39 results on '"Frood, Russell"'
Search Results
2. Federated Learning Enables Big Data for Rare Cancer Boundary Detection
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Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon
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Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS
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- 2022
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3. The use of machine learning/deep learning in PET/CT interpretation to aid in outcome prediction in lymphoma
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Frood, Russell Thomas, Scarsbrook, Andrew, Frangi, Alejandro, Tsoumpas, Charalampos, and Gleeson, Fergus
- Abstract
Lymphoma is a haematopoietic malignancy consisting of two broad categories: Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL). These categories can be further split into subtypes with classical HL (cHL) and diffuse large B cell lymphoma (DLBCL) being the commonest subtypes. The gold standard imaging modality for staging and response assessment for cHL and DLBCL is 2-deoxy-2-[fluorine-18]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT), with patients having a worse prognosis if they do not demonstrate complete metabolic response (CMR). However, approximately 15% of patients will relapse even after CMR. Therefore, being able to identify patients who are likely to relapse it may be possible to stratify treatment early to improve patient outcomes. The aim of this project is to develop and test image derived predictive models based on the baseline PET/CT to risk stratify patients pre-treatment.
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- 2022
4. Temporal evolution of chest radiographic appearances in COVID-19 with clinicoradiological associations: a multicentre United Kingdom resident-led study
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Gangadharan, Sunay, Parker, Storm, Earnshaw, Lydia, Pattinson, James, Tsang, Anthony, PooleNardia Poole, Nardia, Vaughan, Samuel, Morgan, Michael, Rogers, Priya, Kostic, Daniella, Gbegli, Emmanuel, Okonkwo, Ekene, Abani, Obinna, Llewellyn, Oliver, Calciu, Alexandru, Early, Tara, MacMillan, Mark, Khan, Nadir, Janjua, Osman, Jamil, Yasir, Curle, Jennifer, Yeoh, Tricia, Yu-Ching Chang, Kate, See, Yon Huang, Peng, Liam, Billingsley, Sarah, Zhong, Jim, Frood, Russell, Beecham, Joseph, Chan, Nathan, Elzubeir, Lee, Eminaga, Fatma, Kim, Taeyang, Goonasekera, Sanji Tharanga, Hassan, Syed Burair, Aryasomayajula, Saraswati Samyukta, Wijnburg, Alex, Jenkins, Paul, Finzel, Max, Khan, Abeera, Ali, Riaz, Thompson, Charlotte, Fee, Charles, Kite, Dominic, Davies, Sian, Veerasuri, Sowmya, Burnett, Tim, Charters, Pia, Evans, Catrin, Shahin, Yousef, Sukhanenko, Maria, de Boer, Henry, Shah, Nazia, Zahe, Rania, Malalasekera, Weeratunge Mudiyanselage Nishantha, Zamfir, Georgiana, Chattun, Halimah, Patel, Nickeel, Colman, Jordan, Ellis, Olivia, Grover, Kirin, Jassel, Inderbir, Bhatt, Devyani, Kishore, Ajit, Lee, Jonathan, Gangi-Burton, A., Chan, N., Jassel, I., Ashok, A.H., and Nair, A.
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- 2024
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5. Author Correction: Federated learning enables big data for rare cancer boundary detection
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Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon
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- 2023
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6. Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings
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Zhong, Jim, Frood, Russell, McWilliam, Alan, Davey, Angela, Shortall, Jane, Swinton, Martin, Hulson, Oliver, West, Catharine M., Buckley, David, Brown, Sarah, Choudhury, Ananya, Hoskin, Peter, Henry, Ann, and Scarsbrook, Andrew
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- 2023
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7. A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET–CT images: Initial analysis
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Duff, Lisa, Scarsbrook, Andrew F., Mackie, Sarah L., Frood, Russell, Bailey, Marc, Morgan, Ann W., and Tsoumpas, Charalampos
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- 2022
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8. Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma
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Frood, Russell, Clark, Matt, Burton, Cathy, Tsoumpas, Charalampos, Frangi, Alejandro F., Gleeson, Fergus, Patel, Chirag, and Scarsbrook, Andrew
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- 2022
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9. Harmonisation of scanner-dependent contrast variations in magnetic resonance imaging for radiation oncology, using style-blind auto-encoders
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Fatania, Kavi, Clark, Anna, Frood, Russell, Scarsbrook, Andrew, Al-Qaisieh, Bashar, Currie, Stuart, and Nix, Michael
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- 2022
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10. Tumour Size and Overall Survival in a Cohort of Patients with Unifocal Glioblastoma: A Uni- and Multivariable Prognostic Modelling and Resampling Study
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Fatania, Kavi, primary, Frood, Russell, additional, Mistry, Hitesh, additional, Short, Susan C., additional, O’Connor, James, additional, Scarsbrook, Andrew F., additional, and Currie, Stuart, additional
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- 2024
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11. Prediction of Patient Outcomes in Locally Advanced Cervical Carcinoma following Chemoradiotherapy—Comparative Effectiveness of Magnetic Resonance Imaging and 2-Deoxy-2-[18F]fluoro-D-glucose Imaging
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Dhesi, Simran Singh, primary, Frood, Russell, additional, Swift, Sarah, additional, Cooper, Rachel, additional, Muzumdar, Siddhant, additional, Jamal, Mehvish, additional, and Scarsbrook, Andrew, additional
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- 2024
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12. Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study).
