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Michalowska AM, Zhang W, Shanbhag A, Miller RJ, Lemley M, Ramirez G, Buchwald M, Killekar A, Kavanagh PB, Feher A, Miller EJ, Einstein AJ, Ruddy TD, Liang JX, Builoff V, Ouyang D, Berman DS, Dey D, Slomka PJ. Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction. medRxiv 2024:2024.04.23.24305735. [PMID: 38712025 PMCID: PMC11071553 DOI: 10.1101/2024.04.23.24305735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment. Methods Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all). Conclusion In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.
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Affiliation(s)
- Anna M Michalowska
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Wenhao Zhang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Robert Jh Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Giselle Ramirez
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mikolaj Buchwald
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul B Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, United States
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Valerie Builoff
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
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2
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Miller RJH, Killekar A, Shanbhag A, Bednarski B, Michalowska AM, Ruddy TD, Einstein AJ, Newby DE, Lemley M, Pieszko K, Van Kriekinge SD, Kavanagh PB, Liang JX, Huang C, Dey D, Berman DS, Slomka PJ. Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography. Nat Commun 2024; 15:2747. [PMID: 38553462 PMCID: PMC10980695 DOI: 10.1038/s41467-024-46977-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Bryan Bednarski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Anna M Michalowska
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, NY, USA
- Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, New York, NY, USA
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Gora, Gora, Poland
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul B Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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3
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Miller RJH, Shanbhag A, Killekar A, Lemley M, Bednarski B, Kavanagh PB, Feher A, Miller EJ, Bateman T, Builoff V, Liang JX, Newby DE, Dey D, Berman DS, Slomka PJ. AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging. JACC Cardiovasc Imaging 2024:S1936-878X(24)00038-X. [PMID: 38456877 DOI: 10.1016/j.jcmg.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/18/2023] [Accepted: 01/04/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. OBJECTIVES The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility. METHODS The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization. RESULTS In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%. CONCLUSIONS AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.
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Affiliation(s)
- Robert J H Miller
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiac Sciences, University of Calgary, Calgary Alberta, Canada
| | - Aakash Shanbhag
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Aditya Killekar
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Mark Lemley
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Bryan Bednarski
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Paul B Kavanagh
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Timothy Bateman
- Cardiovascular Imaging Technologies LLC, Kansas City, Missouri, USA
| | - Valerie Builoff
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Joanna X Liang
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Damini Dey
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Daniel S Berman
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Piotr J Slomka
- Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
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4
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Williams MC, Shanbhag AD, Zhou J, Michalowska AM, Lemley M, Miller RJ, Killekar A, Waechter P, Gransar H, Van Kriekinge SD, Builoff V, Feher A, Miller EJ, Bateman T, Dey D, Berman D, Slomka PJ. Automated vessel specific coronary artery calcification quantification with deep learning in a large multi-center registry. Eur Heart J Cardiovasc Imaging 2024:jeae045. [PMID: 38376471 DOI: 10.1093/ehjci/jeae045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/22/2023] [Accepted: 01/30/2024] [Indexed: 02/21/2024] Open
Abstract
AIMS Vessel specific coronary artery calcification (CAC) is additive to global CAC for prognostic assessment. We assessed accuracy and prognostic implications of vessel-specific automated deep learning (DL) CAC analysis on electrocardiogram gated and attenuation correction computed tomography (CT) in a large multicenter registry. METHODS AND RESULTS Vessel-specific CAC was assessed in the left main/left anterior descending (LM/LAD), left circumflex (LCX) and right coronary artery (RCA) using a DL model trained on 3000 gated CT and tested on 2094 gated CT and 5969 non-gated attenuation correction CT. Vessel-specific agreement was assessed with linear weighted Cohen's Kappa for CAC zero, 1-100, 101-400 and >400 Agatston units (AU). Risk of major adverse cardiovascular events (MACE) was assessed during 2.4±1.4 years follow-up, with hazard ratios (HR) and 95% confidence intervals (CI). There was strong to excellent agreement between DL and expert ground truth for CAC in LM/LAD, LCX and RCA on gated CT [0.90 (95% CI 0.89 to 0.92); 0.70 (0.68 to 0.73); 0.79 (0.77 to 0.81)] and attenuation correction CT [(0.78 (0.77 to 0.80); 0.60 (0.58 to 0.62); 0.70 (0.68 to 0.71)]. MACE occurred in 242 (12%) undergoing gated CT and 841(14%) of undergoing attenuation correction CT. LM/LAD CAC >400 AU was associated with the highest risk of MACE on gated (HR 12.0, 95% CI 7.96, 18.0, p<0.001) and attenuation correction CT (HR 4.21, 95% CI 3.48, 5.08, p<0.001). CONCLUSION Vessel-specific CAC assessment with DL can be performed accurately and rapidly on gated CT and attenuation correction CT and provides important prognostic information.
