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1. Competing for pixels: a self-play algorithm for weakly-supervised segmentation

Author

Saeed, Shaheer U., Huang, Shiqi, Ramalhinho, João, Gayo, Iani J. M. B., Montaña-Brown, Nina, Bonmati, Ester, Pereira, Stephen P., Davidson, Brian, Barratt, Dean C., Clarkson, Matthew J., and Hu, Yipeng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Weakly-supervised segmentation (WSS) methods, reliant on image-level labels indicating object presence, lack explicit correspondence between labels and regions of interest (ROIs), posing a significant challenge. Despite this, WSS methods have attracted attention due to their much lower annotation costs compared to fully-supervised segmentation. Leveraging reinforcement learning (RL) self-play, we propose a novel WSS method that gamifies image segmentation of a ROI. We formulate segmentation as a competition between two agents that compete to select ROI-containing patches until exhaustion of all such patches. The score at each time-step, used to compute the reward for agent training, represents likelihood of object presence within the selection, determined by an object presence detector pre-trained using only image-level binary classification labels of object presence. Additionally, we propose a game termination condition that can be called by either side upon exhaustion of all ROI-containing patches, followed by the selection of a final patch from each. Upon termination, the agent is incentivised if ROI-containing patches are exhausted or disincentivised if an ROI-containing patch is found by the competitor. This competitive setup ensures minimisation of over- or under-segmentation, a common problem with WSS methods. Extensive experimentation across four datasets demonstrates significant performance improvements over recent state-of-the-art methods. Code: https://github.com/s-sd/spurl/tree/main/wss

Published
2024

2. Precursor Epithelial Subtypes of Adenocarcinoma Arising from Intraductal Papillary Mucinous Neoplasms (A-IPMN): Clinicopathological Features, Recurrence and Response to Adjuvant Chemotherapy

Author

Lucocq, James, Haugk, Beate, Parkinson, Daniel, Darne, Antony, Joseph, Nejo, Hawkyard, Jake, White, Steve, Mownah, Omar, Menon, Krishna, Furukawa, Takaki, Inoue, Yosuke, Hirose, Yuki, Sasahira, Naoki, Mittal, Anubhav, Samra, Jas, Sheen, Amy, Feretis, Michael, Balakrishnan, Anita, Ceresa, Carlo, Davidson, Brian, Pande, Rupaly, Dasari, Bobby V. M., Tanno, Lulu, Karavias, Dimitrios, Helliwell, Jack, Young, Alistair, Nunes, Quentin, Urbonas, Tomas, Silva, Michael, Gordon-Weeks, Alex, Barrie, Jenifer, Gomez, Dhanny, van Laarhoven, Stijn, Nawara, Hossam, Doyle, Joseph, Bhogal, Ricky, Harrison, Ewen, Roalso, Marcus, Ciprani, Deborah, Aroori, Somaiah, Ratnayake, Bathiya, Koea, Jonathan, Capurso, Gabriele, Bellotti, Ruben, Stättner, Stefan, Alsaoudi, Tareq, Bhardwaj, Neil, Jeffery, Fraser, Connor, Saxon, Cameron, Andrew, Jamieson, Nigel, Roberts, Keith, Soreide, Kjetil, Gill, Anthony J., and Pandanaboyana, Sanjay

Published
2024
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3. ASO Visual Abstract: Precursor Epithelial Subtypes of Adenocarcinoma Arising from Intraductal Papillary Mucinous Neoplasms (A-IPMN)—Clinicopathological Features, Recurrence and Response to Adjuvant Chemotherapy

Author

Lucocq, James, Haugk, Beate, Parkinson, Daniel, Darne, Antony, Joseph, Nejo, Hawkyard, Jake, White, Steve, Mownah, Omar, Menon, Krishna, Furukawa, Takaki, Inoue, Yosuke, Hirose, Yuki, Sasahira, Naoki, Mittal, Anubhav, Samra, Jas, Sheen, Amy, Feretis, Michael, Balakrishnan, Anita, Ceresa, Carlo, Davidson, Brian, Pande, Rupaly, Dasari, Bobby V. M., Tanno, Lulu, Karavias, Dimitrios, Helliwell, Jack, Young, Alistair, Nunes, Quentin, Urbonas, Tomas, Silva, Michael, Gordon-Weeks, Alex, Barrie, Jenifer, Gomez, Dhanny, van Laarhoven, Stijn, Nawara, Hossam, Doyle, Joseph, Bhogal, Ricky, Harrison, Ewen, Roalso, Marcus, Ciprani, Deborah, Aroori, Somaiah, Ratnayake, Bathiya, Koea, Jonathan, Capurso, Gabriele, Bellotti, Ruben, Stättner, Stefan, Alsaoudi, Tareq, Bhardwaj, Neil, Jeffery, Fraser, Connor, Saxon, Cameron, Andrew, Jamieson, Nigel, Roberts, Keith, Soreide, Kjetil, Gill, Anthony J., and Pandanaboyana, Sanjay

Published
2024
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4. Five-year recurrence/survival after pancreatoduodenectomy for pancreatic adenocarcinoma: does pre-existing diabetes matter? Results from the Recurrence After Whipple's (RAW) study

Author

Lead Unit, Aroori, Somaiah, Labib, Peter L., Russell, Thomas B., Streeter, Adam, Denson, Jemimah, Puckett, Mark, Zhou, Shang-Ming, Browning, Matthew, Collaborating Units, Ausania, Fabio, Gonzalez-Abos, Carolina, Pando, Elizabeth, Fernandes, Nair, Moller, Elsa G., Taboada, Cristina D., Roberts, Keith, Pande, Rupaly, Alfarah, Jameel, Kausar, Ambareen, Bandyopadhyay, Samik, Abdelrahim, Ahmed, Khan, Ayesha, Mavroeidis, Vasileios K., Jordan, Caitlin, Rees, Jonathan R.E., Marangoni, Gabriele, Blege, Harry, Thomasset, Sarah, Cambridge, William, White, Olga, Frampton, Adam, Blacker, Sarah, Blackburn, Jessie, Sweeney, Casie, Lykoudis, Pavlos, Field, Daniel, Gouda, Mohammed, Maglione, Manuel, Bellotti, Ruben, Alhaboob, Nassir, Hamid, Hytham K.S., Bari, Hassaan, Ahmed, Hassan, Smith, Andrew, Moriarty, Catherine, White, Louise, Priestley, Mark, Bode, Kerry, Sharp, Judith, Wragg, Rosie, Jackson, Beverley, Craven, Samuel, Spalding, Duncan, Fehervari, Matyas, Pai, Madhava, Alghazawi, Laith, Onifade, Anjola, Srinivasan, Parthi, Ribaud, Julliette, Nair, Ashitha, Mariathasan, Michael, Grayson, Niamh, Davidson, Brian, Pericleous, Stephanos, Patel, Krishna, Shaw, Conrad, Morare, Nolitha, Zaban, Mohamad K., Bhogal, Ricky, Doyle, Joseph, Croagh, Daniel, Rajagopalan, Ashray, Dominguez, Ismael, Guerrero, Alan, Moguel, Andre, Chan, Carlos, Thakkar, Rohan, Jones, Michael, Buckley, Edward, Akter, Nasreen, Treherne, Kyle, Gomez, Dhanny, Gordon, Gregory, Silva, Michael, Hughes, Daniel, Urbonas, Tomas, Lapolla, Pierfrancesco, Mingoli, Andrea, Brachini, Gioia, Caronna, Roberto, Chirletti, Piero, Porcu, Alberto, Perra, Teresa, Shah, Nehal S., Abd Kahar, Nurul N., Hall, Thomas, Nadeem, Nabeegh, Hamady, Zaed, Karar, Shoura, Arshad, Ali, Al-Sarrieh, Bilal, Yarwood, Adam, Hammoda, Mohammed, Serrablo, Alejandro, Artigas, Maria, Paterna-López, Sandra, Roberts, Keith J., Thomasset, Sarah C., Frampton, Adam E., Smith, Andrew M., Davidson, Brian R., Bhogal, Ricky H., Silva, Michael A., and Hamady, Zaed Z.R.

