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1. Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography

Author

Kwiecinski, Jacek, Wang, Kang-Ling, Tzolos, Evangelos, Moss, Alastair, Daghem, Marwa, Adamson, Philip D., Dey, Damini, Molek-Dziadosz, Patrycja, Dawson, Dana, Arumugam, Parthiban, Sabharwal, Nikant, Greenwood, John P., Townend, John N., Calvert, Patrick A., Rudd, James HF., Berman, Daniel, Verjans, Johan W., Williams, Michelle C., Slomka, Piotr, Dweck, Marc R., and Newby, David E.

Published
2024
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3. Combination of computed tomography angiography with coronary artery calcium score for improved diagnosis of coronary artery disease: a collaborative meta-analysis of stable chest pain patients referred for invasive coronary angiography.

Author

Mohamed, Mahmoud, Bosserdt, Maria, Wieske, Viktoria, Dubourg, Benjamin, Alkadhi, Hatem, Garcia, Mario, Leschka, Sebastian, Zimmermann, Elke, Shabestari, Abbas, Nørgaard, Bjarne, Meijs, Matthijs, Øvrehus, Kristian, Diederichsen, Axel, Knuuti, Juhani, Halvorsen, Bjørn, Mendoza-Rodriguez, Vladymir, Wan, Yung-Liang, Bettencourt, Nuno, Martuscelli, Eugenio, Buechel, Ronny, Mickley, Hans, Sun, Kai, Muraglia, Simone, Kaufmann, Philipp, Herzog, Bernhard, Tardif, Jean-Claude, Schütz, Georg, Laule, Michael, Newby, David, Achenbach, Stephan, Haase, Robert, Biavati, Federico, Mézquita, Aldo, Schlattmann, Peter, Dewey, Marc, and Budoff, Matthew

Subjects
Computed tomography angiography, Coronary angiography, Coronary artery disease, Humans, Female, Male, Coronary Artery Disease, Coronary Angiography, Computed Tomography Angiography, Calcium, Predictive Value of Tests, Tomography, X-Ray Computed, Coronary Stenosis, Chest Pain
Abstract

OBJECTIVES: Coronary computed tomography angiography (CCTA) has higher diagnostic accuracy than coronary artery calcium (CAC) score for detecting obstructive coronary artery disease (CAD) in patients with stable chest pain, while the added diagnostic value of combining CCTA with CAC is unknown. We investigated whether combining coronary CCTA with CAC score can improve the diagnosis of obstructive CAD compared with CCTA alone. METHODS: A total of 2315 patients (858 women, 37%) aged 61.1 ± 10.2 from 29 original studies were included to build two CAD prediction models based on either CCTA alone or CCTA combined with the CAC score. CAD was defined as at least 50% coronary diameter stenosis on invasive coronary angiography. Models were built by using generalized linear mixed-effects models with a random intercept set for the original study. The two CAD prediction models were compared by the likelihood ratio test, while their diagnostic performance was compared using the area under the receiver-operating-characteristic curve (AUC). Net benefit (benefit of true positive versus harm of false positive) was assessed by decision curve analysis. RESULTS: CAD prevalence was 43.5% (1007/2315). Combining CCTA with CAC improved CAD diagnosis compared with CCTA alone (AUC: 87% [95% CI: 86 to 89%] vs. 80% [95% CI: 78 to 82%]; p

Published
2024

5. Qualitative and quantitative analysis of 18F-GP1 positron emission tomography in thrombotic cardiovascular disease

Author

Whittington, Beth, Tzolos, Evangelos, Joshi, Shruti, Bing, Rong, Andrews, Jack, Loganath, Krithika, Craig, Neil, Balmforth, Craig, Clark, Laura, Lucatelli, Christophe, MacAskill, Mark G, Tavares, Adriana A. S., Clark, Tim, Mills, Nicholas L., Nash, Jennifer, Dey, Damini, Slomka, Piotr J., Koglin, Norman, Stephens, Andrew W., Dweck, Marc R., Williams, Michelle C., Whiteley, William, van Beek, Edwin J. R., Wardlaw, Joanna M., and Newby, David E.

Published
2024
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11. Assessment of the alpha 7 nicotinic acetylcholine receptor as an imaging marker of cardiac repair-associated processes using NS14490

Author

Reid, Victoria J. M., McLoughlin, Wesley K. X., Pandya, Kalyani, Stott, Holly, Iškauskienė, Monika, Šačkus, Algirdas, Marti, Judit A., Kurian, Dominic, Wishart, Thomas M., Lucatelli, Christophe, Peters, Dan, Gray, Gillian A., Baker, Andrew H., Newby, David E., Hadoke, Patrick W. F., Tavares, Adriana A. S., and MacAskill, Mark G.

Published
2024
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12. First-in-human controlled inhalation of thin graphene oxide nanosheets to study acute cardiorespiratory responses

Author

Andrews, Jack P. M., Joshi, Shruti S., Tzolos, Evangelos, Syed, Maaz B., Cuthbert, Hayley, Crica, Livia E., Lozano, Neus, Okwelogu, Emmanuel, Raftis, Jennifer B., Bruce, Lorraine, Poland, Craig A., Duffin, Rodger, Fokkens, Paul H. B., Boere, A. John F., Leseman, Daan L. A. C., Megson, Ian L., Whitfield, Phil D., Ziegler, Kerstin, Tammireddy, Seshu, Hadjidemetriou, Marilena, Bussy, Cyrill, Cassee, Flemming R., Newby, David E., Kostarelos, Kostas, and Miller, Mark R.