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Affiliation(s)
- Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash D Shanbhag
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Jianhang Zhou
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Anna M Michalowska
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Jh Miller
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Parker Waechter
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Valerie Builoff
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Timothy Bateman
- Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel Berman
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
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5
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Miller RJH, Shanbhag A, Killekar A, Lemley M, Bednarski B, Van Kriekinge SD, Kavanagh PB, Feher A, Miller EJ, Einstein AJ, Ruddy TD, Liang JX, Builoff V, Berman DS, Dey D, Slomka PJ. AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging. NPJ Digit Med 2024; 7:24. [PMID: 38310123 PMCID: PMC10838293 DOI: 10.1038/s41746-024-01020-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/18/2024] [Indexed: 02/05/2024] Open
Abstract
Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90-0.98). During median follow-up of 2.7 years (IQR 1.6-4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Bryan Bednarski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul B Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Valerie Builoff
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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6
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Grodecki K, Killekar A, Simon J, Lin A, Cadet S, McElhinney P, Chan C, Williams MC, Pressman BD, Julien P, Li D, Chen P, Gaibazzi N, Thakur U, Mancini E, Agalbato C, Munechika J, Matsumoto H, Menè R, Parati G, Cernigliaro F, Nerlekar N, Torlasco C, Pontone G, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems. Br J Radiol 2023; 96:20220180. [PMID: 37310152 PMCID: PMC10461277 DOI: 10.1259/bjr.20220180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/15/2023] [Accepted: 05/30/2023] [Indexed: 06/14/2023] Open
Abstract
OBJECTIVE We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems. METHODS A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death. RESULTS The final population comprised 743 patients (mean age 65 ± 17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores. CONCLUSION Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time. ADVANCES IN KNOWLEDGE Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.
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Affiliation(s)
| | - Aditya Killekar
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sebastien Cadet
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cato Chan
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Michelle C. Williams
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Barry D. Pressman
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Peter Julien
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Peter Chen
- Department of Medicine, Women’s Guild Lung Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nicola Gaibazzi
- Cardiology, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | | | | | - Cecilia Agalbato
- Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | - Jiro Munechika
- Division of Radiology, Showa University School of Medicine, Tokyo, Japan
| | - Hidenari Matsumoto
- Division of Cardiology, Showa University School of Medicine, Tokyo, Japan
| | | | | | | | | | | | - Gianluca Pontone
- Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | | | - Piotr J. Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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7
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Miller RJH, Pieszko K, Shanbhag A, Feher A, Lemley M, Killekar A, Kavanagh PB, Van Kriekinge SD, Liang JX, Huang C, Miller EJ, Bateman T, Berman DS, Dey D, Slomka PJ. Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events. J Nucl Med 2023; 64:652-658. [PMID: 36207138 PMCID: PMC10071789 DOI: 10.2967/jnumed.122.264423] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 11/16/2022] Open
Abstract
Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (CAC) from these scans in an automatic manner. We evaluated CAC quantification derived with a DL model, including correlation with expert annotations and associations with major adverse cardiovascular events (MACE). Methods: We trained a convolutional long short-term memory DL model to automatically quantify CAC on CTAC scans using 6,608 studies (2 centers) and evaluated the model in an external cohort of patients without known coronary artery disease (n = 2,271) obtained in a separate center. We assessed agreement between DL and expert annotated CAC scores. We also assessed associations between MACE (death, revascularization, myocardial infarction, or unstable angina) and CAC categories (0, 1-100, 101-400, or >400) for scores manually derived by experienced readers and scores obtained fully automatically by DL using multivariable Cox models (adjusted for age, sex, past medical history, perfusion, and ejection fraction) and net reclassification index. Results: In the external testing population, DL CAC was 0 in 908 patients (40.0%), 1-100 in 596 (26.2%), 100-400 in 354 (15.6%), and >400 in 413 (18.2%). Agreement in CAC category by DL CAC and expert annotation was excellent (linear weighted κ, 0.80), but DL CAC was obtained automatically in less than 2 s compared with about 2.5 min for expert CAC. DL CAC category was an independent risk factor for MACE with hazard ratios in comparison to a CAC of zero: CAC of 1-100 (2.20; 95% CI, 1.54-3.14; P < 0.001), CAC of 101-400 (4.58; 95% CI, 3.23-6.48; P < 0.001), and CAC of more than 400 (5.92; 95% CI, 4.27-8.22; P < 0.001). Overall, the net reclassification index was 0.494 for DL CAC, which was similar to expert annotated CAC (0.503). Conclusion: DL CAC from SPECT/CT attenuation maps agrees well with expert CAC annotations and provides a similar risk stratification but can be obtained automatically. DL CAC scores improved classification of a significant proportion of patients as compared with SPECT myocardial perfusion alone.