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Chelliah, Alysha, Wood, David A, Canas, Liane S, Shuaib, Haris, Currie, Stuart, Fatania, Kavi, Frood, Russell, Rowland-Hill, Chris, Thust, Stefanie, Wastling, Stephen J, Tenant, Sean, McBain, Catherine, Foweraker, Karen, Williams, Matthew, Wang, Qiquan, Roman, Andrei, Dragos, Carmen, MacDonald, Mark, Lau, Yue Hui, and Linares, Christian A
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- 2024
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13. Bronchial Artery Embolisation for Massive Haemoptysis: Immediate and Long-Term Outcomes—A Retrospective Study
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Frood, Russell, Karthik, Shishir, Mirsadraee, Saeed, Clifton, Ian, Flood, Karen, and McPherson, Simon J.
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- 2020
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14. Prediction of Patient Outcomes in Locally Advanced Cervical Carcinoma Following Chemoradiotherapy—Comparative Effectiveness of Magnetic Resonance Imaging and 2-Deoxy-2-[ 18 F]fluoro-D-glucose Imaging.
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Dhesi, Simran Singh, Frood, Russell, Swift, Sarah, Cooper, Rachel, Muzumdar, Siddhant, Jamal, Mehvish, and Scarsbrook, Andrew
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PREDICTIVE tests , *LOG-rank test , *METASTASIS , *CANCER relapse , *MAGNETIC resonance imaging , *POSITRON emission tomography computed tomography , *RETROSPECTIVE studies , *CHEMORADIOTHERAPY , *RISK assessment , *TREATMENT effectiveness , *COMPARATIVE studies , *RADIOPHARMACEUTICALS , *KAPLAN-Meier estimator , *DEOXY sugars , *PROGRESSION-free survival , *DATA analysis , *SENSITIVITY & specificity (Statistics) , *OVERALL survival , *PROPORTIONAL hazards models , *DISEASE risk factors , *EVALUATION ,CERVIX uteri tumors - Abstract
Simple Summary: This study investigates the effectiveness of three imaging methods—T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography-computed tomography (2-[18F]FDG PET-CT)—individually and combined, in assessing treatment response for locally advanced cervical carcinoma (LACC). As the third most common cancer worldwide, precise post-treatment evaluation is crucial for planning and follow-up. This research addresses the lack of a standardised response assessment after chemoradiotherapy for LACC, introducing a five-point qualitative scale for assessment. The findings aim to fill knowledge gaps in treatment response evaluation, potentially influencing clinical practices for better patient outcomes in cervical cancer management. Purpose: To evaluate the utility and comparative effectiveness of three five-point qualitative scoring systems for assessing response on PET-CT and MRI imaging individually and in combination, following curative-intent chemoradiotherapy (CRT) in locally advanced cervical cancer (LACC). Their performance in the prediction of subsequent patient outcomes was also assessed; Methods: Ninety-seven patients with histologically confirmed LACC treated with CRT using standard institutional protocols at a single centre who underwent PET-CT and MRI at staging and post treatment were identified retrospectively from an institutional database. The post-CRT imaging studies were independently reviewed, and response assessed using five-point scoring tools for T2WI, DWI, and FDG PET-CT. Patient characteristics, staging, treatment, and follow-up details including progression-free survival (PFS) and overall survival (OS) outcomes were collected. To compare diagnostic performance metrics, a two-proportion z-test was employed. A Kaplan–Meier analysis (Mantel–Cox log-rank) was performed. Results: The T2WI (p < 0.00001, p < 0.00001) and DWI response scores (p < 0.00001, p = 0.0002) had higher specificity and accuracy than the PET-CT. The T2WI score had the highest positive predictive value (PPV), while the negative predictive value (NPV) was consistent across modalities. The combined MR scores maintained high NPV, PPV, specificity, and sensitivity, and the PET/MR consensus scores showed superior diagnostic accuracy and specificity compared to the PET-CT score alone (p = 0.02926, p = 0.0083). The Kaplan–Meier analysis revealed significant differences in the PFS based on the T2WI (p < 0.001), DWI (p < 0.001), combined MR (p = 0.003), and PET-CT/MR consensus scores (p < 0.001) and in the OS for the T2WI (p < 0.001), DWI (p < 0.001), and combined MR scores (p = 0.031) between responders and non-responders. Conclusion: Post-CRT response assessment using qualitative MR scoring and/or consensus PET-CT and MRI scoring was a better predictor of outcome compared to PET-CT assessment alone. This requires validation in a larger prospective study but offers the potential to help stratify patient follow-up in the future. [ABSTRACT FROM AUTHOR]
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- 2024
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15. Comparative effectiveness of standard vs. AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging: a multi-institutional reader study evaluation.