Published
2024
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13. Clinical Quantitative Coronary Artery Stenosis and Coronary Atherosclerosis Imaging: A Consensus Statement from the Quantitative Cardiovascular Imaging Study Group

Author

Vázquez Mézquita, Aldo J., Biavati, Federico, Falk, Volkmar, Alkadhi, Hatem, Hajhosseiny, Reza, Maurovich-Horvat, Pál, Manka, Robert, Kozerke, Sebastian, Stuber, Matthias, Derlin, Thorsten, Channon, Keith M., Išgum, Ivana, Coenen, Adriaan, Foellmer, Bernhard, Dey, Damini, Volleberg, Rick H. J. A., Meinel, Felix G., Dweck, Marc R., Piek, Jan J., van de Hoef, Tim, Landmesser, Ulf, Guagliumi, Giulio, Giannopoulos, Andreas A., Botnar, René M., Khamis, Ramzi, Williams, Michelle C., Newby, David E., Dewey, Marc, Sack, Ingolf, editor, and Schaeffter, Tobias, editor

Published
2024
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14. Roadmap on the Use of Artificial Intelligence for Imaging of Vulnerable Atherosclerotic Plaque in Coronary Arteries

Author

Föllmer, Bernhard, Williams, Michelle C., Dey, Damini, Arbab-Zadeh, Armin, Maurovich-Horvat, Pál, Volleberg, Rick H. J. A., Rueckert, Daniel, Schnabel, Julia A., Newby, David E., Dweck, Marc R., Guagliumi, Giulio, Falk, Volkmar, Vázquez Mézquita, Aldo J., Biavati, Federico, Išgum, Ivana, Dewey, Marc, Sack, Ingolf, editor, and Schaeffter, Tobias, editor

Published
2024
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16. Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis

Author

Miller, Robert J.H., Manral, Nipun, Lin, Andrew, Shanbhag, Aakash, Park, Caroline, Kwiecinski, Jacek, Killekar, Aditya, McElhinney, Priscilla, Matsumoto, Hidenari, Razipour, Aryabod, Grodecki, Kajetan, Kwan, Alan C., Han, Donghee, Kuronuma, Keiichiro, Flores Tomasino, Guadalupe, Geers, Jolien, Goeller, Markus, Marwan, Mohamed, Gransar, Heidi, Tamarappoo, Balaji K., Cadet, Sebastien, Cheng, Victor Y., Achenbach, Stephan, Nicholls, Stephen J., Wong, Dennis T., Chen, Lu, Cao, J. Jane, Berman, Daniel S., Dweck, Marc R., Newby, David E., Williams, Michelle C., Slomka, Piotr J., and Dey, Damini

Published
2024
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17. Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

Author

Lassen, Martin Lyngby, Tzolos, Evangelos, Massera, Daniele, Cadet, Sebastien, Bing, Rong, Kwiecinski, Jacek, Dey, Damini, Berman, Daniel S, Dweck, Marc R, Newby, David E, and Slomka, Piotr J

Subjects
Medical and Biological Physics, Biomedical and Clinical Sciences, Clinical Sciences, Physical Sciences, Cardiovascular, Bioengineering, Heart Disease, Biomedical Imaging, Clinical Research, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Motion correction, PET/CT, Cardiac PET, F-18-sodium fluoride, Aortic valve imaging, 18F-sodium fluoride, Clinical sciences, Biomedical engineering, Medical and biological physics
Abstract

BackgroundCurrent 18F-NaF assessments of aortic valve microcalcification using 18F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements.Materials and methodsFourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUVmax values measured in the aortic valve and maximum target-to-background ratios (TBRmax) values after correcting for blood pool activity.ResultsCompared to standard and ECG-MC reconstructions, increases in both SUVmax and TBRmax were observed following 3 × MC (SUVmax: Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBRmax: Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R2 value) to the AVCS when compared to the standard methods (SUVmax: Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBRmax: Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46).Conclusion3 × MC improves the correlation between the AVCS and SUVmax and TBRmax and should be considered in PET studies of aortic valves using 18F-NaF.

Published
2022

18. Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Author

Dahan, Simon, Xu, Hao, Williams, Logan Z. J., Fawaz, Abdulah, Yang, Chunhui, Coalson, Timothy S., Williams, Michelle C., Newby, David E., Edwards, A. David, Glasser, Matthew F., Young, Alistair A., Rueckert, Daniel, and Robinson, Emma C.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Neurons and Cognition
Abstract

Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem, by proposing patching mechanisms for general surface meshes. Sequences of patches are then processed by a transformer encoder and used for classification or regression. We validate our method on a range of different biomedical surface domains and tasks: brain age prediction in the developing Human Connectome Project (dHCP), fluid intelligence prediction in the Human Connectome Project (HCP), and coronary artery calcium score classification using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART) dataset, and investigate the impact of pretraining and data augmentation on model performance. Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice. Furthermore, analysis of transformer attention maps offers clear and individualised predictions of the features driving each task. Code is available on Github: https://github.com/metrics-lab/surface-vision-transformers, Comment: 10 pages, 3 figures, Submitted to IEEE Transactions on Medical Imaging

Published
2022

19. Clinical Significance of Myocardial Injury in Patients Hospitalized for COVID-19: A Prospective, Multicenter, Cohort Study