Published
2024
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5. Active learning using adaptable task-based prioritisation

Author

Saeed, Shaheer U., Ramalhinho, João, Pinnock, Mark, Shen, Ziyi, Fu, Yunguan, Montaña-Brown, Nina, Bonmati, Ester, Barratt, Dean C., Pereira, Stephen P., Davidson, Brian, Clarkson, Matthew J., and Hu, Yipeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney, unseen in training, using between approximately 40\% to 60\% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6\% and 10.2\% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.

Published
2022

7. OSI (Office of Special Investigations) at war

Author

Davidson, Brian, TSgt

Subjects
COUNTERTERRORISM - United States, ROLES AND MISSIONS - Air Force - United States, EXPLOSIVE ORDNANCE DISPOSAL, OPERATION - Enduring Freedom - Intelligence Operations, WEAPONS - Disposal
Abstract

illus

Published
2005

8. Predictors of long-term survival after resection of adenocarcinoma arising from intraductal papillary mucinous neoplasm and derivation of a prognostic model: An international multicenter study (ADENO-IPMN study)

Author

Lucocq, James, Joseph, Nejo, Hawkyard, Jake, Haugk, Beate, White, Steve, Lye, Jonathan, Parkinson, Daniel, Mownah, Omar, Menon, Krishna, Furukawa, Takaki, Hirose, Yuki, Sasahira, Naoki, Inoue, Yosuke, Mittal, Anubhav, Samra, Jas, Sheen, Amy, Feretis, Michael, Balakrishnan, Anita, Ceresa, Carlo, Davidson, Brian, Pande, Rupaly, Dasari, Bobby, Roberts, Keith, Tanno, Lulu, Karavias, Dimitrios, Helliwell, Jack, Young, Alistair, Marks, Kate, Nunes, Quentin, Urbonas, Tomas, Silva, Michael, Gordon-Weeks, Alex, Barrie, Jenifer, Gomez, Dhanny, van Laarhoven, Stijn, Nawara, Hossam, Doyle, Joseph, Bhogal, Ricky, Harrison, Ewen, Roalso, Marcus, Ciprani, Debora, Aroori, Somaiah, Ratnayake, Bathiya, Koea, Jonathan, Capurso, Gabriele, Stättner, Stefan, Bellotti, Ruben, Alsaoudi, Tareq, Bhardwaj, Neil, Rajesh, Srujan, Jeffery, Fraser, Connor, Saxon, Cameron, Andrew, Jamieson, Nigel, Gill, Anthony, Soreide, Kjetil, and Pandanaboyana, Sanjay

Published
2024
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9. Risk of Recurrence After Surgical Resection for Adenocarcinoma Arising From Intraductal Papillary Mucinous Neoplasia (IPMN) With Patterns of Distribution and Treatment: An International, Multicenter, Observational Study (ADENO-IPMN Study)

Author

Lucocq, James, Hawkyard, Jake, Robertson, Francis P., Haugk, Beate, Lye, Jonathan, Parkinson, Daniel, White, Steve, Mownah, Omar, Zen, Yoh, Menon, Krishna, Furukawa, Takaaki, Inoue, Yosuke, Hirose, Yuki, Sasahira, Naoki, Feretis, Michael, Balakrishnan, Anita, Zelga, Piotr, Ceresa, Carlo, Davidson, Brian, Pande, Rupaly, Dasari, Bobby, Tanno, Lulu, Karavias, Dimitrios, Helliwell, Jack, Young, Alistair, Nunes, Quentin, Urbonas, Tomas, Silva, Michael, Gordon-Weeks, Alex, Barrie, Jenifer, Gomez, Dhanny, van Laarhoven, Stijn, Doyle, Joseph, Bhogal, Ricky, Harrison, Ewen, Roalso, Marcus, Ciprani, Debora, Aroori, Somaiah, Ratnayake, Bathiya, Koea, Jonathan, Capurso, Gabriele, Bellotti, Ruben, Stättner, Stefan, Alsaoudi, Tareq, Bhardwaj, Neil, Jeffery, Fraser, Connor, Saxon, Cameron, Andrew, Jamieson, Nigel, Sheen, Amy, Mittal, Anubhav, Samra, Jas, Gill, Anthony, Roberts, Keith, Soreide, Kjetil, and Pandanaboyana, Sanjay

Published
2024
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10. Invasive intraductal oncocytic papillary neoplasms (IOPN) and adenocarcimoma arising from intraductal papillary mucinous neoplasms (A-IPMN) of the pancreas: comparative analysis of clinicopathological features, patterns of recurrence and survival: a multicentre study

Author

Lucocq, James, Haugk, Beate, Joseph, Nejo, Hawkyard, Jake, White, Steve, Mownah, Omar, Menon, Krishna, Furukawa, Takaki, Inoue, Yosuke, Hirose, Yuki, Sasahira, Naoki, Mittal, Anubhav, Samra, Jas, Sheen, Amy, Feretis, Michael, Balakrishnan, Anita, Ceresa, Carlo, Davidson, Brian, Pande, Rupaly, Dasari, Bobby V.M., Tanno, Lulu, Karavias, Dimitrios, Helliwell, Jack, Young, Alistair, Nunes, Quentin, Urbonas, Tomas, Silva, Michael, Gordon-Weeks, Alex, Barrie, Jenifer, Gomez, Dhanny, van Laarhoven, Stijn, Nawara, Hossam, Doyle, Joseph, Bhogal, Ricky, Harrison, Ewen, Roalso, Marcus, Zaharia, Claudia, Ciprani, Debora, Aroori, Somaiah, Ratnayake, Bathiya, Koea, Jonathan, Capurso, Gabriele, Bellotti, Ruben, Stättner, Stefan, Alsaoudi, Tareq, Bhardwaj, Neil, Jeffery, Fraser, Connor, Saxon, Cameron, Andrew, Jamieson, Nigel, Roberts, Keith, Soreide, Kjetil, Gill, Anthony J., and Pandanaboyana, Sanjay

