glaudemans; Alessia Gimelli; Panagiotis Georgoulias; Olivier Gheysens; Oliver Gaemperli; Gilbert Habib; Roland Hustinx; Bernard Cosyns; Hein J Verberne; Fabien Hyafil; Paola A Erba; Mark Lubberink; Piotr Slomka; Ivana Išgum; Dimitris Visvikis; Márton Kolossváry; Antti Saraste
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-04-17       Impact factor: 9.236

9.  Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry.

Authors:  Richard Rios; Robert J H Miller; Lien Hsin Hu; Yuka Otaki; Ananya Singh; Marcio Diniz; Tali Sharir; Andrew J Einstein; Mathews B Fish; Terrence D Ruddy; Philipp A Kaufmann; Albert J Sinusas; Edward J Miller; Timothy M Bateman; Sharmila Dorbala; Marcelo DiCarli; Serge Van Kriekinge; Paul Kavanagh; Tejas Parekh; Joanna X Liang; Damini Dey; Daniel S Berman; Piotr Slomka
Journal:  Cardiovasc Res       Date:  2022-07-20       Impact factor: 13.081

10.  Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease.

Authors:  Yuka Otaki; Ananya Singh; Paul Kavanagh; Robert J H Miller; Tejas Parekh; Balaji K Tamarappoo; Tali Sharir; Andrew J Einstein; Mathews B Fish; Terrence D Ruddy; Philipp A Kaufmann; Albert J Sinusas; Edward J Miller; Timothy M Bateman; Sharmila Dorbala; Marcelo Di Carli; Sebastien Cadet; Joanna X Liang; Damini Dey; Daniel S Berman; Piotr J Slomka
Journal:  JACC Cardiovasc Imaging       Date:  2021-07-14
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