Author

Greenwood, J.P., McCann, G.P., Berry, C., Dweck, M., Miller, C.M., Chiribiri, A., Prasad, S., Ferreira, V.M., Bucciarelli-Ducci, C., Dawson, D., Moon, James C., Artico, Jessica, Shiwani, Hunain, Davies, Rhodri, Dweck, Marc, Berry, Colin, Roditi, Giles, Young, Robin, McConnachie, Alex, Kelly, Bernard, Macfarlane, Peter W., Miller, Christopher A., Levelt, Eylem, Goreka, Miroslawa, Somers, Kathryn, Byrom-Goulthorp, Roo J., Anderson, Michelle, Britton, Laura, Richards, Fiona, Jones, Laura M., Arnold, Ranjit, Moss, Alastair, Fisher, Jude, Wormleighton, Joanne, Parke, Kelly, Wright, Rachel, Yeo, Jian, Dawson, Dana, Falconer, Judith, Harries, Valerie, Henderson, Paula, Singh, Trisha, Newby, David, Piechnik, Stefan, Popescu, Iulia, Lukaschuk, Elena, Zhang, Qiang, Shanmuganathan, Mayooran, Neubauer, Stefan, Raman, Betty, Channon, Keith, Krasopoulos, Catherine, Nunes, Claudia, Da Silva Rodrigues, Liliana, Nixon, Harriet, Panopoulou, Athanasia, Fletcher, Alison, Manley, Peter, Mangion, Kenneth, Morrow, Andrew, Sykes, Robert, Fallon, Kirsty, Brown, Ammani, Kelly, Laura, McGinley, Christopher, Briscoe, Michael, Woodward, Rosemary, Hopkins, Tracey, McLennan, Evonne, Tynan, Nicola, Dymock, Laura, Swoboda, Peter, Wright, Judith, Exley, Donna, Steeds, Richard, Hutton, Kady, MacDonald, Sonia, Treibel, Thomas, Shetye, Abhishek, Miller, Christopher M., Orsborne, Christopher, Woodville-Jones, William, Ferguson, Susan, Bratis, Konstantinos, Fairbairn, Timothy, Sionas, Michail, Widdows, Peris, Chew, Pei Gee, Marsden, Christian, Collins, Tom, George, Linsha, Kearney, Lisa, Flett, Andrew, Smith, Simon, Zhenge, Alice, Harvey, Jake, Inacio, Liliana, Hanam-Penfold, Tomas, Gruner, Lucy, Fontana, Marianna, Razvi, Yousuf S.K., Crause, Jacolene, Davies, Nina M., Brown, James T., Chaco, Liza, Patel, Rishi, Kotecha, Tushar, Knight, Dan S., Green, Thomas, Ripley, David, Thompson, Maria, Chiribiri, Amedeo, Akerele, Ugochi, Cifra, Elna, Alskaf, Ebraham, Crawley, Richard, Villa, Adriana, Bucciarelli-Ducci, Chiara, Nightingale, Angus K., Wright, Kim, Bonnick, Esther D., Hopkins, Emma, George, Jessy, Joseph, Linta, Cole, Graham, Vimalesvaran, Kavitha, Ali, Nadine, Carr, Caitlin R., Ross, Alexandra A.R., King, Clara, Prasad, Sanjay, Farzad, Zohreh, Salmi, Sara A., Kirby, Kevin, McDiarmid, Adam, Stevenson, Hannah J., Matsvimbo, Pamela S., Joji, Lency, Fearby, Margaret, Brown, Benjamin, Bunce, Nicholas, Jennings, Robert, Sookhoo, Vennessa, Joshi, Shatabdi, Kanagala, Prathap, Fullalove, Sandra, Toohey, Catherine, Fenlon, Kate, Bellenger, Nicholas, He, Jingzhou, Statton, Sarah, Pamphilon, Nicola, Steele, Anna, Ball, Claire, McGahey, Ann, Balma, Silvia, Wilkes, Lynsey, Lewis, Katy, Walter, Michelle, Ionescu, Adrian, Ninan, Tishi, Richards, Suzanne, Williams, Marie, Alfakih, Khaled, Pilgrim, Samia, Joy, George, Manisty, Charlotte H., Hussain, Ifza, Gorecka, Miroslawa, McCann, Gerry P., Alzahir, Mohammed, Ramirez, Sara, Lin, Andrew, Swoboda, Peter P., McDiarmid, Adam K., Manisty, Charlotte, Treibel, Thomas A., Piechnik, Stefan K., Davies, Rhodri H., Ferreira, Vanessa M., Dweck, Marc R., and Greenwood, John P.

Published
2024
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20. Quantitative Computed Tomography Angiography for the Evaluation of Valvular Fibrocalcific Volume in Aortic Stenosis

Author

Lembo, Maria, Joshi, Shruti S., Geers, Jolien, Bing, Rong, Carnevale, Lorenzo, Pawade, Tania A., Doris, Mhairi K., Tzolos, Evangelos, Grodecki, Kajetan, Cadet, Sebastien, Craig, Neil, Singh, Trisha, Slomka, Piotr J., White, Audrey, Guala, Andrea, Rodriguez-Palomares, Jose F., Ruiz-Muñoz, Aroa, Dux-Santoy, Lydia, Teixido-Tura, Gisela, Galian-Gay, Laura, Williams, Michelle C., Newby, David E., Kwak, Soongu, Lee, Seung-Pyo, Powers, Andreanne, Clavel, Marie-Annick, Dey, Damini, and Dweck, Marc R.

Published
2024
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24. Rationale and Design of SCOT-HEART 2 Trial: CT Angiography for the Prevention of Myocardial Infarction

Author

McDermott, Michael, Meah, Mohammed N., Khaing, Phyo, Wang, Kang-Ling, Ramsay, Jennifer, Scott, Gillian, Rickman, Hannah, Burt, Tom, McGowan, Ian, Fairbairn, Timothy, Bucukoglu, Marise, Bull, Russell, Timmis, Adam, van Beek, Edwin J.R., Roditi, Giles, Adamson, Philip D., Lewis, Steff, Norrie, John, McKinstry, Brian, Guthrie, Bruce, Ritchie, Lewis, Mills, Nicholas L., Dweck, Marc R., Williams, Michelle C., and Newby, David E.