Published
2024
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11. Benefits and Harms of Interventions to Improve Anxiety, Depression, and Other Mental Health Outcomes for Autistic People: A Systematic Review and Network Meta-Analysis of Randomised Controlled Trials

Author

Linden, Audrey, Best, Lawrence, Elise, Freya, Roberts, Danielle, Branagan, Aoife, Tay, Yong Boon Ernest, Crane, Laura, Cusack, James, Davidson, Brian, Davidson, Ian, Hearst, Caroline, Mandy, William, Rai, Dheeraj, Smith, Edward, and Gurusamy, Kurinchi

Abstract

Mental health difficulties are prevalent in autistic people with [approximately]14%-50% having experienced depression and [approximately]40%-80% having experienced anxiety disorders. Identifying interventions that improve autistic people's mental health is a top priority. However, at present, there is no high-quality network meta-analysis of benefits and harms of different interventions. We conducted a systematic review and network meta-analysis of randomised controlled trials, searching MEDLINE, EMBASE, other databases, and trial registers until 17 October 2020. We included randomised controlled trials reporting anxiety or depression in a suitable format. We calculated effect estimates and 95% credible intervals using Bayesian network meta-analysis. Our search identified 13,794 reports, of which 71 randomised controlled trials (3630 participants) were eligible for inclusion. All trials had high risk of bias. The follow-up period ranged from 1 to 24 months. Evidence indicates uncertainty about the effects of different interventions, with more high-quality evidence needed. Available evidence suggests that some forms of cognitive behavioural therapy may decrease anxiety and depression scores in autistic children and adults; mindfulness therapy may decrease anxiety and depression scores in autistic adults with previous mental health conditions; and behavioural interventions may provide some benefit for depression in autistic children. We recommend that autistic people are given access to mental health interventions available to non-autistic people, following principles of person-centred care.

Published
2023
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12. Long-term Outcomes following Resection of Adenocarcinoma Arising from Intraductal Papillary Mucinous Neoplasm (A-IPMN) versus Pancreatic Ductal Adenocarcinoma (PDAC): A Propensity-score Matched Analysis

Author

Lucocq, James, Halle-Smith, James, Haugk, Beate, Joseph, Nejo, Hawkyard, Jake, Lye, Jonathan, Parkinson, Daniel, White, Steve, Mownah, Omar, Zen, Yoh, Menon, Krishna, Furukawa, Takaki, Inoue, Yosuke, Hirose, Yuki, Sasahira, Naoki, Mittal, Anubhav, Samra, Jas, Sheen, Amy, Feretis, Michael, Balakrishnan, Anita, Ceresa, Carlo, Davidson, Brian, Pande, Rupaly, Dasari, Bobby V.M., Tanno, Lulu, Karavias, Dimitrios, Helliwell, Jack, Young, Alistair, Marks, Kate, Nunes, Quentin, Urbonas, Tomas, Silva, Michael, Gordon-Weeks, Alex, Barrie, Jenifer, Gomez, Dhanny, van Laarhoven, Stijn, Nawara, Hossam, Doyle, Joseph, Bhogal, Ricky, Harrison, Ewen, Roalso, Marcus, Ciprani, Debora, Aroori, Somaiah, Ratnayake, Bathiya, Koea, Jonathan, Capurso, Gabriele, Bellotti, Ruben, Stättner, Stefan, Alsaoudi, Tareq, Bhardwaj, Neil, Rajesh, Srujan, Jeffery, Fraser, Connor, Saxon, Cameron, Andrew, Jamieson, Nigel, Soreide, Kjetil, Gill, Anthony J., Roberts, Keith, and Pandanaboyana, Sanjay

Published
2024
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13. Serious complications of pancreatoduodenectomy correlate with lower rates of adjuvant chemotherapy: Results from the recurrence after Whipple's (RAW) study

Author

Labib, Peter L., Russell, Thomas B., Streeter, Adam, Denson, Jemimah, Puckett, Mark, Zhou, Shang-Ming, Browning, Matthew, Aroori, Somaiah, Ausania, Fabio, Pando, Elizabeth, Roberts, Keith, Kausar, Ambareen, Mavroeidis, Vasileios K., Marangoni, Gabriele, Thomasset, Sarah, Frampton, Adam, Lykoudis, Pavlos, Maglione, Manuel, Alhaboob, Nassir, Bari, Hassaan, Smith, Andrew, Spalding, Duncan, Srinivasan, Parthi, Davidson, Brian, Bhogal, Ricky, Croagh, Daniel, Dominguez, Ismael, Thakkar, Rohan, Gomez, Dhanny, Silva, Michael, Lapolla, Pierfrancesco, Mingoli, Andrea, Porcu, Alberto, Shah, Nehal Sureshkumar, Hamady, Zaed, Al-Sarrieh, Bilal, Serrablo, Alejandro, Gonzalez-Abos, Carolina, Fernandes, Nair, Moller, Elsa Garcia, Taboada, Cristina Dopazo, Pande, Rupaly, Alfarah, Jameel, Bandyopadhyay, Samik, Abdelrahim, Ahmed, Khan, Ayesha, Jordan, Caitlin, Rees, Jonathan R.E., Blege, Harry, Cambridge, William, White, Olga, Blacker, Sarah, Blackburn, Jessie, Sweeney, Casie, Field, Daniel, Gouda, Mohammed, Bellotti, Ruben, Hamid, Hytham K.S., Ahmed, Hassan, Moriarty, Catherine, White, Louise, Priestley, Mark, Bode, Kerry, Sharp, Judith, Wragg, Rosie, Jackson, Beverley, Craven, Samuel, Fehervari, Matyas, Pai, Madhava, Alghazawi, Laith, Onifade, Anjola, Ribaud, Julliette, Nair, Ashitha, Mariathasan, Michael, Grayson, Niamh, Pericleous, Stephanos, Patel, Krishna, Shaw, Conrad, Morare, Nolitha, Zaban, Mohamad Khish, Doyle, Joseph, Guerrero, Alan, Moguel, Andre, Chan, Carlos, Jones, Michael, Buckley, Edward, Akter, Nasreen, Treherne, Kyle, Gordon, Gregory, Hughes, Daniel, Urbonas, Tomas, Brachini, Gioia, Caronna, Roberto, Chirletti, Piero, Perra, Teresa, Abd Kahar, Nurul Nadhirah, Hall, Thomas, Nadeem, Nabeegh, Karar, Shoura, Arshad, Ali, Yarwood, Adam, Hammoda, Mohammed, Artigas, Maria, Paterna-López, Sandra, Roberts, Keith J., Thomasset, Sarah C., Frampton, Adam E., Smith, Andrew M., Davidson, Brian R., Bhogal, Ricky H., Silva, Michael A., Shah, Nehal S., and Hamady, Zaed Z.R.