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Affiliation(s)
- Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Interventional Cardiology and Cardiac Surgery, University of Zielona Góra, Zielona Góra, Poland
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Attila Feher
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Mark Lemley
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Paul B Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Serge D Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut; and
| | - Timothy Bateman
- Cardiovascular Imaging Technologies LLC, Kansas City, Missouri
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California;
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8
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Singh A, Kwiecinski J, Cadet S, Killekar A, Tzolos E, Williams MC, Dweck MR, Newby DE, Dey D, Slomka PJ. Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks. J Nucl Cardiol 2023; 30:604-615. [PMID: 35701650 PMCID: PMC9747983 DOI: 10.1007/s12350-022-03010-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/04/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Coronary 18F-sodium-fluoride (18F-NaF) positron emission tomography (PET) showed promise in imaging coronary artery disease activity. Currently image processing remains subjective due to the need for manual registration of PET and computed tomography (CT) angiography data. We aimed to develop a novel fully automated method to register coronary 18F-NaF PET to CT angiography using pseudo-CT generated by generative adversarial networks (GAN). METHODS A total of 169 patients, 139 in the training and 30 in the testing sets were considered for generation of pseudo-CT from non-attenuation corrected (NAC) PET using GAN. Non-rigid registration was used to register pseudo-CT to CT angiography and the resulting transformation was used to align PET with CT angiography. We compared translations, maximal standard uptake value (SUVmax) and target to background ratio (TBRmax) at the location of plaques, obtained after observer and automated alignment. RESULTS Automatic end-to-end registration was performed for 30 patients with 88 coronary vessels and took 27.5 seconds per patient. Difference in displacement motion vectors between GAN-based and observer-based registration in the x-, y-, and z-directions was 0.8 ± 3.0, 0.7 ± 3.0, and 1.7 ± 3.9 mm, respectively. TBRmax had a coefficient of repeatability (CR) of 0.31, mean bias of 0.03 and narrow limits of agreement (LOA) (95% LOA: - 0.29 to 0.33). SUVmax had CR of 0.26, mean bias of 0 and narrow LOA (95% LOA: - 0.26 to 0.26). CONCLUSION Pseudo-CT generated by GAN are perfectly registered to PET can be used to facilitate quick and fully automated registration of PET and CT angiography.
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Affiliation(s)
- Ananya Singh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Jacek Kwiecinski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Sebastien Cadet
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Evangelos Tzolos
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Michelle C Williams
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Marc R Dweck
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - David E Newby
- BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Suite Metro 203, Los Angeles, CA, 90048, USA.
