Search Results - "Commandeur, Frederic"

Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study by Commandeur, Frederic, Slomka, Piotr J, Goeller, Markus, Chen, Xi, Cadet, Sebastien, Razipour, Aryabod, McElhinney, Priscilla, Gransar, Heidi, Cantu, Stephanie, Miller, Robert J H, Rozanski, Alan, Achenbach, Stephan, Tamarappoo, Balaji K, Berman, Daniel S, Dey, Damini

“…Abstract Aims Our aim was to evaluate the performance of machine learning (ML), integrating clinical parameters with coronary artery calcium (CAC), and…”
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Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography by Goeller, Markus, Tamarappoo, Balaji K, Kwan, Alan C, Cadet, Sebastien, Commandeur, Frederic, Razipour, Aryabod, Slomka, Piotr J, Gransar, Heidi, Chen, Xi, Otaki, Yuka, Friedman, John D, Cao, J Jane, Albrecht, Moritz H, Bittner, Daniel O, Marwan, Mohamed, Achenbach, Stephan, Berman, Daniel S, Dey, Damini

“…Increased attenuation of pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) from coronary computed tomography angiography (CTA)…”
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Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study by 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, Slomka, Piotr J

“…Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely…”
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Deep Learning–Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects by Eisenberg, Evann, McElhinney, Priscilla A., Commandeur, Frederic, Chen, Xi, Cadet, Sebastien, Goeller, Markus, Razipour, Aryabod, Gransar, Heidi, Cantu, Stephanie, Miller, Robert J.H., Slomka, Piotr J., Wong, Nathan D., Rozanski, Alan, Achenbach, Stephan, Tamarappoo, Balaji K., Berman, Daniel S., Dey, Damini

“…Epicardial adipose tissue (EAT) volume (cm ) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate…”
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Epicardial adipose tissue density and volume are related to subclinical atherosclerosis, inflammation and major adverse cardiac events in asymptomatic subjects by Goeller, Markus, Achenbach, Stephan, Marwan, Mohamed, Doris, Mhairi K., Cadet, Sebastien, Commandeur, Frederic, Chen, Xi, Slomka, Piotr J., Gransar, Heidi, Cao, J. Jane, Wong, Nathan D., Albrecht, Moritz H., Rozanski, Alan, Tamarappoo, Balaji K., Berman, Daniel S., Dey, Damini

“…We investigated whether epicardial adipose tissue (EAT) volume and density are related to early atherosclerosis, plaque inflammation and major adverse cardiac…”
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Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry by Hu, Lien-Hsin, Betancur, Julian, 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, Germano, Guido, Commandeur, Frederic, Liang, Joanna X, Otaki, Yuka, Tamarappoo, Balaji K, Dey, Damini, Berman, Daniel S, Slomka, Piotr J

“…Abstract Aims To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography…”
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Metabolic syndrome, fatty liver, and artificial intelligence-based epicardial adipose tissue measures predict long-term risk of cardiac events: a prospective study by Lin, Andrew, Wong, Nathan D, Razipour, Aryabod, McElhinney, Priscilla A, Commandeur, Frederic, Cadet, Sebastien J, Gransar, Heidi, Chen, Xi, Cantu, Stephanie, Miller, Robert J H, Nerlekar, Nitesh, Wong, Dennis T L, Slomka, Piotr J, Rozanski, Alan, Tamarappoo, Balaji K, Berman, Daniel S, Dey, Damini

“…We sought to evaluate the association of metabolic syndrome (MetS) and computed tomography (CT)-derived cardiometabolic biomarkers (non-alcoholic fatty liver…”
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Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry by 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., Slomka, Piotr J.

“…Upper reference limits for transient ischemic dilation (TID) have not been rigorously established for cadmium-zinc-telluride (CZT) camera systems. We aimed to…”
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Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT by 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., Slomka, Piotr J.