Published
2023
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14. Voice-assisted Image Labelling for Endoscopic Ultrasound Classification using Neural Networks

Author

Bonmati, Ester, Hu, Yipeng, Grimwood, Alexander, Johnson, Gavin J., Goodchild, George, Keane, Margaret G., Gurusamy, Kurinchi, Davidson, Brian, Clarkson, Matthew J., Pereira, Stephen P., and Barratt, Dean C.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Ultrasound imaging is a commonly used technology for visualising patient anatomy in real-time during diagnostic and therapeutic procedures. High operator dependency and low reproducibility make ultrasound imaging and interpretation challenging with a steep learning curve. Automatic image classification using deep learning has the potential to overcome some of these challenges by supporting ultrasound training in novices, as well as aiding ultrasound image interpretation in patient with complex pathology for more experienced practitioners. However, the use of deep learning methods requires a large amount of data in order to provide accurate results. Labelling large ultrasound datasets is a challenging task because labels are retrospectively assigned to 2D images without the 3D spatial context available in vivo or that would be inferred while visually tracking structures between frames during the procedure. In this work, we propose a multi-modal convolutional neural network (CNN) architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. We use a CNN composed of two branches, one for voice data and another for image data, which are joined to predict image labels from the spoken names of anatomical landmarks. The network was trained using recorded verbal comments from expert operators. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels. We conclude that the addition of spoken commentaries can increase the performance of ultrasound image classification, and eliminate the burden of manually labelling large EUS datasets necessary for deep learning applications., Comment: Submitted to IEEE TMI

Published
2021
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16. Patterns, timing and predictors of recurrence following pancreaticoduodenectomy for distal cholangiocarcinoma: An international multicentre retrospective cohort study

Author

Browning, Matthew, Mownah, Omar, Gonzalez-Abos, Carolina, Fernandes, Nair, Moller, Elsa Garcia, Taboada, Cristina Dopazo, Pande, Rupaly, Alfarah, Jameel, Bandyopadhyay, Samik, Abdelrahim, Ahmed, Khan, Ayesha, Jordan, Caitlin, Rees, Jonathan RE., Doyle, Joseph, Blege, Harry, Cambridge, William, White, Olga, Blacker, Sarah, Blackburn, Jessie, Sweeney, Casie, Fehervari, Matyas, Pai, Madhava, Alghazawi, Laith, Onifade, Anjola, Field, Daniel, Gouda, Mohammed, Maglione, Manuel, Hamid, Hytham KS., Ribaud, Julliette, Nair, Ashitha, Mariathasan, Michael, Grayson, Niamh, Ahmed, Hassan, Moriarty, Catherine, White, Louise, Priestley, Mark, Bode, Kerry, Sharp, Judith, Wragg, Rosie, Jackson, Beverley, Craven, Samuel, Guerrero, Alan, Moguel, Andre, Chan, Carlos, Jones, Michael, Buckley, Edward, Akter, Nasreen, Treherne, Kyle, Gordon, Gregory, Hughes, Daniel, Urbonas, Tomas, Brachini, Gioia, Caronna, Roberto, Chirletti, Piero, Pericleous, Stephanos, Patel, Krishna, Shaw, Conrad, Morare, Nolitha, Zaban, Mohamad K., Perra, Teresa, Abd Kahar, Nurul Nadhirah, Hall, Thomas, Nadeem, Nabeegh, Karar, Shoura, Arshad, Ali, Yarwood, Adam, Hammoda, Mohammed, Artigas, Maria Consuelo, Paterna-López, Sandra, Labib, Peter LZ., Russell, Thomas B., Denson, Jemimah L., Puckett, Mark A., Ausania, Fabio, Pando, Elizabeth, Roberts, Keith J., Kausar, Ambareen, Mavroeidis, Vasileios K., Bhogal, Ricky H., Marangoni, Gabriele, Thomasset, Sarah C., Frampton, Adam E., Spalding, Duncan R., Lykoudis, Pavlos, Bellotti, Ruben, Alhaboob, Nassir, Srinivasan, Parthi, Bari, Hassaan, Smith, Andrew, Dominguez-Rosado, Ismael, Croagh, Daniel, Thakkar, Rohan G., Gomez, Dhanny, Silva, Michael A., Lapolla, Pierfrancesco, Mingoli, Andrea, Davidson, Brian R., Porcu, Alberto, Shah, Nehal S., Hamady, Zaed Z., Al-Sarireh, Bilal A., Serrablo, Alejandro, and Aroori, Somaiah

Published
2024
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21. Predictors of actual five-year survival and recurrence after pancreatoduodenectomy for ampullary adenocarcinoma: results from an international multicentre retrospective cohort study

Author

Aroori, Somaiah, Labib, Peter L., Russell, Thomas B., Streeter, Adam, Denson, Jemimah, Puckett, Mark, Browning, Matthew G., Ausania, Fabio, González-Abós, Carolina, Pando, Elizabeth, Fernandes, Nair, Moller, Elsa G., Taboada, Cristina D., Roberts, Keith J., Pande, Rupaly, Alfarah, Jameel, Kausar, Ambareen, Bandyopadhyay, Samik, Abdelrahim, Ahmed, Khan, Ayesha, Mavroeidis, Vasileios K., Jordan, Caitlin, Rees, Jonathan R.E., Marangoni, Gabriele, Blege, Collaborator: Harry, Thomasset, Sarah, Cambridge, William, White, Olga, Frampton, Adam, Blacker, Sarah, Blackburn, Jessie, Sweeney, Casie, Lykoudis, Pavlos, Field, Daniel, Gouda, Mohammed, Maglione, Manuel, Bellotti, Ruben, Alhaboob, Nassir, Hamid, Hytham K.S., Bari, Hassaan, Ahmed, Hassan, Smith, Andrew, Moriarty, Catherine, White, Louise, Priestley, Mark, Bode, Kerry, Sharp, Judith, Wragg, Rosie, Jackson, Beverley, Craven, Samuel, Spalding, Duncan, Fehervari, Matyas, Pai, Madhava, Alghazawi, Laith, Onifade, Anjola, Srinivasan, Parthi, Ribaud, Julliette, Nair, Ashitha, Mariathasan, Michael, Grayson, Niamh, Davidson, Brian, Pericleous, Stephanos, Krishna Patel, Shaw, Conrad, Morare, Nolitha, Zaban, Mohamad K., Bhogal, Ricky, Doyle, Joseph, Croagh, Daniel, Dominguez, Ismael, Guerrero, Alan, Moguel, Andre, Chan, Carlos, Thakkar, Rohan, Jones, Michael, Buckley, Edward, Akter, Nasreen, Treherne, Kyle, Gomez, Dhanny, Gordon, Gregory, Silva, Michael, Hughes, Daniel, Urbonas, Tomas, Lapolla, Pierfrancesco, Mingoli, Andrea, Brachini, Gioia, Caronna, Roberto, Chirletti, Piero, Porcu, Alberto, Perra, Teresa, Shah, Nehal S., Abd Kahar, Nurul N., Hall, Thomas, Nadeem, Nabeegh, Hamady, Zaed, Karar, Shoura, Arshad, Ali, Al-Sarrieh, Bilal, Yarwood, Adam, Hammoda, Mohammed, Serrablo, Alejandro, Artigas, Maria, Paterna-López, Sandra, Thomasset, Sarah C., Frampton, Adam E., Smith, Andrew M., Davidson, Brian R., Bhogal, Ricky H., Silva, Michael A., and Hamady, Zaed Z.R.