Published
2024
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28. Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a Consensus Statement from the Quantitative Cardiovascular Imaging Study Group

Author

Mézquita, Aldo J. Vázquez, Biavati, Federico, Falk, Volkmar, Alkadhi, Hatem, Hajhosseiny, Reza, Maurovich-Horvat, Pál, Manka, Robert, Kozerke, Sebastian, Stuber, Matthias, Derlin, Thorsten, Channon, Keith M., Išgum, Ivana, Coenen, Adriaan, Foellmer, Bernhard, Dey, Damini, Volleberg, Rick H. J. A., Meinel, Felix G., Dweck, Marc R., Piek, Jan J., van de Hoef, Tim, Landmesser, Ulf, Guagliumi, Giulio, Giannopoulos, Andreas A., Botnar, René M., Khamis, Ramzi, Williams, Michelle C., Newby, David E., and Dewey, Marc

Published
2023
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29. Whole Heart Anatomical Refinement from CCTA using Extrapolation and Parcellation

Author

Xu, Hao, Niederer, Steven A., Williams, Steven E., Newby, David E., Williams, Michelle C., and Young, Alistair A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Coronary computed tomography angiography (CCTA) provides detailed an-atomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be learnt directly from the image (image- to-label mapping). This approach only required manual correction in a small subset of cases (80 for extrapolation, 50 for parcella-tion, compared with 260 for initial labels). An initial 6-label segmentation (left ventricle, left ventricular myocardium, right ventricle, left atrium, right atrium and aorta) was refined to a 10-label segmentation that added a label for the pulmonary artery and divided the left atrium label into body, left and right veins and appendage components. The final method was tested using 30 cases, 10 each from Philips, Siemens and Toshiba scanners. In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e.g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle. This method provides a simple framework for flexible refinement of anatomical labels. The code and executables are available at cemrg.com., Comment: 9 pages, 5 figures, presented at Functional Imaging and Modeling of the Heart 2021

Published
2021

31. Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study

Author

Thomas, Sheena, Denton, Jon, Farral, Robyn, Taylor, Carolyn, Qin, Wendy, Kasongo, Mary, Anthony, Susan, Banning, Adrian, Xie, Cheng, Kharbanda, Rajesh K, Pritchard, Amy, Halborg, Thomas, Syed, Nigar, Fry, Sam, Mathers, Chris, Rose, Anne, Hudson, George, Bajaj, Amrita, Das, Intrajeet, Deshpande, Aparna, Rao, Praveen, Lawday, Dan, Mirsadraee, Saeed, Hudson, Benjamin, Berry, Colin, Marwan, Mohamed, Maurovich-Horvat, Pál, He, Guo-Wei, Lin, Wen-Hua, Fan, Li-Juan, Takahashi, Naohiko, Kondo, Hidekazu, Dai, Neng, Ge, Junbo, Koo, Bon-Kwon, Guglielmo, Marco, Pontone, Gianluca, Huck, Daniel, Benedek, Theodora, Rajani, Ronak, Vilic, Dijana, Aljazzaf, Haleema, Mun, Mak S, Benedetti, Giulia, Preston, Rebecca L, Raisi-Estabragh, Zahra, Connolly, Derek L, Sharma, Vinoda, Grenfell, Rebecca, Bradlow, William, Schmitt, Matthias, Serfaty, Fabiano, Gottlieb, Ilan, Neves, Mario FT, Newby, David E, Dweck, Marc R, Hatem, Stéphane, Redheuil, Alban, Benetos, Georgios, Beer, Meinrad, Granillo, Gastón AR, Selvanayagam, Joseph, Lopez-Jimenez, Francisco, De Bosscher, Ruben, Tavildari, Alain, Figtree, Gemma, Danad, Ibrahim, Shantouf, Ronney, Kietselaer, Bas, Tousoulis, Dimitris, Dangas, George, Mehta, Nehal N, Kontanidis, Christos, Kunadian, Vijay, Fairbairn, Timothy A, Chan, Kenneth, Wahome, Elizabeth, Tsiachristas, Apostolos, Antonopoulos, Alexios S, Patel, Parijat, Lyasheva, Maria, Kingham, Lucy, West, Henry, Oikonomou, Evangelos K, Volpe, Lucrezia, Mavrogiannis, Michail C, Nicol, Edward, Mittal, Tarun K, Kotronias, Rafail A, Adlam, David, Modi, Bhavik, Rodrigues, Jonathan, Screaton, Nicholas, Kardos, Attila, Greenwood, John P, Sabharwal, Nikant, De Maria, Giovanni Luigi, Munir, Shahzad, McAlindon, Elisa, Sohan, Yogesh, Tomlins, Pete, Siddique, Muhammad, Kelion, Andrew, Shirodaria, Cheerag, Pugliese, Francesca, Petersen, Steffen E, Blankstein, Ron, Desai, Milind, Gersh, Bernard J, Achenbach, Stephan, Libby, Peter, Neubauer, Stefan, Channon, Keith M, Deanfield, John, and Antoniades, Charalambos

Published
2024
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34. Coronary Atherosclerotic Plaque Activity and Risk of Myocardial Infarction

Author

Wang, Kang-Ling, Balmforth, Craig, Meah, Mohammed N., Daghem, Marwa, Moss, Alastair J., Tzolos, Evangelos, Kwiecinski, Jacek, Molek-Dziadosz, Patrycja, Craig, Neil, Bularga, Anda, Adamson, Philip D., Dawson, Dana K., Arumugam, Parthiban, Sabharwal, Nikant K., Greenwood, John P., Townend, Jonathan N., Calvert, Patrick A., Rudd, James H.F., Verjans, Johan W., Berman, Daniel S., Slomka, Piotr J., Dey, Damini, Mills, Nicholas L., van Beek, Edwin J.R., Williams, Michelle C., Dweck, Marc R., and Newby, David E.

Published
2024
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36. Troponin in acute chest pain to risk stratify and guide effective use of computed tomography coronary angiography (TARGET-CTCA): a randomised controlled trial

Author

Lee, Kuan Ken, Lowe, David, O’Brien, Rachel, Wereski, Ryan, Bularga, Anda, Taggart, Caelan, Lowry, Matthew T. H., Ferry, Amy V., Williams, Michelle C., Roditi, Giles, Byrne, John, Tuck, Chris, Cranley, Denise, Thokala, Praveen, Goodacre, Steve, Keerie, Catriona, Norrie, John, Newby, David E., Gray, Alasdair J., and Mills, Nicholas L.

