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3. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Author

Pieszko, Konrad, Shanbhag, Aakash D., Singh, Ananya, Hauser, M. Timothy, Miller, Robert J. H., Liang, Joanna X., Motwani, Manish, Kwieciński, Jacek, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Berman, Daniel S., Dey, Damini, and Slomka, Piotr J.

Published
2023
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4. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry

Author

Rios, Richard, Miller, Robert JH, Manral, Nipun, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Van Kriekinge, Serge D, Kavanagh, Paul B, Parekh, Tejas, Liang, Joanna X, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Health Services and Systems, Health Sciences, Information and Computing Sciences, Applied Computing, Patient Safety, Cardiovascular, Heart Disease, Good Health and Well Being, Humans, Machine Learning, Myocardial Perfusion Imaging, Registries, Tomography, Emission-Computed, Single-Photon, Machine learning, Clinical implementation, Missing values, Prognosis, Myocardial perfusion imaging, Engineering, Medical and Health Sciences, Biomedical Engineering, Bioinformatics and computational biology, Health services and systems, Applied computing
Abstract

BackgroundMachine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk.MethodsWe included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD).ResultsDuring mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p

Published
2022

7. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

Author

Williams, Michelle C., Bednarski, Bryan P., Pieszko, Konrad, Miller, Robert J. H., Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F., Einstein, Andrew J., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Fish, Mathews B., Ruddy, Terrence D., Acampa, Wanda, Hauser, M. Timothy, Kaufmann, Philipp A., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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10. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

Author

Miller, Robert J.H., Bednarski, Bryan P., Pieszko, Konrad, Kwiecinski, Jacek, Williams, Michelle C., Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Hauser, M. Timothy, Dorbala, Sharmila, Di Carli, Marcelo F., Fish, Mathews B., Ruddy, Terrence D., Bateman, Timothy M., Einstein, Andrew J., Kaufmann, Philipp A., Miller, Edward J., Sinusas, Albert J., Acampa, Wanda, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2024
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11. Characterizing Hypertension Specialist Care in Canada: A National Survey

Author

Lui, Samantha, Dubrofsky, Lisa, Khan, Nadia A., Tobe, Sheldon W., Huynh, Jessica, Kuyper, Laura, Mathew, Anna, Amin, Syed, Schiffrin, Ernesto L., Harvey, Paula, Leung, Alexander A., Ruzicka, Marcel, Mangat, Birinder, Reid, David, Floras, John, Bittman, Jesse, Garbutt, Lauren, Braam, Branko, Suri, Rita, Hannah-Shmouni, Fady, Prebtani, Ally, Savard, Sebastien, MacMillan, Thomas E., Ruddy, Terrence D., Vallee, Michel, Bollu, Apoorva, Logan, Alexander, Padwal, Raj, and Ringrose, Jennifer

Published
2023
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12. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images

Author

Miller, Robert J. H., Singh, Ananya, Otaki, Yuka, Tamarappoo, Balaji K., Kavanagh, Paul, Parekh, Tejas, Hu, Lien-Hsin, Gransar, Heidi, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo F., Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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15. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry

Author

Han, Donghee, Rozanski, Alan, Gransar, Heidi, Tzolos, Evangelos, Miller, Robert J. H., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Hu, Lien-Hsin, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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20. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT

Author

Eisenberg, Evann, Miller, Robert J. H., Hu, Lien-Hsin, Rios, Richard, Betancur, Julian, Azadani, Peyman, Han, Donghee, Sharir, Tali, Einstein, Andrew J., Bokhari, Sabahat, Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Otaki, Yuka, Tamarappoo, Balaji K., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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22. Myocardial Perfusion PET for the Detection and Reporting of Coronary Microvascular Dysfunction: A JACC: Cardiovascular Imaging Expert Panel Statement

Author

Schindler, Thomas H., Fearon, William F., Pelletier-Galarneau, Matthieu, Ambrosio, Giuseppe, Sechtem, Udo, Ruddy, Terrence D., Patel, Krishna K., Bhatt, Deepak L., Bateman, Timothy M., Gewirtz, Henry, Shirani, Jamshid, Knuuti, Juhani, Gropler, Robert J., Chareonthaitawee, Panithaya, Slart, Riemer H.J.A., Windecker, Stephan, Kaufmann, Philipp A., Abraham, Maria R., Taqueti, Viviany R., Ford, Thomas J., Camici, Paolo G., Schelbert, Heinrich R., and Dilsizian, Vasken