Published
2023
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25. Tissue CD14+CD8+ T cells reprogrammed by myeloid cells and modulated by LPS

Author

Pallett, Laura J., Swadling, Leo, Diniz, Mariana, Maini, Alexander A., Schwabenland, Marius, Gasull, Adrià Dalmau, Davies, Jessica, Kucykowicz, Stephanie, Skelton, Jessica K., Thomas, Niclas, Schmidt, Nathalie M., Amin, Oliver E., Gill, Upkar S., Stegmann, Kerstin A., Burton, Alice R., Stephenson, Emily, Reynolds, Gary, Whelan, Matt, Sanchez, Jenifer, de Maeyer, Roel, Thakker, Clare, Suveizdyte, Kornelija, Uddin, Imran, Ortega-Prieto, Ana M., Grant, Charlotte, Froghi, Farid, Fusai, Giuseppe, Lens, Sabela, Pérez-del-Pulgar, Sofia, Al-Akkad, Walid, Mazza, Giuseppe, Noursadeghi, Mahdad, Akbar, Arne, Kennedy, Patrick T. F., Davidson, Brian R., Prinz, Marco, Chain, Benjamin M., Haniffa, Muzlifah, Gilroy, Derek W., Dorner, Marcus, Bengsch, Bertram, Schurich, Anna, and Maini, Mala K.

Published
2023
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26. Intraoperative Liver Surface Completion with Graph Convolutional VAE

Author

Foti, Simone, Koo, Bongjin, Dowrick, Thomas, Ramalhinho, Joao, Allam, Moustafa, Davidson, Brian, Stoyanov, Danail, and Clarkson, Matthew J.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure. We introduce a new data augmentation technique that randomly perturbs shapes in their frequency domain to compensate the limited size of our dataset. The core of our method is a variational autoencoder (VAE) that is trained to learn a latent space for complete shapes of the liver. At inference time, the generative part of the model is embedded in an optimisation procedure where the latent representation is iteratively updated to generate a model that matches the intraoperative partial point cloud. The effect of this optimisation is a progressive non-rigid deformation of the initially generated shape. Our method is qualitatively evaluated on real data and quantitatively evaluated on synthetic data. We compared with a state-of-the-art rigid registration algorithm, that our method outperformed in visible areas.

Published
2020
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29. Luftwaffe counts its pfennigs

Author

Davidson, Brian

Subjects
PROCUREMENT - Air Force - Germany, AIR FORCE - Germany
Abstract

por chart

Published
1995

31. More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation

Author

Fu, Yunguan, Robu, Maria R., Koo, Bongjin, Schneider, Crispin, van Laarhoven, Stijn, Stoyanov, Danail, Davidson, Brian, Clarkson, Matthew J., and Hu, Yipeng

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. With a laparoscopic liver image segmentation application, we investigate the performance impact by altering the quantities of labelled and unlabelled training data, using a semi-supervised segmentation algorithm based on the mean teacher learning paradigm. We first report a significantly higher segmentation accuracy, compared with supervised learning. Interestingly, this comparison reveals that the training strategy adopted in the semi-supervised algorithm is also responsible for this observed improvement, in addition to the added unlabelled data. We then compare different combinations of labelled and unlabelled data set sizes for training semi-supervised segmentation networks, to provide a quantitative example of the practically useful trade-off between the two data planning strategies in this surgical guidance application., Comment: Accepted to MICCAI MIL3ID 2019

Published
2019

32. Whole-Sample Mapping of Cancerous and Benign Tissue Properties

Author

Neary-Zajiczek, Lydia, Essmann, Clara, Clancy, Neil, Haider, Aiman, Miranda, Elena, Shaw, Michael, Gander, Amir, Davidson, Brian, Fernandez-Reyes, Delmiro, Pawar, Vijay, and Stoyanov, Danail

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Structural and mechanical differences between cancerous and healthy tissue give rise to variations in macroscopic properties such as visual appearance and elastic modulus that show promise as signatures for early cancer detection. Atomic force microscopy (AFM) has been used to measure significant differences in stiffness between cancerous and healthy cells owing to its high force sensitivity and spatial resolution, however due to absorption and scattering of light, it is often challenging to accurately locate where AFM measurements have been made on a bulk tissue sample. In this paper we describe an image registration method that localizes AFM elastic stiffness measurements with high-resolution images of haematoxylin and eosin (H\&E)-stained tissue to within 1.5 microns. Color RGB images are segmented into three structure types (lumen, cells and stroma) by a neural network classifier trained on ground-truth pixel data obtained through k-means clustering in HSV color space. Using the localized stiffness maps and corresponding structural information, a whole-sample stiffness map is generated with a region matching and interpolation algorithm that associates similar structures with measured stiffness values. We present results showing significant differences in stiffness between healthy and cancerous liver tissue and discuss potential applications of this technique., Comment: Accepted at MICCAI2019

Published
2019

33. Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation

Author

Pfeiffer, Micha, Funke, Isabel, Robu, Maria R., Bodenstedt, Sebastian, Strenger, Leon, Engelhardt, Sandy, Roß, Tobias, Clarkson, Matthew J., Gurusamy, Kurinchi, Davidson, Brian R., Maier-Hein, Lena, Riediger, Carina, Welsch, Thilo, Weitz, Jürgen, and Speidel, Stefanie

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
Abstract

In the medical domain, the lack of large training data sets and benchmarks is often a limiting factor for training deep neural networks. In contrast to expensive manual labeling, computer simulations can generate large and fully labeled data sets with a minimum of manual effort. However, models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real laparoscopic images, we exploit recent advances in unpaired image-to-image translation. We extent an image-to-image translation method to generate a diverse multitude of realistically looking synthetic images based on images from a simple laparoscopy simulation. By incorporating means to ensure that the image content is preserved during the translation process, we ensure that the labels given for the simulated images remain valid for their realistically looking translations. This way, we are able to generate a large, fully labeled synthetic data set of laparoscopic images with realistic appearance. We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images. We achieve average dice scores of up to 0.89 in some patients without manually labeling a single laparoscopic image and show that using our synthetic data to pre-train models can greatly improve their performance. The synthetic data set will be made publicly available, fully labeled with segmentation maps, depth maps, normal maps, and positions of tools and camera (http://opencas.dkfz.de/image2image)., Comment: Accepted at MICCAI 2019