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9
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Shanbhag AD, Zhou J, miller R, Pieszko K, Lemley M, Killekar A, Van Kriekinge SD, Gransar H, Kavanagh P, miller EJ, Bateman TM, Dey D, Berman DS, Slomka P. DEEP LEARNING ACCURATELY QUANTIFIES PER VESSEL CORONARY CALCIUM SCORES IN A LARGE MULTICENTER INTERNATIONAL REGISTRY. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)01814-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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10
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Killekar A, Grodecki K, Lin A, Cadet S, McElhinney P, Razipour A, Chan C, Pressman BD, Julien P, Chen P, Simon J, Maurovich-Horvat P, Gaibazzi N, Thakur U, Mancini E, Agalbato C, Munechika J, Matsumoto H, Menè R, Parati G, Cernigliaro F, Nerlekar N, Torlasco C, Pontone G, Dey D, Slomka P. Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional long short-term memory networks. J Med Imaging (Bellingham) 2022; 9:054001. [PMID: 36090960 PMCID: PMC9446878 DOI: 10.1117/1.jmi.9.5.054001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/16/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in coronavirus disease 2019 (COVID-19) patients but are not part of clinical routine because the required manual segmentation of lung lesions is prohibitively time consuming. We aim to automatically segment ground-glass opacities and high opacities (comprising consolidation and pleural effusion). Approach: We propose a new fully automated deep-learning framework for fast multi-class segmentation of lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional long short-term memory (ConvLSTM) networks. Utilizing the expert annotations, model training was performed using five-fold cross-validation to segment COVID-19 lesions. The performance of the method was evaluated on CT datasets from 197 patients with a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2, 68 unseen test cases, and 695 independent controls. Results: Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score of 0.89 ± 0.07 ; excellent correlations of 0.93 and 0.98 for ground-glass opacity (GGO) and high opacity volumes, respectively, were obtained. In the external testing set of 68 patients, we observed a Dice score of 0.89 ± 0.06 as well as excellent correlations of 0.99 and 0.98 for GGO and high opacity volumes, respectively. Computations for a CT scan comprising 120 slices were performed under 3 s on a computer equipped with an NVIDIA TITAN RTX GPU. Diagnostically, the automated quantification of the lung burden % discriminate COVID-19 patients from controls with an area under the receiver operating curve of 0.96 (0.95-0.98). Conclusions: Our method allows for the rapid fully automated quantitative measurement of the pneumonia burden from CT, which can be used to rapidly assess the severity of COVID-19 pneumonia on chest CT.
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Affiliation(s)
- Aditya Killekar
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | | | - Andrew Lin
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | - Sebastien Cadet
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | - Priscilla McElhinney
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | - Aryabod Razipour
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | - Cato Chan
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | - Barry D. Pressman
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | - Peter Julien
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | - Peter Chen
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | | | | | | | - Udit Thakur
- Monash Health, Melbourne, Victoria, Australia
| | | | - Cecilia Agalbato
- University of Milan, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | | | | | - Roberto Menè
- IRCCS Istituto Auxologico Italiano, Department of Cardiovascular, Neural and Metabolic Sciences, Milan, Italy
- University of Milano-Bicocca, Department of Medicine and Surgery, Milan, Italy
| | - Gianfranco Parati
- IRCCS Istituto Auxologico Italiano, Department of Cardiovascular, Neural and Metabolic Sciences, Milan, Italy
- University of Milano-Bicocca, Department of Medicine and Surgery, Milan, Italy
| | - Franco Cernigliaro
- IRCCS Istituto Auxologico Italiano, Department of Cardiovascular, Neural and Metabolic Sciences, Milan, Italy
- University of Milano-Bicocca, Department of Medicine and Surgery, Milan, Italy
| | | | - Camilla Torlasco
- IRCCS Istituto Auxologico Italiano, Department of Cardiovascular, Neural and Metabolic Sciences, Milan, Italy
- University of Milano-Bicocca, Department of Medicine and Surgery, Milan, Italy
| | - Gianluca Pontone
- University of Milan, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Damini Dey
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
| | - Piotr Slomka
- Cedars-Sinai Medical Center, Department of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Los Angeles, California, United States
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11
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Pieszko K, Shanbhag A, Killekar A, Miller RJ, Lemley M, Otaki Y, Singh A, Kwiecinski J, Gransar H, Van Kriekinge SD, Kavanagh PB, Miller EJ, Bateman T, Liang JX, Berman DS, Dey D, Slomka PJ. Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events. JACC Cardiovasc Imaging 2022; 16:675-687. [PMID: 36284402 DOI: 10.1016/j.jcmg.2022.06.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/06/2022] [Accepted: 06/09/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. CAC is also visible in computed tomographic attenuation correction (CTAC) scans, always acquired with cardiac positron emission tomographic (PET) imaging. OBJECTIVES The aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans. METHODS The novel DL model, originally developed for video applications, was adapted to rapidly quantify CAC. The model was trained using 9,543 expert-annotated CT scans and was tested in 4,331 patients from an external cohort undergoing PET/CT imaging with major adverse cardiac events (MACEs) (follow-up 4.3 years), including same-day paired electrocardiographically gated CAC scans available in 2,737 patients. MACE risk stratification in 4 CAC score categories (0, 1-100, 101-400, and >400) was analyzed and CAC scores derived from electrocardiographically gated CT scans (standard scores) by expert observers were compared with automatic DL scores from CTAC scans. RESULTS Automatic DL scoring required <6 seconds per scan. DL CTAC scores provided stepwise increase in the risk for MACE across the CAC score categories (HR up to 3.2; P < 0.001). Net reclassification improvement of standard CAC scores over DL CTAC scores was nonsignificant (-0.02; 95% CI: -0.11 to 0.07). The negative predictive values for MACE of zero CAC with standard (85%) and DL CTAC (83%) CAC scores were similar (P = 0.19). CONCLUSIONS DL CTAC scores predict cardiovascular risk similarly to standard CAC scores quantified manually by experienced operators from dedicated electrocardiographically gated CAC scans and can be obtained almost instantly, with no changes to PET/CT scanning protocol.