Published
2023
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37. Bypass Grafting and Native Coronary Artery Disease Activity

Author

Kwiecinski, Jacek, Tzolos, Evangelos, Fletcher, Alexander J, Nash, Jennifer, Meah, Mohammed N, Cadet, Sebastien, Adamson, Philip D, Grodecki, Kajetan, Joshi, Nikhil, Williams, Michelle C, van Beek, Edwin JR, Lai, Chi, Tavares, Adriana AS, MacAskill, Mark G, Dey, Damini, Baker, Andrew H, Leipsic, Jonathon, Berman, Daniel S, Sellers, Stephanie L, Newby, David E, Dweck, Marc R, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease - Coronary Heart Disease, Atherosclerosis, Cardiovascular, Heart Disease, Clinical Research, Aged, Calcinosis, Calcium, Coronary Angiography, Coronary Artery Disease, Disease Progression, Female, Humans, Male, Middle Aged, Plaque, Atherosclerotic, Predictive Value of Tests, Sodium Fluoride, F-18-NaF, CABG, coronary artery bypass graft, coronary artery disease, PET/CT, (18)F-NaF, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

ObjectivesThe aim of this study was to describe the potential of 18F-sodium fluoride (18F-NaF) positron emission tomography (PET) to identify graft vasculopathy and to investigate the influence of coronary artery bypass graft (CABG) surgery on native coronary artery disease activity and progression.BackgroundAs well as developing graft vasculopathy, CABGs have been proposed to accelerate native coronary atherosclerosis.MethodsPatients with established coronary artery disease underwent baseline 18F-NaF PET, coronary artery calcium scoring, coronary computed tomographic angiography, and 1-year repeat coronary artery calcium scoring. Whole-vessel coronary microcalcification activity (CMA) on 18F-NaF PET and change in calcium scores were quantified in patients with and without CABG surgery.ResultsAmong 293 participants (mean age 65 ± 9 years, 84% men), 48 (16%) underwent CABG surgery 2.7 years [IQR: 1.4-10.4 years] previously. Although all arterial and the majority (120 of 128 [94%]) of vein grafts showed no 18F-NaF uptake, 8 saphenous vein grafts in 7 subjects had detectable CMA. Bypassed native coronary arteries had 3 times higher CMA values (2.1 [IQR: 0.4-7.5] vs 0.6 [IQR: 0-2.7]; P < 0.001) and greater progression of 1-year calcium scores (118 Agatston unit [IQR: 48-194 Agatston unit] vs 69 [IQR: 21-142 Agatston unit]; P = 0.01) compared with patients who had not undergone CABG, an effect confined largely to native coronary plaques proximal to the graft anastomosis. In sensitivity analysis, bypassed native coronary arteries had higher CMA (2.0 [IQR: 0.4-7.5] vs 0.8 [IQR: 0.3-3.2]; P < 0.001) and faster disease progression (24% [IQR: 16%-43%] vs 8% [IQR: 0%-24%]; P = 0.002) than matched patients (n = 48) with comparable burdens of coronary artery disease and cardiovascular comorbidities in the absence of bypass grafting.ConclusionsNative coronary arteries that have been bypassed demonstrate increased disease activity and more rapid disease progression than nonbypassed arteries, an observation that appears independent of baseline atherosclerotic plaque burden. Microcalcification activity is not a dominant feature of graft vasculopathy.

Published
2022

38. Uniform or Sex-Specific Cardiac Troponin Thresholds to Rule Out Myocardial Infarction at Presentation

Author

Mills, Nicholas L., Strachan, Fiona E., Tuck, Christopher, Anand, Atul, Akinlade, Olawale Mathias, Barker, Stephanie, Blades, Jennifer, Boeddinghaus, Jasper, Bularga, Anda, de Bakker, Marie, Chapman, Andrew R., Doudesis, Dimitrios, Ferry, Amy V., Fujisawa, Takeshi, Georgiev, Konstantin, Kimenai, Dorien M., Lee, Kuan Ken, Lyell, Iona, Li, Ziwen, Lowry, Matthew TH., McKinlay, Lynn, McDermott, Michael, McPherson, Jean, Mendusic, Filip, Sorbie, Andrew, Souter, Grace, Schulberg, Stacey D., Taggart, Caelan, Thurston, Alexander JF., Tew, Yong Yong, Perez-Vicencio, Daniel, Wang, Yiqing, Wereski, Ryan, Williams, Kelly, Newby, David E., Fox, Keith AA., Berry, Colin, Walker, Simon, Weir, Christopher J., Ford, Ian, Gray, Alasdair, Collinson, Paul O., Apple, Fred S., Reid, Alan, Cruikshank, Anne, Findlay, Iain, Amoils, Shannon, McAllister, David A., Maguire, Donogh, Stevens, Jennifer, Norrie, John, Shah, Anoop SV., Andrews, Jack PM., Adamson, Philip D., Moss, Alastair, Anwar, Mohamed S., Hung, John, Malo, Jonathan, Fischbacher, Colin M., Croal, Bernard L., Leslie, Stephen J., Keerie, Catriona, Parker, Richard A., Walker, Allan, Harkess, Ronnie, Wackett, Tony, Weir, Christopher, Armstrong, Roma, Stirling, Laura, MacDonald, Claire, Sadat, Imran, Finlay, Frank, Harrison, Kathy, Linksted, Pamela, Lavenberg, Stephen, Lowry, Matthew T.H., Tuck, Chris, and Shah, Anoop S.V.