Published
2019

36. Towards Multi-modal Self-supervised Video and Ultrasound Pose Estimation for Laparoscopic Liver Surgery

Author

Montaña-Brown, Nina, Ramalhinho, João, Koo, Bongjin, Allam, Moustafa, Davidson, Brian, Gurusamy, Kurinchi, Hu, Yipeng, Clarkson, Matthew J., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Aylward, Stephen, editor, Noble, J. Alison, editor, Hu, Yipeng, editor, Lee, Su-Lin, editor, Baum, Zachary, editor, and Min, Zhe, editor

Published
2022
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37. Enhanced recovery for liver transplantation: recommendations from the 2022 International Liver Transplantation Society consensus conference

Author

Mayr, Andreas, Dominguez, Beatriz, Muller, Elmi, Rando, Karina, Enoch, Mary Anne, Tamir, Noam, Healy, Pamela, Manser, Tanja, Briggs, Tim, Chaudhary, Abhideep, Humar, Abhinav, Jafarian, Ali, Soin, Arvinder Singh, Eghtesad, Bijan, Miller, Charles, Cherqui, Daniel, Samuel, Didier, Broering, Dieter, Pomfret, Elizabeth, Villamil, Federico, Durand, Francois, Berlakovich, Gabriela, McCaughan, Geoffrey, Auzinger, Georg, Testa, Giuliano, Klintmalm, Goran, Belghiti, Jacques, Findlay, James, Lai, Jennifer, Fung, John, Klinck, John, Roberts, John, Liu, Linda, Cattral, Mark, Ghobrial, Mark, Selzner, Markus, Ramsay, Michael, Rela, Mohamed, Ascher, Nancy, Man, Nancy Kwan, Selzner, Nazia, Burra, Patrizia, Friend, Peter, Busuttil, Ronald, Hwang, Shin, McCluskey, Stuart, Mas, Valeria, Vohra, Vijay, Vij, Vivek, Merritt, William, Tokat, Yaman, Kang, Yoogoo, Chan, Albert, Mazzola, Alessandra, Hessheimer, Amelia, Rammohan, Ashwin, Hogan, Brian, Vinaixa, Carmen, Nasralla, David, Victor, David, De Martin, Eleonora, Alconchel, Felipe, Roll, Garrett, Kabacam, Gokhan, Sapisochin, Gonzalo, Campos-Varela, Isabel, Liu, Jiang, Patel, Madhukar S., Izzy, Manhal, Kalisvaart, Marit, Adams, Megan, Goldaracena, Nicholas, Tinguely, Pascale, Hernandez-Alejandro, Roberto, Chadha, Ryan, Shaker, Tamer Mahmoud, Klair, Tarunjeet S., Pan, Terry, Tanaka, Tomohiro, Yoon, Uzung, Kirchner, Varvara, Hannon, Vivienne, Cheah, Yee Lee, Frola, Carlo, Morkane, Clare, Milliken, Don, Lurje, Georg, Potts, Jonathan, Fernandez, Thomas, Badenoch, Adam, Mukhtar, Ahmed, Zanetto, Alberto, Montano-Loza, Aldo, Chieh, Alfred Kow Wei, Shetty, Amol, DeWolf, Andre, Olmos, Andrea, Mrzljak, Anna, Blasi, Annabel, Berzigotti, Annalisa, Malik, Ashish, Rajakumar, Akila, Davidson, Brian, O'Farrell, Bryan, Kotton, Camille, Imber, Charles, Kwon, Choon Hyuck David, Wray, Christopher, Ahn, Chul-Soo, Krenn, Claus, Quintini, Cristiano, Maluf, Daniel, Mina, Daniel Santa, Sellers, Daniel, Balci, Deniz, Patel, Dhupal, Rudow, Dianne LaPointe, Monbaliu, Diethard, Bezinover, Dmitri, Krzanicki, Dominik, Kim, Dong-Sik, Brombosz, Elizabeth, Blumberg, Emily, Weiss, Emmanuel, Wey, Emmanuel, Kaldas, Fady, Saliba, Faouzi, Pittau, Gabriella, Wagener, Gebhard, Song, Gi-Won, Biancofiore, Gianni, Crespo, Gonzalo, Rodríguez, Gonzalo, Palli, Graciela Martinez, McKenna, Gregory, Petrowsky, Henrik, Egawa, Hiroto, Montasser, Iman, Pirenne, Jacques, Eason, James, Guarrera, James, Pomposelli, James, Lerut, Jan, Emond, Jean, Boehly, Jennifer, Towey, Jennifer, Hillingsø, Jens G, de Jonge, Jeroen, Caicedo, Juan, Heimbach, Julie, Emamaullee, Juliet Ann, Bartoszko, Justyna, Ma, Ka Wing, Kronish, Kate, Forkin, Katherine T., Chok, Kenneth Siu Ho, Olthoff, Kim, Reyntjens, Koen, Lee, Kwang-Woong, Suh, Kyung-Suk, Denehy, Linda, van der Laan, Luc J.W., McCormack, Lucas, Gorvin, Lucy, Ruffolo, Luis, Bhat, Mamatha, Ramírez, María Amalia Matamoros, Londoño, Maria-Carlota, Gitman, Marina, Levstik, Mark, de Santibañes, Martin, Lindsay, Martine, Parotto, Matteo, Armstrong, Matthew, Kasahara, Mureo, Schofield, Nick, Rizkalla, Nicole, Akamatsu, Nobuhisa, Scatton, Olivier, Keskin, Onur, Imventarza, Oscar, Andacoglu, Oya, Muiesan, Paolo, Giorgio, Patricia, Northup, Patrick, Matins, Paulo, Abt, Peter, Newsome, Philip N, Dutkowski, Philipp, Bhangui, Pooja, Bhangui, Prashant, Tandon, Puneeta, Brustia, Raffaele, Planinsic, Raymond, Brown, Robert, Porte, Robert, Barth, Rolf, Ciria, Rubén, Florman, Sander, Dharancy, Sebastien, Pai, Sher-Lu, Yagi, Shintaro, Nadalin, Silvio, Chinnakotla, Srinath, Forbes, Stuart J, Rahman, Suehana, Hong, Suk Kyun, Liying, Sun, Orloff, Susan, Rubman, Susan, Eguchi, Susumu, Ikegami, Toru, Reichman, Trevor, Settmacher, Utz, Aluvihare, Varuna, Xia, Victor, Yoon, Young-In, Soejima, Yuji, Genyk, Yuri, Jalal, Arif, Borakati, Aditya, Gustar, Adrian, Mohamed, Ahmed, Ramirez, Alejandro, Rothnie, Alex, Scott, Aneya, Sharma, Anika, Munro, Annalise, Mahay, Arun, Liew, Belle, Hidalgo, Camila, Crouch, Cara, Yan, Cheung Tsz, Tschuor, Christoph, Shaw, Conrad, Schizas, Dimitrios, Fritche, Dominic, Huda, Fabia Ferdousi, Wells, Gemma, Farrer, Giselle, Kwok, Hiu Tat, Kostakis, Ioannis, Mestre-Costa, Joao, Fan, Ka Hay, Fan, Ka Siu, Fraser, Kyra, Jeilani, Lelia, Pang, Li, Lenti, Lorenzo, Kathirvel, Manikandan, Zachiotis, Marinos, Vailas, Michail, Milan, Michele Mazza, Elnagar, Mohamed, Alradhawi, Mohammad, Dimitrokallis, Nikolaos, Machairas, Nikolaos, Morare, Nolitha, Yeung, Oscar, Khanal, Pragalva, Satish, Pranav, Ghani, Shahi Abdul, Makhdoom, Shahroo, Arulrajan, Sithhipratha, Bogan, Stephanie, Pericleous, Stephanos, Blakemore, Timon, Otti, Vanessa, Lam, Walter, Jackson, Whitney, Abdi, Zakee, Pollok, Joerg M, Berenguer, Marina, Niemann, Claus U, Raptis, Dimitri A, and Spiro, Michael