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12
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Singh A, Kwiecinski J, Miller RJH, Otaki Y, Kavanagh PB, Van Kriekinge SD, Parekh T, Gransar H, Pieszko K, Killekar A, Tummala R, Liang JX, Di Carli M, Berman DS, Dey D, Slomka PJ. Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images. Circ Cardiovasc Imaging 2022; 15:e014526. [PMID: 36126124 PMCID: PMC10035936 DOI: 10.1161/circimaging.122.014526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing. METHODS A total of 4735 consecutive patients referred for stress and rest 82Rb positron emission tomography between 2010 and 2018 were followed up for all-cause mortality for 4.15 (2.24-6.3) years. DL network utilized polar maps of stress and rest perfusion, myocardial blood flow, myocardial flow reserve, and spill-over fraction combined with cardiac volumes, singular indices, and sex. Patients scanned from 2010 to 2016 were used for training and validation. The network was tested in a set of 1135 patients scanned from 2017 to 2018 to simulate prospective clinical implementation. RESULTS In prospective testing, the area under the receiver operating characteristic curve for all-cause mortality prediction by DL (0.82 [95% CI, 0.77-0.86]) was higher than ischemia (0.60 [95% CI, 0.54-0.66]; P <0.001), myocardial flow reserve (0.70 [95% CI, 0.64-0.76], P <0.001) or a comprehensive logistic regression model (0.75 [95% CI, 0.69-0.80], P <0.05). The highest quartile of patients by DL had an annual all-cause mortality rate of 11.87% and had a 16.8 ([95% CI, 6.12%-46.3%]; P <0.001)-fold increase in the risk of death compared with the lowest quartile patients. DL showed a 21.6% overall reclassification improvement as compared with established measures of ischemia. CONCLUSIONS The DL model trained directly on polar maps allows improved patient risk stratification in comparison with established methods for positron emission tomography flow or perfusion assessments.
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Affiliation(s)
- Ananya Singh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jacek Kwiecinski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Robert JH Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada
| | - Yuka Otaki
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul B. Kavanagh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Serge D. Van Kriekinge
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Tejas Parekh
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Heidi Gransar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Interventional Cardiology and Cardiac Surgery, Collegium Medicum, University of Zielona Góra, Zielona Góra, Poland
| | - Aditya Killekar
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ramyashree Tummala
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joanna X. Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Marcelo Di Carli
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Daniel S. Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J. Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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13
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Simon J, Grodecki K, Cadet S, Killekar A, Slomka P, Zara SJ, Zsarnóczay E, Nardocci C, Nagy N, Kristóf K, Vásárhelyi B, Müller V, Merkely B, Dey D, Maurovich-Horvat P. Radiomorphological signs and clinical severity of SARS-CoV-2 lineage B.1.1.7. BJR Open 2022; 4:20220016. [PMID: 36452055 PMCID: PMC9667478 DOI: 10.1259/bjro.20220016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 04/09/2022] [Indexed: 11/05/2022] Open
Abstract
Objective We aimed to assess the differences in the severity and chest-CT radiomorphological signs of SARS-CoV-2 B.1.1.7 and non-B.1.1.7 variants. Methods We collected clinical data of consecutive patients with laboratory-confirmed COVID-19 and chest-CT imaging who were admitted to the Emergency Department between September 1- November 13, 2020 (non-B.1.1.7 cases) and March 1-March 18, 2021 (B.1.1.7 cases). We also examined the differences in the severity and radiomorphological features associated with COVID-19 pneumonia. Total pneumonia burden (%), mean attenuation of ground-glass opacities and consolidation were quantified using deep-learning research software. Results The final population comprised 500 B.1.1.7 and 500 non-B.1.1.7 cases. Patients with B.1.1.7 infection were younger (58.5 ± 15.6 vs 64.8 ± 17.3; p < .001) and had less comorbidities. Total pneumonia burden was higher in the B.1.1.7 patient group (16.1% [interquartile range (IQR):6.0-34.2%] vs 6.6% [IQR:1.2-18.3%]; p < .001). In the age-specific analysis, in patients <60 years B.1.1.7 pneumonia had increased consolidation burden (0.1% [IQR:0.0-0.7%] vs 0.1% [IQR:0.0-0.2%]; p < .001), and severe COVID-19 was more prevalent (11.5% vs 4.9%; p = .032). Mortality rate was similar in all age groups. Conclusion Despite B.1.1.7 patients were younger and had fewer comorbidities, they experienced more severe disease than non-B.1.1.7 patients, however, the risk of death was the same between the two groups. Advances in knowledge Our study provides data on deep-learning based quantitative lung lesion burden and clinical outcomes of patients infected by B.1.1.7 VOC. Our findings might serve as a model for later investigations, as new variants are emerging across the globe.