Published
2024
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39. Observer repeatability and interscan reproducibility of 18F-sodium fluoride coronary microcalcification activity

Author

Tzolos, Evangelos, Kwiecinski, Jacek, Lassen, Martin Lyngby, Cadet, Sebastien, Adamson, Philip D, Moss, Alastair J, Joshi, Nikhil, Williams, Michelle C, van Beek, Edwin JR, Dey, Damini, Berman, Daniel S, Dweck, Marc R, Newby, David E, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Biomedical Imaging, Atherosclerosis, Cardiovascular, Clinical Research, Heart Disease - Coronary Heart Disease, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Calcinosis, Fluorine Radioisotopes, Humans, Observer Variation, Positron Emission Tomography Computed Tomography, Reproducibility of Results, Sodium, Sodium Fluoride, Motion correction, PET, CT, cardiac PET, F-18-NaF, vulnerable plaque, coronary microcalcification activity, 18F-NaF, PET/CT, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundWe aimed to establish the observer repeatability and interscan reproducibility of coronary 18F-sodium-fluoride positron emission tomography (PET) uptake using a novel semi-automated approach, coronary microcalcification activity (CMA).MethodsPatients with multivessel coronary artery disease underwent repeated hybrid PET and computed tomography angiography (CTA) imaging (PET/CTA). CMA was defined as the integrated standardized uptake values (SUV) in the entire coronary tree exceeding 2 standard deviations above the background SUV. Coefficients of repeatability between the same observer (intraobserver repeatability), between 2 observers (interobserver repeatability) and coefficient of reproducibility between 2 scans (interscan reproducibility), were determined at vessel and patient level.ResultsIn 19 patients, CMA was assessed twice in 43 coronary vessels on two PET/CT scans performed 12 ± 5 days apart. There was excellent intraclass correlation for intraobserver and interobserver repeatability as well as interscan reproducibility (all ≥ 0.991). There was 100% intraobserver, interobserver and interscan agreement for the presence (CMA > 0) or absence (CMA = 0) of coronary18F-NaF uptake. Mean CMA was 3.12 ± 0.62 with coefficients of repeatability of ≤ 10% for all measures: intraobserver 0.24 and 0.22, interobserver 0.30 and 0.29 and interscan 0.33 and 0.32 at a per-vessel and per-patient level, respectively.ConclusionsCMA is a repeatable and reproducible global measure of coronary atherosclerotic activity.

Published
2022

40. Machine Learning with 18F-Sodium Fluoride PET and Quantitative Plaque Analysis on CT Angiography for the Future Risk of Myocardial Infarction

Author

Kwiecinski, Jacek, Tzolos, Evangelos, Meah, Mohammed N, Cadet, Sebastien, Adamson, Philip D, Grodecki, Kajetan, Joshi, Nikhil V, Moss, Alastair J, Williams, Michelle C, van Beek, Edwin JR, Berman, Daniel S, Newby, David E, Dey, Damini, Dweck, Marc R, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Heart Disease - Coronary Heart Disease, Biomedical Imaging, Atherosclerosis, Cardiovascular, Clinical Research, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Positron Emission Tomography Computed Tomography, myocardial infarction, CT, F-18-NaF PET, quantitative plaque analysis, machine-learning, 18F-NaF PET, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

Coronary 18F-sodium fluoride (18F-NaF) PET and CT angiography-based quantitative plaque analysis have shown promise in refining risk stratification in patients with coronary artery disease. We combined both of these novel imaging approaches to develop an optimal machine-learning model for the future risk of myocardial infarction in patients with stable coronary disease. Methods: Patients with known coronary artery disease underwent coronary 18F-NaF PET and CT angiography on a hybrid PET/CT scanner. Machine-learning by extreme gradient boosting was trained using clinical data, CT quantitative plaque analysis, measures and 18F-NaF PET, and it was tested using repeated 10-fold hold-out testing. Results: Among 293 study participants (65 ± 9 y; 84% male), 22 subjects experienced a myocardial infarction over the 53 (40-59) months of follow-up. On univariable receiver-operator-curve analysis, only 18F-NaF coronary uptake emerged as a predictor of myocardial infarction (c-statistic 0.76, 95% CI 0.68-0.83). When incorporated into machine-learning models, clinical characteristics showed limited predictive performance (c-statistic 0.64, 95% CI 0.53-0.76) and were outperformed by a quantitative plaque analysis-based machine-learning model (c-statistic 0.72, 95% CI 0.60-0.84). After inclusion of all available data (clinical, quantitative plaque and 18F-NaF PET), we achieved a substantial improvement (P = 0.008 versus 18F-NaF PET alone) in the model performance (c-statistic 0.85, 95% CI 0.79-0.91). Conclusion: Both 18F-NaF uptake and quantitative plaque analysis measures are additive and strong predictors of outcome in patients with established coronary artery disease. Optimal risk stratification can be achieved by combining clinical data with these approaches in a machine-learning model.

Published
2022

41. Machine Learning for Myocardial Infarction Compared With Guideline-Recommended Diagnostic Pathways

Author

Boeddinghaus, Jasper, Doudesis, Dimitrios, Lopez-Ayala, Pedro, Lee, Kuan Ken, Koechlin, Luca, Wildi, Karin, Nestelberger, Thomas, Borer, Raphael, Miró, Òscar, Martin-Sanchez, F. Javier, Strebel, Ivo, Rubini Giménez, Maria, Keller, Dagmar I., Christ, Michael, Bularga, Anda, Li, Ziwen, Ferry, Amy V., Tuck, Chris, Anand, Atul, Gray, Alasdair, Mills, Nicholas L., Mueller, Christian, Richards, A. Mark, Pemberton, Chris, Troughton, Richard W., Aldous, Sally J., Brown, Anthony F.T., Dalton, Emily, Hammett, Chris, Hawkins, Tracey, O’Kane, Shanen, Parke, Kate, Ryan, Kimberley, Schluter, Jessica, Barker, Stephanie, Blades, Jennifer, Chapman, Andrew R., Fujisawa, Takeshi, Kimenai, Dorien M., McDermott, Michael, Newby, David E., Schulberg, Stacey D., Shah, Anoop S.V., Sorbie, Andrew, Soutar, Grace, Strachan, Fiona E., Taggart, Caelan, Vicencio, Daniel Perez, Wang, Yiqing, Wereski, Ryan, Williams, Kelly, Weir, Christopher J., Berry, Colin, Reid, Alan, Maguire, Donogh, Collinson, Paul O., Sandoval, Yader, Smith, Stephen W., Wussler, Desiree, Muench-Gerber, Tamar, Glaeser, Jonas, Spagnuolo, Carlos, Huré, Gabrielle, Gehrke, Juliane, Puelacher, Christian, Gualandro, Danielle M., Shrestha, Samyut, Kawecki, Damian, Morawiec, Beata, Muzyk, Piotr, Buergler, Franz, Buser, Andreas, Rentsch, Katharina, Twerenbold, Raphael, López, Beatriz, Martinez-Nadal, Gemma, Adrada, Esther Rodriguez, Parenica, Jiri, and von Eckardstein, Arnold