Published
2023
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42. Spectral imaging of thermal damage induced during microwave ablation in the liver

Author

Clancy, Neil T., Gurusamy, Kurinchi, Jones, Geoffrey, Davidson, Brian, Clarkson, Matthew J., Hawkes, David J., and Stoyanov, Danail

Subjects
Physics - Medical Physics, 78A70 (Primary), 92C55, 78M22 (Secondary)
Abstract

Induction of thermal damage to tissue through delivery of microwave energy is frequently applied in surgery to destroy diseased tissue such as cancer cells. Minimization of unwanted harm to healthy tissue is still achieved subjectively, and the surgeon has few tools at their disposal to monitor the spread of the induced damage. This work describes the use of optical methods to monitor the time course of changes to the tissue during delivery of microwave energy in the porcine liver. Multispectral imaging and diffuse reflectance spectroscopy are used to monitor temporal changes in optical properties in parallel with thermal imaging. The results demonstrate the ability to monitor the spatial extent of thermal damage on a whole organ, including possible secondary effects due to vascular damage. Future applications of this type of imaging may see the multispectral data used as a feedback mechanism to avoid collateral damage to critical healthy structures and to potentially verify sufficient application of energy to the diseased tissue., Comment: 4pg,6fig. Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Published
2018
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43. Augmented Reality needle ablation guidance tool for Irreversible Electroporation in the pancreas

Author

Kuzhagaliyev, Timur, Clancy, Neil T., Janatka, Mirek, Tchaka, Kevin, Vasconcelos, Francisco, Clarkson, Matthew J., Gurusamy, Kurinchi, Hawkes, David J., Davidson, Brian, and Stoyanov, Danail

Subjects
Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Irreversible electroporation (IRE) is a soft tissue ablation technique suitable for treatment of inoperable tumours in the pancreas. The process involves applying a high voltage electric field to the tissue containing the mass using needle electrodes, leaving cancerous cells irreversibly damaged and vulnerable to apoptosis. Efficacy of the treatment depends heavily on the accuracy of needle placement and requires a high degree of skill from the operator. In this paper, we describe an Augmented Reality (AR) system designed to overcome the challenges associated with planning and guiding the needle insertion process. Our solution, based on the HoloLens (Microsoft, USA) platform, tracks the position of the headset, needle electrodes and ultrasound (US) probe in space. The proof of concept implementation of the system uses this tracking data to render real-time holographic guides on the HoloLens, giving the user insight into the current progress of needle insertion and an indication of the target needle trajectory. The operator's field of view is augmented using visual guides and real-time US feed rendered on a holographic plane, eliminating the need to consult external monitors. Based on these early prototypes, we are aiming to develop a system that will lower the skill level required for IRE while increasing overall accuracy of needle insertion and, hence, the likelihood of successful treatment., Comment: 6 pages, 5 figures. Proc. SPIE 10576 (2018) Copyright 2018 Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited

Published
2018
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46. Five-year recurrence/survival after pancreatoduodenectomy for pancreatic adenocarcinoma: does pre-existing diabetes matter? Results from the Recurrence After Whipple's (RAW) study

Author

Rajagopalan, Ashray, primary, Aroori, Somaiah, additional, Russell, Thomas B., additional, Labib, Peter L., additional, Ausania, Fabio, additional, Pando, Elizabeth, additional, Roberts, Keith J., additional, Kausar, Ambareen, additional, Mavroeidis, Vasileios K., additional, Marangoni, Gabriele, additional, Thomasset, Sarah C., additional, Frampton, Adam E., additional, Lykoudis, Pavlos, additional, Maglione, Manuel, additional, Alhaboob, Nassir, additional, Bari, Hassaan, additional, Smith, Andrew M., additional, Spalding, Duncan, additional, Srinivasan, Parthi, additional, Davidson, Brian R., additional, Bhogal, Ricky H., additional, Dominguez, Ismael, additional, Thakkar, Rohan, additional, Gomez, Dhanny, additional, Silva, Michael A., additional, Lapolla, Pierfrancesco, additional, Mingoli, Andrea, additional, Porcu, Alberto, additional, Shah, Nehal S., additional, Hamady, Zaed Z.R., additional, Al-Sarrieh, Bilal, additional, Serrablo, Alejandro, additional, Croagh, Daniel, additional, Gonzalez-Abos, Carolina, additional, Fernandes, Nair, additional, Moller, Elsa Garcia, additional, Taboada, Cristina Dopazo, additional, Roberts, Keith, additional, Pande, Rupaly, additional, Alfarah, Jameel, additional, Bandyopadhyay, Samik, additional, Abdelrahim, Ahmed, additional, Khan, Ayesha, additional, Jordan, Caitlin, additional, Rees, Jonathan R.E., additional, Blege, Harry, additional, Thomasset, Sarah, additional, Cambridge, William, additional, White, Olga, additional, Frampton, Adam, additional, Blacker, Sarah, additional, Blackburn, Jessie, additional, Sweeney, Casie, additional, Field, Daniel, additional, Gouda, Mohammed, additional, Bellotti, Ruben, additional, Hamid, Hytham K.S., additional, Ahmed, Hassan, additional, Smith, Andrew, additional, Moriarty, Catherine, additional, White, Louise, additional, Priestley, Mark, additional, Bode, Kerry, additional, Sharp, Judith, additional, Wragg, Rosie, additional, Jackson, Beverley, additional, Craven, Samuel, additional, Fehervari, Matyas, additional, Pai, Madhava, additional, Alghazawi, Laith, additional, Onifade, Anjola, additional, Ribaud, Julliette, additional, Nair, Ashitha, additional, Mariathasan, Michael, additional, Grayson, Niamh, additional, Davidson, Brian, additional, Pericleous, Stephanos, additional, Patel, Krishna, additional, Shaw, Conrad, additional, Morare, Nolitha, additional, Zaban, Mohamad Khish, additional, Bhogal, Ricky, additional, Doyle, Joseph, additional, Rajagopalan, Ashray, additional, Guerrero, Alan, additional, Moguel, Andre, additional, Chan, Carlos, additional, Jones, Michael, additional, Buckley, Edward, additional, Akter, Nasreen, additional, Treherne, Kyle, additional, Gordon, Gregory, additional, Silva, Michael, additional, Hughes, Daniel, additional, Urbonas, Tomas, additional, Brachini, Gioia, additional, Caronna, Roberto, additional, Chirletti, Piero, additional, Perra, Teresa, additional, Shah, Nehal Sureshkumar, additional, Abd Kahar, Nurul Nadhirah, additional, Hall, Thomas, additional, Nadeem, Nabeegh, additional, Hamady, Zaed, additional, Karar, Shoura, additional, Arshad, Ali, additional, Yarwood, Adam, additional, Hammoda, Mohammed, additional, Artigas, Maria, additional, Paterna-López, Sandra, additional, Streeter, Adam, additional, Denson, Jemimah, additional, Puckett, Mark, additional, Zhou, Shang-Ming, additional, and Browning, Matthew, additional

