Evidence map›Paper›PMID 35337643›Full record

ArticleThe Lancet. Digital health2022

Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study.

Andrew Lin, Nipun Manral, Priscilla McElhinney, Aditya Killekar, Hidenari Matsumoto, Jacek Kwiecinski, Konrad Pieszko, Aryabod Razipour, Kajetan Grodecki, Caroline Park and 22 more

Erratum issuedAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 187 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
187citing papers in PubMed, 5 pooled it
–field-weighted citation impact
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

187 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

32 authors.

Andrew LinBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University, Melbourne, VIC, Australia; MonashHeart, Monash Health, Melbourne, VIC, Australia.
Nipun ManralBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Priscilla McElhinneyBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Aditya KillekarDivision of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Hidenari MatsumotoDivision of Cardiology, Showa University School of Medicine, Tokyo, Japan.
Jacek KwiecinskiDivision of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA; British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK; Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland.
Konrad PieszkoDivision of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Interventional Cardiology, Collegium Medicum, University of Zielona Góra, Poland.
Aryabod RazipourBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Kajetan GrodeckiBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Cardiology, Medical University of Warsaw, Warsaw, Poland.
Caroline ParkBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Yuka OtakiDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Mhairi DorisBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Alan C KwanDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Donghee HanDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Keiichiro KuronumaDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Guadalupe Flores TomasinoDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Evangelos TzolosDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Aakash ShanbhagDivision of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Markus GoellerDepartment of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.
Mohamed MarwanDepartment of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.
Heidi GransarDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Balaji K TamarappooDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Sebastien CadetDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Stephan AchenbachDepartment of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.
Stephen J NichollsMonash Cardiovascular Research Centre, Victorian Heart Institute, Monash University, Melbourne, VIC, Australia; MonashHeart, Monash Health, Melbourne, VIC, Australia.
Dennis T WongMonash Cardiovascular Research Centre, Victorian Heart Institute, Monash University, Melbourne, VIC, Australia; MonashHeart, Monash Health, Melbourne, VIC, Australia.
Daniel S BermanDepartment of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Marc DweckBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
David E NewbyBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Michelle C WilliamsBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.
Piotr J SlomkaDivision of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Damini DeyBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: damini.dey@cshs.org.

Funding

Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT AngiographyR01HL148787 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2020 to 2023
$3.0M
British Heart Foundation CH/09/002/26360British Heart Foundation FS/14/78/31020British Heart Foundation FS/ICRF/20/26002NHLBI NIH HHS R01 HL148787
6 · The paper itself

Abstract

backgroundAtherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity.

methodsThis international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score.

findingsIn the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0-5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm

interpretationOur novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction.

fundingNational Heart, Lung, and Blood Institute and the Miriam & Sheldon G Adelson Medical Research Foundation.

Indexed as

Deep LearningPlaque, AtheroscleroticComputed Tomography AngiographyConstriction, PathologicHumansProspective StudiesRetrospective Studies

Identifiers

PMID35337643
PMCPMC9047317

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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.