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Affiliation(s)
| | | | - Sebastian Cadet
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Aditya Killekar
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Piotr Slomka
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | | | | | - Chiara Nardocci
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Norbert Nagy
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Katalin Kristóf
- Department of Laboratory Medicine, Semmelweis University, Budapest, Hungary
| | - Barna Vásárhelyi
- Department of Laboratory Medicine, Semmelweis University, Budapest, Hungary
| | - Veronika Müller
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Béla Merkely
- MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, USA
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14
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Lin A, Manral N, McElhinney P, Killekar A, Matsumoto H, Kwiecinski J, Pieszko K, Razipour A, Grodecki K, Park C, Otaki Y, Doris M, Kwan AC, Han D, Kuronuma K, Flores Tomasino G, Tzolos E, Shanbhag A, Goeller M, Marwan M, Gransar H, Tamarappoo BK, Cadet S, Achenbach S, Nicholls SJ, Wong DT, Berman DS, Dweck M, Newby DE, Williams MC, Slomka PJ, Dey D. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health 2022; 4:e256-e265. [PMID: 35337643 PMCID: PMC9047317 DOI: 10.1016/s2589-7500(22)00022-x] [Citation(s) in RCA: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 01/01/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Atherosclerotic 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. METHODS This 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. FINDINGS In 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 mm3 or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70-16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07-5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99-1·04; p=0·35). INTERPRETATION Our 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. FUNDING National Heart, Lung, and Blood Institute and the Miriam & Sheldon G Adelson Medical Research Foundation.
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Affiliation(s)
- Andrew Lin
- Biomedical 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 Manral
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aditya Killekar
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Hidenari Matsumoto
- Division of Cardiology, Showa University School of Medicine, Tokyo, Japan
| | - Jacek Kwiecinski
- Division 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 Pieszko
- Division 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 Razipour
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kajetan Grodecki
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Cardiology, Medical University of Warsaw, Warsaw, Poland
| | - Caroline Park
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Yuka Otaki
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mhairi Doris
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Alan C Kwan
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Donghee Han
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Keiichiro Kuronuma
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Guadalupe Flores Tomasino
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Evangelos Tzolos
- Department 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 Shanbhag
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Markus Goeller
- Department of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Mohamed Marwan
- Department of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Heidi Gransar
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Balaji K Tamarappoo
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sebastien Cadet
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Stephan Achenbach
- Department of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Stephen J Nicholls
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University, Melbourne, VIC, Australia; MonashHeart, Monash Health, Melbourne, VIC, Australia
| | - Dennis T Wong
- Monash Cardiovascular Research Centre, Victorian Heart Institute, Monash University, Melbourne, VIC, Australia; MonashHeart, Monash Health, Melbourne, VIC, Australia
| | - Daniel S Berman
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Marc Dweck
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Piotr J Slomka
- Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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15
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Singh A, Pieszko K, Shanbhag A, Kwiecinski J, Miller RJH, Otaki Y, Killekar A, Kavanagh P, Van Kriekinge S, Wei CC, Parekh T, Berman DS, Slomka P. IMPROVED MORTALITY RISK ASSESSMENT FROM MYOCARDIAL PET FLOW, PERFUSION AND CALCIUM SCORES USING ARTIFICIAL INTELLIGENCE. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)02173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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16
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Pieszko K, Shanbhag A, Killekar A, Miller RJH, Lemley M, Hyun MC, Singh A, Otaki Y, Van Kriekinge SD, Kavanagh P, Gransar H, Miller EJ, Bateman T, Dey D, Berman DS, Slomka P. DEEP LEARNING FROM UNGATED LOW-DOSE CT ATTENUATION CORRECTION MAPS PREDICTS MAJOR ADVERSE CARDIAC EVENTS SIMILAR TO STANDARD CORONARY CALCIUM SCORES. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)02218-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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17