Published
2024
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47. Lipoprotein Apheresis Acutely Reverses Coronary Microvascular Dysfunction in Patients With Severe Hypercholesterolemia

Author

Wu, Melinda D, Moccetti, Federico, Brown, Eran, Davidson, Brian P, Atkinson, Tamara, Belcik, J Todd, Giraud, George, Duell, P Barton, Fazio, Sergio, Tavori, Hagai, Tsimikas, Sotirios, and Lindner, Jonathan R

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biomedical Imaging, Clinical Research, Heart Disease - Coronary Heart Disease, Heart Disease, Atherosclerosis, Cardiovascular, Aged, Animals, Biomarkers, Blood Component Removal, Case-Control Studies, Cholesterol, LDL, Coronary Circulation, Coronary Disease, Down-Regulation, Female, Humans, Hypercholesterolemia, Male, Microcirculation, Middle Aged, Muscle, Skeletal, Peripheral Arterial Disease, Recovery of Function, Severity of Illness Index, Sheep, Domestic, Time Factors, Treatment Outcome, apheresis, contrast ultrasound, familial hypercholesterolemia, myocardial contrast echocardiography, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

OBJECTIVES:This study evaluated whether lipoprotein apheresis produces immediate changes in resting perfusion in subjects with severe hypercholesterolemia, and whether there is a difference in the response between peripheral and coronary microcirculations. BACKGROUND:Lipoprotein apheresis is used in patients with severe hypercholesterolemia to reduce plasma levels of low-density lipoprotein cholesterol. METHODS:Quantitative contrast-enhanced ultrasound perfusion imaging of the myocardium at rest and skeletal muscle at rest and during calibrated contractile exercise was performed before and immediately after lipoprotein apheresis in 8 subjects with severe hypercholesterolemia, 7 of whom had a diagnosis of familial hypercholesterolemia. Myocardial perfusion imaging was also performed in 14 normal control subjects. Changes in myocardial work and left ventricular function were assessed by echocardiography. Ex vivo ovine coronary and femoral artery ring tension assays were assessed in the presence of pre- and post-apheresis plasma. RESULTS:Apheresis acutely decreased low-density lipoprotein cholesterol (234.9 ± 103.2 mg/dl vs. 67.1 ± 49.5 mg/dl; p < 0.01) and oxidized phospholipid on apolipoprotein B-100 (60.2 ± 55.2 nmol/l vs. 47.0 ± 24.5 nmol/l; p = 0.01), and acutely increased resting myocardial perfusion (55.1 [95% confidence interval: 77.2 to 73.1] IU/s vs. 135 [95% confidence interval: 81.2 to 189.6] IU/s; p = 0.01), without changes in myocardial work. Myocardial longitudinal strain improved in those subjects with reduced pre-apheresis function. Skeletal muscle perfusion at rest and during contractile exercise was unchanged by apheresis. Acetylcholine-mediated dilation of ex vivo ovine coronary but not femoral arteries was impaired in pre-apheresis plasma and was completely reversed in post-apheresis plasma. CONCLUSIONS:Lipoprotein apheresis produces an immediate improvement in coronary microvascular function, which increases myocardial perfusion and normalizes endothelial-dependent vasodilation. These changes are not observed in the periphery. (Acute Microvascular Changes With LDL Apheresis; NCT02388633).

Published
2019

49. Phase 0 Study of Vandetanib-Eluting Radiopaque Embolics as a Preoperative Embolization Treatment in Patients with Resectable Liver Malignancies

Author

Beaton, Laura, Tregidgo, Henry F.J., Znati, Sami A., Forsyth, Sharon, Counsell, Nicholas, Clarkson, Matthew J., Bandula, Steven, Chouhan, Manil, Lowe, Helen L., Thin, May Zaw, Hague, Julian, Sharma, Dinesh, Pollok, Joerg-Matthias, Davidson, Brian R., Raja, Jowad, Munneke, Graham, Stuckey, Daniel J., Bascal, Zainab A., Wilde, Paul E., Cooper, Sarah, Ryan, Samantha, Czuczman, Peter, Boucher, Eveline, Hartley, John A., Atkinson, David, Lewis, Andrew L., Jansen, Marnix, Meyer, Tim, and Sharma, Ricky A.

Published
2022
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50. Ward based goal directed fluid therapy (GDFT) in acute pancreatitis (GAP) trial: A feasibility randomised controlled trial

Author

Costello, Jonathan, Tzelis, Dimitris, Eastgate, Christine, Ciaponi, Maria Jose, McNeil, Margaret, Garcia, Sara Mingo, Pakou, Glykeria, Schwalowsky-Monks, Otto, Asis, Gretchelle, Osakanu, Atokoleka, Troller, Rebekkah, Dimitrokallis, Nikolaos, Pericleous, Stephanos, Khalil, Amjad, Abeles, Aliza, Rees, Charles, Abdulkareem, Khalid, Voultsos, Mavroudis, Froghi, Farid, Soggiu, Fiammetta, Ricciardi, Federico, Vindrola-Padros, Cecilia, Floros, Lefteris, Martin, Daniel, Filipe, Helder, Varcada, Massimo, Gurusamy, Kurinchi, Bhattacharya, Satya, Fanshawe, Angela, Delcea, Bogdan, Mathur, Pawan, and Davidson, Brian

Published
2022
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