Published
2024
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42. PAR4 Antagonism in Patients With Coronary Artery Disease Receiving Antiplatelet Therapies

Author

Nash, Jennifer, Meah, Mohammed N., Whittington, Beth, Debono, Samuel, Raftis, Jennifer, Miller, Mark R., Sorbie, Andrew, Mills, Nicholas L., Nespoux, Josselin, Bruce, Lorraine, Duffin, Rodger, Dhaun, Neeraj, Brittan, Mairi, Chao, Longfei, Merali, Samira, Kim, Minji, Wang, Zhaoqing, Zhang, Yue, Jin, Shiqiang, Wang, Beqing, Kozinn, Marc, and Newby, David E.

Published
2024
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47. Effects of oral anticoagulation in people with atrial fibrillation after spontaneous intracranial haemorrhage (COCROACH): prospective, individual participant data meta-analysis of randomised trials

Author

Klug, Didier, Casolla, Barbara, Puy, Laurent, Coffee, Morgane, Kuchcinski, Grégory, Labreuche, Julien, van Nieuwenhuizen, Koen M., Algra, Ale, van Gelder, Isabelle C., Kappelle, L. Jaap, Rinkel, Gabriel J.E., Schutgens, Roger E.G., Khatri, Pooja, Conwit, Robin, Falcone, Guido, Elm, Jordan, Anderson, Craig S., Song, Lili, Pandian, Jeyaraj, Hart, Robert G., Sharma, Mukul, Aref, Hany, Tarhuni, Wadea, Fabregas, Joan Marti, Diener, Hans-Christoph, Endres, Matthias, Lemmens, Robin, Kwon, Sun U., Lee, Byung-Chul, Ameriso, Sebastian, Milling, Truman J., Kasner, Scott E., Mikulik, Robert, Xavier, Denis, Beer, Ronny, Toni, Danilo, Eckstein, Jens, Seiffge, David, Ferro, Jose M., Tsivgoulis, Georgios, Sharma, Sanjib K., Wei-Liou, Chia, Hohnloser, Stefan H., Katsanos, Aristeidis, Bosch, Jackie, Healey, Jeff, Eikelboom, John, Khaw, Alexander, Gladstone, David, Pikula, Aleksandra, Coutts, Shelagh, Smith, Eric E., Butcher, Ken, Field, Thalia, Gioia, Laura, Stapf, Christian, Halse, Omid, Ringleb, Peter, Enzinger, Christian, Sibon, Igor, Montaner, Joan, Caso, Valeria, Heuschmann, Peter, Lip, Gregory Y.H., Haefeli, Walter, Debette, Stefanie, Dennis, Martin S., Wyller, Torgeir Bruun, Rønning, Ole M., Eilertsen, Helle, Ihle-Hansen, Hege, Sandset, Else Charlotte, Pennlert, Johanna, Glader, Eva-Lotta, Kruuse, Christina, Wester, Per, Carlsson, Maria, Forfang, Elisabeth, Al-Shahi Salman, Rustam, Stephen, Jacqueline, Tierney, Jayne F, Lewis, Steff C, Newby, David E, Parry-Jones, Adrian R, White, Philip M, Connolly, Stuart J, Benavente, Oscar R, Dowlatshahi, Dar, Cordonnier, Charlotte, Viscoli, Catherine M, Sheth, Kevin N, Kamel, Hooman, Veltkamp, Roland, Larsen, Kristin T, Hofmeijer, Jeannette, Kerkhoff, Henk, Schreuder, Floris H B M, Shoamanesh, Ashkan, Klijn, Catharina J M, and van der Worp, H Bart

Published
2023
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49. Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19