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Lin A, Manral N, McElhinney P, Killekar A, Matsumoto H, Kwiecinski J, Pieszko K, Grodecki K, Otaki Y, Han D, Tzolos E, Shanbhag A, Goeller M, Marwan M, Gransar H, Cadet S, Achenbach S, Nicholls SJ, Wong DTL, Berman DS, Dweck M, Newby DE, Williams MC, Slomka P, Dey D. DEEP LEARNING FROM CORONARY COMPUTED TOMOGRAPHY ANGIOGRAPHY FOR ATHEROSCLEROTIC PLAQUE AND STENOSIS QUANTIFICATION AND CARDIAC RISK PREDICTION. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)04474-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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18
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Pieszko K, Shanbhag A, Killekar A, Lemley M, Otaki Y, Kriekinge SV, Kavanagh P, Miller RJ, Miller EJ, Bateman T, Dey D, Berman D, Slomka P. Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks. Proc SPIE Int Soc Opt Eng 2022; 12032:120323A. [PMID: 36277935 PMCID: PMC9585987 DOI: 10.1117/12.2613147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We aimed to develop a novel deep-learning based method for automatic coronary artery calcium (CAC) quantification in low-dose ungated computed tomography attenuation correction maps (CTAC). In this study, we used convolutional long-short -term memory deep neural network (conv-LSTM) to automatically derive coronary artery calcium score (CAC) from both standard CAC scans and low-dose ungated scans (CT-attenuation correction maps). We trained convLSTM to segment CAC using 9543 scans. A U-Net model was trained as a reference method. Both models were validated in the OrCaCs dataset (n=32) and in the held-out cohort (n=507) without prior coronary interventions who had CTAC standard CAC scan acquired contemporarily. Cohen's kappa coefficients and concordance matrices were used to assess agreement in four CAC score categories (very low: <10, low:10-100; moderate:101-400 and high >400). The median time to derive results on a central processing unit (CPU) was significantly shorter for the conv-LSTM model- 6.18s (inter quartile range [IQR]: 5.99, 6.3) than for UNet (10.1s, IQR: 9.82, 15.9s, p<0.0001). The memory consumption during training was much lower for our model (13.11Gb) in comparison with UNet (22.31 Gb). Conv-LSTM performed comparably to UNet in terms of agreement with expert annotations, but with significantly shorter inference times and lower memory consumption.
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Affiliation(s)
- K Pieszko
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - A Shanbhag
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - A Killekar
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - M Lemley
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Y Otaki
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Serge Van Kriekinge
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Kavanagh
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Jh Miller
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Tim Bateman
- Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA
| | - D Dey
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - D Berman
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - P Slomka
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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19
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Lin A, Manral N, McElhinney P, Killekar A, Matsumoto H, Cadet S, Achenbach S, Nicholls SJ, Wong DT, Berman D, Dweck M, Newby DE, Williams MC, Slomka PJ, Dey D. Deep learning-based plaque quantification from coronary computed tomography angiography: external validation and comparison with intravascular ultrasound. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Atherosclerotic plaque quantification from coronary computed tomography angiography (CTA) enables accurate assessment of coronary artery disease burden, progression, and prognosis. However, quantitative plaque analysis is time-consuming and requires high expertise. We sought to develop and externally validate an artificial intelligence (AI)-based deep learning (DL) approach for CTA-derived measures of plaque volume and stenosis severity. We compared the performance of DL to expert readers and the gold standard of intravascular ultrasound (IVUS).
Methods
This was a multicenter study of patients undergoing coronary CTA at 11 sites, with software-based quantitative plaque measurements performed at a per-lesion level by expert readers. AI-based plaque analysis was performed by a DL novel convolutional neural network which automatically segmented the coronary artery wall, lumen, and plaque for the computation of plaque volume and stenosis severity. Using expert measurements as ground truth, the DL algorithm was trained on 887 patients (4,686 lesions). Thereafter, the algorithm was applied to an independent test set of 221 patients (1,234 lesions), which included an external validation cohort of 171 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) trial as well as 50 patients who underwent IVUS within one month of CTA. We report the performance of AI-based plaque analysis in the independent test set.