“…The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion…”
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Haralick textural features on T 2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer by Gnep, Khémara, Fargeas, Auréline, Gutiérrez-Carvajal, Ricardo E, Commandeur, Frédéric, Mathieu, Romain, Ospina, Juan D, Rolland, Yan, Rohou, Tanguy, Vincendeau, Sébastien, Hatt, Mathieu, Acosta, Oscar, de Crevoisier, Renaud

“…To explore the association between magnetic resonance imaging (MRI), including Haralick textural features, and biochemical recurrence following prostate cancer…”
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Radiomics to Identify High-Risk Atherosclerotic Plaque From Computed Tomography: The Power of Quantification by Dey, Damini, Commandeur, Frederic

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Population model of bladder motion and deformation based on dominant eigenmodes and mixed-effects models in prostate cancer radiotherapy by Rios, Richard, De Crevoisier, Renaud, Ospina, Juan D., Commandeur, Frederic, Lafond, Caroline, Simon, Antoine, Haigron, Pascal, Espinosa, Jairo, Acosta, Oscar

“…•We propose a population model to estimate the probability of the bladder presence on a given region during treatment using only the planning CT scan as input…”
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Radiomics to identify high-risk atherosclerotic plaque from CT: the power of quantification by Dey, Damini, Commandeur, Frederic

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MRI to CT Prostate Registration for Improved Targeting in Cancer External Beam Radiotherapy by Commandeur, Frederic, Simon, Antoine, Mathieu, Romain, Nassef, Mohamed, Ospina Arango, Juan David, Rolland, Yan, Haigron, Pascal, de Crevoisier, Renaud, Acosta, Oscar

“…External radiotherapy is a major clinical treatment for localized prostate cancer. Currently, computed tomography (CT) is used to delineate the prostate and to…”
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Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT by Commandeur, Frederic, Goeller, Markus, Betancur, Julian, Cadet, Sebastien, Doris, Mhairi, Chen, Xi, Berman, Daniel S., Slomka, Piotr J., Tamarappoo, Balaji K., Dey, Damini

“…Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine…”
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Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study by 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, Slomka, Piotr J

“…The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion…”
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Cardiac CT: Technological Advances in Hardware, Software, and Machine Learning Applications by Commandeur, Frederic, Goeller, Markus, Dey, Damini

“…Purpose of Review Multidetector row computed tomography (CT) allows noninvasive imaging of the heart and coronary arteries. The purpose of this review is to…”
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Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study by Tamarappoo, Balaji K., Lin, Andrew, Commandeur, Frederic, McElhinney, Priscilla A., Cadet, Sebastien, Goeller, Markus, Razipour, Aryabod, Chen, Xi, Gransar, Heidi, Cantu, Stephanie, Miller, Robert JH, Achenbach, Stephan, Friedman, John, Hayes, Sean, Thomson, Louise, Wong, Nathan D., Rozanski, Alan, Slomka, Piotr J., Berman, Daniel S., Dey, Damini

“…We sought to assess the performance of a comprehensive machine learning (ML) risk score integrating circulating biomarkers and computed tomography (CT)…”
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Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT by Hu, Lien-Hsin, Miller, Robert J H, Sharir, Tali, Commandeur, Frederic, Rios, Richard, 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, Eisenberg, Evann, Dey, Damini, Berman, Daniel S, Slomka, Piotr J

“…Abstract Aims Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) stress-only protocols reduce radiation exposure and cost…”
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Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study by Commandeur, Frederic, Goeller, Markus, Razipour, Aryabod, Cadet, Sebastien, Hell, Michaela M, Kwiecinski, Jacek, Chen, Xi, Chang, Hyuk-Jae, Marwan, Mohamed, Achenbach, Stephan, Berman, Daniel S, Slomka, Piotr J, Tamarappoo, Balaji K, Dey, Damini

“…To evaluate the performance of deep learning for robust and fully automated quantification of epicardial adipose tissue (EAT) from multicenter cardiac CT data…”
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