Published
2023
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23. Myocardial Ischemic Burden and Differences in Prognosis Among Patients With and Without Diabetes: Results From the Multicenter International REFINE SPECT Registry

Author

Han, Donghee, Rozanski, Alan, Gransar, Heidi, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X, Hu, Lien-Hsin, Germano, Guido, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Cardiovascular, Heart Disease, Biomedical Imaging, Heart Disease - Coronary Heart Disease, Diabetes, Metabolic and endocrine, Good Health and Well Being, Aged, Angina, Unstable, Cohort Studies, Coronary Artery Disease, Diabetes Mellitus, Diabetic Angiopathies, Female, Humans, Male, Middle Aged, Myocardial Infarction, Myocardial Ischemia, Myocardial Perfusion Imaging, Prevalence, Prognosis, Propensity Score, Registries, Risk Factors, Tomography, Emission-Computed, Single-Photon, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectivePrevalence and prognostic impact of cardiovascular disease differ between patients with or without diabetes. We aimed to explore differences in the prevalence and prognosis of myocardial ischemia by automated quantification of total perfusion deficit (TPD) among patients with and without diabetes.Research design and methodsOf 20,418 individuals who underwent single-photon emission computed tomography myocardial perfusion imaging, 2,951 patients with diabetes were matched to 2,951 patients without diabetes based on risk factors using propensity score. TPD was categorized as TPD = 0%, 0% < TPD < 1%, 1% ≤ TPD < 5%, 5% ≤ TPD ≤ 10%, and TPD >10%. Major adverse cardiovascular events (MACE) were defined as a composite of all-cause mortality, myocardial infarction, unstable angina, or late revascularization.ResultsMACE risk was increased in patients with diabetes compared with patients without diabetes at each level of TPD above 0 (P < 0.001 for interaction). In patients with TPD >10%, patients with diabetes had greater than twice the MACE risk compared with patients without diabetes (annualized MACE rate 9.4 [95% CI 6.7-11.6] and 3.9 [95% CI 2.8-5.6], respectively, P < 0.001). Patients with diabetes with even very minimal TPD (0% < TPD < 1%) experienced a higher risk for MACE than those with 0% TPD (hazard ratio 2.05 [95% CI 1.21-3.47], P = 0.007). Patients with diabetes with a TPD of 0.5% had a similar MACE risk as patients without diabetes with a TPD of 8%.ConclusionsFor every level of TPD >0%, even a very minimal deficit of 0% < TPD < 1%, the MACE risk was higher in the patients with diabetes compared with patients without diabetes. Patients with diabetes with minimal ischemia had comparable MACE risk as patients without diabetes with significant ischemia.

Published
2020

24. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Author

Singh, Ananya, Miller, Robert J.H., Otaki, Yuka, Kavanagh, Paul, Hauser, Michael T., Tzolos, Evangelos, Kwiecinski, Jacek, Van Kriekinge, Serge, Wei, Chih-Chun, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Huang, Cathleen, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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27. Impact of cardiac size on diagnostic performance of single-photon emission computed tomography myocardial perfusion imaging: insights from the REgistry of Fast Myocardial Perfusion Imaging with NExt generation single-photon emission computed tomography

Author

Randazzo, Michael J, primary, Elias, Pierre, additional, Poterucha, Timothy J, additional, Sharir, Tali, additional, Fish, Matthews B, additional, Ruddy, Terrence D, additional, Kaufmann, Philipp A, additional, Sinusas, Albert J, additional, Miller, Edward J, additional, Bateman, Timothy, additional, Dorbala, Sharmila, additional, Di Carli, Marcelo, additional, Castillo, Michelle, additional, Liang, Joanna X, additional, Miller, Robert J H, additional, Dey, Damini, additional, Berman, Daniel S, additional, Slomka, Piotr J, additional, and Einstein, Andrew J, additional

Published
2024
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32. Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry

Author

Klein, Eyal, Miller, Robert J. H., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Otaki, Yuka, Gransar, Heidi, Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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33. Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

Author

Betancur, Julian, Hu, Lien-Hsin, Commandeur, Frederic, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Germano, Guido, Otaki, Yuka, Liang, Joanna X, Tamarappoo, Balaji K, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Cardiovascular, Heart Disease - Coronary Heart Disease, Clinical Research, Biomedical Imaging, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aged, Coronary Artery Disease, Deep Learning, Female, Heart Ventricles, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Myocardial Perfusion Imaging, Stress, Physiological, Tomography, Emission-Computed, Single-Photon, obstructive coronary artery disease, SPECT myocardial perfusion imaging, deep learning, convolutional neural network, total perfusion deficit, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD). Methods: 1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress 99mTc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. Results: 718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73; P < 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (P < 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (P < 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read (P = 0.3). Conclusion: DL improves automatic interpretation of MPI as compared with current quantitative methods.

Published
2019

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

Author

Betancur, Julian, Commandeur, Frederic, Motlagh, Mahsaw, Sharir, Tali, Einstein, Andrew J, Bokhari, Sabahat, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Germano, Guido, Otaki, Yuka, Tamarappoo, Balaji K, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Biomedical Imaging, Clinical Research, Heart Disease, Cardiovascular, Heart Disease - Coronary Heart Disease, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Aged, Aged, 80 and over, Coronary Circulation, Coronary Stenosis, Deep Learning, Female, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Myocardial Perfusion Imaging, Organophosphorus Compounds, Organotechnetium Compounds, Predictive Value of Tests, Radiopharmaceuticals, Registries, Technetium Tc 99m Sestamibi, Tomography, Emission-Computed, Single-Photon, convolutional neural network, deep learning, obstructive coronary artery disease, SPECT myocardial perfusion imaging, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

OBJECTIVES:The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). BACKGROUND:Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI. METHODS:A total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress 99mTc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied. Obstructive disease was defined as ≥70% narrowing of coronary arteries (≥50% for left main artery). Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. Stress TPD was computed using sex- and camera-specific normal limits. Deep learning was trained using raw and quantitative polar maps and evaluated for prediction of obstructive stenosis in a stratified 10-fold cross-validation procedure. RESULTS:A total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p < 0.01). With deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p < 0.05), and per-vessel sensitivity improved from 64.4% (TPD) to 69.8% (deep learning) (p < 0.01). CONCLUSIONS:Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods.

Published
2018

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

Author

Otaki, Yuka, Singh, Ananya, Kavanagh, Paul, Miller, Robert J.H., Parekh, Tejas, Tamarappoo, Balaji K., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Cadet, Sebastien, Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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42. Practical guide for interpreting and reporting cardiac PET measurements of myocardial blood flow: an Information Statement from the American Society of Nuclear Cardiology, and the Society of Nuclear Medicine and Molecular Imaging

Author

Bateman, Timothy M., Heller, Gary V., Beanlands, Rob, Calnon, Dennis A., Case, James, deKemp, Rob, Gordon DePuey, E., Di Carli, Marcelo, Guler, Emel C., Murthy, Venkatesh L., Rosenblatt, Jeffrey, Sher, Ronald, Slomka, Piotr, and Ruddy, Terrence D.

Published
2021
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49. Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry

Author

Hu, Lien-Hsin, Sharir, Tali, Miller, Robert J.H., Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Dorbala, Sharmila, Di Carli, Marcelo, Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Betancur, Julian, Germano, Guido, Liang, Joanna X., Commandeur, Frederic, Azadani, Peyman N., Gransar, Heidi, Otaki, Yuka, Tamarappoo, Balaji K., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2020
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50. Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT)

Author

Slomka, Piotr J., Betancur, Julian, Liang, Joanna X., Otaki, Yuka, Hu, Lien-Hsin, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo, Fish, Mathews B., Ruddy, Terrence D., Bateman, Timothy M., Einstein, Andrew J., Kaufmann, Philipp A., Miller, Edward J., Sinusas, Albert J., Azadani, Peyman N., Gransar, Heidi, Tamarappoo, Balaji K., Dey, Damini, Berman, Daniel S., and Germano, Guido

Published
2020
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