Author

Ahern, David J, Ai, Zhichao, Ainsworth, Mark, Allan, Chris, Allcock, Alice, Angus, Brian, Ansari, M Azim, Arancibia-Cárcamo, Carolina, Aschenbrenner, Dominik, Attar, Moustafa, Baillie, J Kenneth, Barnes, Eleanor, Bashford-Rogers, Rachael, Bashyal, Archana, Beer, Sally, Berridge, Georgina, Beveridge, Amy, Bibi, Sagida, Bicanic, Tihana, Blackwell, Luke, Bowness, Paul, Brent, Andrew, Brown, Andrew, Broxholme, John, Buck, David, Burnham, Katie, Byrne, Helen, Camara, Susana, Ferreira, Ivan Candido, Charles, Philip, Chen, Wentao, Chen, Yi-Ling, Chong, Amanda, Clutterbuck, Elizabeth, Coles, Mark, Conlon, Christopher, Cornall, Richard, Cribbs, Adam, Curion, Fabiola, Davenport, Emma, Davidson, Neil, Davis, Simon, Dendrou, Calliope, Dequaire, Julie, Dib, Lea, Docker, James, Dold, Christina, Dong, Tao, Downes, Damien, Drakesmith, Hal, Dunachie, Susanna, Duncan, David, Eijsbouts, Chris, Esnouf, Robert, Espinosa, Alexis, Etherington, Rachel, Fairfax, Benjamin, Fairhead, Rory, Fang, Hai, Fassih, Shayan, Felle, Sally, Fernandez Mendoza, Maria, Ferreira, Ricardo, Fischer, Roman, Foord, Thomas, Forrow, Aden, Frater, John, Fries, Anastasia, Gallardo Sanchez, Veronica, Garner, Lucy, Geeves, Clementine, Georgiou, Dominique, Godfrey, Leila, Golubchik, Tanya, Gomez Vazquez, Maria, Green, Angie, Harper, Hong, Harrington, Heather, Heilig, Raphael, Hester, Svenja, Hill, Jennifer, Hinds, Charles, Hird, Clare, Ho, Ling-Pei, Hoekzema, Renee, Hollis, Benjamin, Hughes, Jim, Hutton, Paula, Jackson-Wood, Matthew, Jainarayanan, Ashwin, James-Bott, Anna, Jansen, Kathrin, Jeffery, Katie, Jones, Elizabeth, Jostins, Luke, Kerr, Georgina, Kim, David, Klenerman, Paul, Knight, Julian, Kumar, Vinod, Kumar Sharma, Piyush, Kurupati, Prathiba, Kwok, Andrew, Lee, Angela, Linder, Aline, Lockett, Teresa, Lonie, Lorne, Lopopolo, Maria, Lukoseviciute, Martyna, Luo, Jian, Marinou, Spyridoula, Marsden, Brian, Martinez, Jose, Matthews, Philippa, Mazurczyk, Michalina, McGowan, Simon, McKechnie, Stuart, Mead, Adam, Mentzer, Alexander, Mi, Yuxin, Monaco, Claudia, Montadon, Ruddy, Napolitani, Giorgio, Nassiri, Isar, Novak, Alex, O'Brien, Darragh, O'Connor, Daniel, O'Donnell, Denise, Ogg, Graham, Overend, Lauren, Park, Inhye, Pavord, Ian, Peng, Yanchun, Penkava, Frank, Pereira Pinho, Mariana, Perez, Elena, Pollard, Andrew, Powrie, Fiona, Psaila, Bethan, Quan, T Phuong, Repapi, Emmanouela, Revale, Santiago, Silva-Reyes, Laura, Richard, Jean-Baptiste, Rich-Griffin, Charlotte, Ritter, Thomas, Rollier, Christine, Rowland, Matthew, Ruehle, Fabian, Salio, Mariolina, Sansom, Stephen Nicholas, Sanches Peres, Raphael, Santos Delgado, Alberto, Sauka-Spengler, Tatjana, Schwessinger, Ron, Scozzafava, Giuseppe, Screaton, Gavin, Seigal, Anna, Semple, Malcolm, Sergeant, Martin, Simoglou Karali, Christina, Sims, David, Skelly, Donal, Slawinski, Hubert, Sobrinodiaz, Alberto, Sousos, Nikolaos, Stafford, Lizzie, Stockdale, Lisa, Strickland, Marie, Sumray, Otto, Sun, Bo, Taylor, Chelsea, Taylor, Stephen, Taylor, Adan, Thongjuea, Supat, Thraves, Hannah, Todd, John, Tomic, Adriana, Tong, Orion, Trebes, Amy, Trzupek, Dominik, Tucci, Felicia Anna, Turtle, Lance, Udalova, Irina, Uhlig, Holm, van Grinsven, Erinke, Vendrell, Iolanda, Verheul, Marije, Voda, Alexandru, Wang, Guanlin, Wang, Lihui, Wang, Dapeng, Watkinson, Peter, Watson, Robert, Weinberger, Michael, Whalley, Justin, Witty, Lorna, Wray, Katherine, Xue, Luzheng, Yuen Yeung, Hing, Yin, Zixi, Young, Rebecca, Youngs, Jonathan, Zhang, Ping, Zurke, Yasemin-Xiomara, Banning, Adrian, Antonopoulos, Alexios, Bajaj, Amrita, Kelion, Andrew, Deshpande, Aparna, Kardos, Attila, Hudson, Benjamin, Koo, Bon-Kwon, Shirodaria, Cheerag, Xie, Cheng, Kotanidis, Christos, Mahon, Ciara, Berry, Colin, Adlam, David, Newby, David, Connolly, Derek, Scaletta, Diane, Alexander, Donna, Nicol, Ed, McAlindon, Elisa, Oikonomou, Evangelos, Pugliese, Francesca, Pontone, Gianluca, Benedetti, Giulia, He, Guo-Wei, West, Henry, Kondo, Hidekazu, Benedek, Imre, Das, Intrajeet, Deanfield, John, Graby, John, Greenwood, John, Rodrigues, Jonathan, Ge, Junbo, Channon, Keith, Fabritz, Larissa, Fan, Li-Juan, Kingham, Lucy, Guglielmo, Marco, Lyasheva, Maria, Schmitt, Matthias, Beer, Meinrad, Anderson, Michelle, Desai, Milind, Marwan, Mohamed, Takahashi, Naohiko, Mehta, Nehal, Dai, Neng, Screaton, Nicholas, Sabharwal, Nikant, Maurovich-Horvat, Pál, Rao, Praveen, Kotronias, Rafail, Kharbanda, Rajesh, Preston, Rebecca, Wood, Richard, Blankstein, Ron, Rajani, Ronak, Mirsadraee, Saeed, Munir, Shahzad, Thomas, Sheena, Neubauer, Stefan, Klömpken, Steffen, Petersen, Steffen, Achenbach, Stephan, Anthony, Susan, Mak, Sze, Mittal, Tarun, Benedek, Theodora, Sharma, Vinoda, Lin, Wen-Hua, Kotanidis, Christos P, Rodrigues, Jonathan C L, O’Connor, Daniel, Siddique, Muhammad, Lockstone, Helen, Oikonomou, Evangelos K, Badi, Ileana, Lumley, Sheila F, Constantinides, Bede, Sanderson, Nicholas, Rodger, Gillian, Chau, Kevin K, Lodge, Archie, Tsakok, Maria, Gleeson, Fergus, Indrajeet, Das, Hudson, Benjamin J, Srivastava, Vivek, Farid, Shakil, Krasopoulos, George, Sayeed, Rana, Newby, David E, Channon, Keith M, and Antoniades, Charalambos

Published
2022
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