Results
Within the external validation cohort, there was excellent agreement between DL and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0.876), noncalcified plaque volume (ICC 0.869), and percent diameter stenosis (ICC 0.850; all p<0.001). When compared with IVUS, there was excellent agreement for DL total plaque volume (ICC 0.945), total plaque burden (ICC 0.853), minimal luminal area (ICC 0.864), and percent area stenosis (ICC 0.805; all p<0.001); with strong correlation between DL and IVUS for total plaque volume (r=0.915; p<0.001; Figure). The average DL plaque analysis time was 20 seconds per patient, compared with 25–30 minutes taken by experts.
Conclusions
AI-based plaque quantification from coronary CTA using an externally validated DL approach enables rapid measurements of plaque volume and stenosis severity in close agreement with expert readers and IVUS.
Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute, United States
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Affiliation(s)
- A Lin
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - N Manral
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - P McElhinney
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - A Killekar
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - H Matsumoto
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - S Cadet
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - S Achenbach
- Friedrich Alexander University, Erlangen, Germany
| | | | - D T Wong
- Monash Heart, Melbourne, Australia
| | - D Berman
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - M Dweck
- University of Edinburgh, Edinburgh, United Kingdom
| | - D E Newby
- University of Edinburgh, Edinburgh, United Kingdom
| | - M C Williams
- University of Edinburgh, Edinburgh, United Kingdom
| | - P J Slomka
- Cedars-Sinai Medical Center, Los Angeles, United States of America
| | - D Dey
- Cedars-Sinai Medical Center, Los Angeles, United States of America
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20
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Grodecki K, Killekar A, Lin A, Cadet S, McElhinney P, Razipour A, Chan C, Pressman BD, Julien P, Simon J, Maurovich-Horvat P, Gaibazzi N, Thakur U, Mancini E, Agalbato C, Munechika J, Matsumoto H, Menè R, Parati G, Cernigliaro F, Nerlekar N, Torlasco C, Pontone G, Dey D, Slomka PJ. Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks. ArXiv 2021:arXiv:2104.00138v3. [PMID: 33821209 PMCID: PMC8020980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 07/17/2021] [Indexed: 11/19/2022]
Abstract
Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in Coronavirus disease 2019 (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose a new fully automated deep learning framework for quantification and differentiation between lung lesions in COVID-19 pneumonia from both contrast and non-contrast CT images using convolutional Long Short-Term Memory (LSTM) networks. Utilizing the expert annotations, model training was performed using 5-fold cross-validation to segment ground-glass opacity and high opacity (including consolidation and pleural effusion). The performance of the method was evaluated on CT data sets from 197 patients with positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. Strong agreement between expert manual and automatic segmentation was obtained for lung lesions with a Dice score coefficient of 0.876 ± 0.005; excellent correlations of 0.978 and 0.981 for ground-glass opacity and high opacity volumes. In the external validation set of 67 patients, there was dice score coefficient of 0.767 ± 0.009 as well as excellent correlations of 0.989 and 0.996 for ground-glass opacity and high opacity volumes. Computations for a CT scan comprising 120 slices were performed under 2 seconds on a personal computer equipped with NVIDIA Titan RTX graphics processing unit. Therefore, our deep learning-based method allows rapid fully-automated quantitative measurement of pneumonia burden from CT and may generate the big data with an accuracy similar to the expert readers.
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Affiliation(s)
- Kajetan Grodecki
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aditya Killekar
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrew Lin
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sebastien Cadet
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Priscilla McElhinney
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aryabod Razipour
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cato Chan
- Department of Imaging, Cedars-Sinai Medical Center, USA
| | | | - Peter Julien
- Department of Imaging, Cedars-Sinai Medical Center, USA
| | - Judit Simon
- Department of Radiology, Semmelweis University, Budapest, Hungary
| | | | - Nicola Gaibazzi
- Cardiology, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | | | | | | | - Jiro Munechika
- Division of Radiology, Showa University School of Medicine, Tokyo, Japan
| | - Hidenari Matsumoto
- Division of Cardiology, Showa University School of Medicine, Tokyo, Japan
| | - Roberto Menè
- Department of Cardiovascular, Neural and Metabolic Sciences, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Gianfranco Parati
- Department of Cardiovascular, Neural and Metabolic Sciences, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Italy
| | - Franco Cernigliaro
- Department of Cardiovascular, Neural and Metabolic Sciences, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Italy
| | | | - Camilla Torlasco
- Department of Cardiovascular, Neural and Metabolic Sciences, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Italy
| | | | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr J. Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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