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Frood, Russell, Willaime, Julien M. Y., Miles, Brad, Chambers, Greg, Al-Chalabi, H'ssein, Ali, Tamir, Hougham, Natasha, Brooks, Naomi, Petrides, George, Naylor, Matthew, Ward, Daniel, Sulkin, Tom, Chaytor, Richard, Strouhal, Peter, Patel, Chirag, and Scarsbrook, Andrew F.
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ARTIFICIAL intelligence ,WORKFLOW ,LYMPHOMAS ,COMPUTER software quality control ,COMPUTED tomography ,ADRENAL insufficiency - Abstract
Background: Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymphoma, with the time to evaluate such studies varying depending on the complexity of the case. Integrating artificial intelligence (AI) within the reporting workflow has the potential to improve quality and efficiency. The aims of the present study were to evaluate the influence of an integrated research prototype segmentation tool implemented within diagnostic PET/CT reading software on the speed and quality of reporting with variable levels of experience, and to assess the effect of the AI-assisted workflow on reader confidence and whether this tool influenced reporting behaviour. Methods: Nine blinded reporters (three trainees, three junior consultants and three senior consultants) from three UK centres participated in a two-part reader study. A total of 15 lymphoma staging PET/CT scans were evaluated twice: first, using a standard PET/CT reporting workflow; then, after a 6- week gap, with AI assistance incorporating pre-segmentation of disease sites within the reading software. An even split of PET/CT segmentations with gold standard (GS), false-positive (FP) over-contour or false-negative (FN) under-contour were provided. The read duration was calculated using file logs, while the report quality was independently assessed by two radiologists with >15 years of experience. Confidence in AI assistance and identification of disease was assessed via online questionnaires for each case. Results: There was a significant decrease in time between non-AI and AI-assisted reads (median 15.0 vs. 13.3 min, p <0.001). Sub-analysis confirmed this was true for both junior (14.5 vs. 12.7 min, p = 0.03) and senior consultants (15.1 vs. 12.2 min, p = 0.03) but not for trainees (18.1 vs. 18.0 min, p = 0.2). There was no significant difference between report quality between reads. AI assistance provided a significant increase in confidence of disease identification (p <0.001). This held true when splitting the data into FN, GS and FP. In 19/88 cases, participants did not identify either FP (31.8%) or FN (11.4%) segmentations. This was significantly greater for trainees (13/30, 43.3%) than for junior (3/28, 10.7%, p = 0.05) and senior consultants (3/30, 10.0%, p = 0.05). Conclusions: The study findings indicate that an AI-assisted workflow achieves comparable performance to humans, demonstrating a marginal enhancement in reporting speed. Less experienced readers were more influenced by segmentation errors. An AI-assisted PET/CT reading workflow has the potential to increase reporting efficiency without adversely affecting quality, which could reduce costs and report turnaround times. These preliminary findings need to be confirmed in larger studies. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Imaging Spectrum of the Developing Glioblastoma: A Cross-Sectional Observation Study
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Currie, Stuart, primary, Fatania, Kavi, additional, Frood, Russell, additional, Whitehead, Ruth, additional, Start, Joanna, additional, Lee, Ming-Te, additional, McDonald, Benjamin, additional, Rankeillor, Kate, additional, Roberts, Paul, additional, Chakrabarty, Aruna, additional, Mathew, Ryan K., additional, Murray, Louise, additional, Short, Susan, additional, and Scarsbrook, Andrew, additional
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- 2023
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17. Can MR textural analysis improve the prediction of extracapsular nodal spread in patients with oral cavity cancer?
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Frood, Russell, Palkhi, Ebrahim, Barnfield, Mark, Prestwich, Robin, Vaidyanathan, Sriram, and Scarsbrook, Andrew
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- 2018
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18. An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images
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Duff, Lisa M, Scarsbrook, Andrew F, Ravikumar, Nishant, Frood, Russell, Van Praagh, Gijs D, Mackie, Sarah L, Bailey, Marc A, Tarkin, Jason M, Mason, Justin C, Van Der Geest, Kornelis SM, Slart, Riemer HJA, Morgan, Ann W, Tsoumpas, Charalampos, Cardiovascular Centre (CVC), Translational Immunology Groningen (TRIGR), Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Guided Treatment in Optimal Selected Cancer Patients (GUTS), Duff, Lisa M [0000-0002-4295-6356], Scarsbrook, Andrew F [0000-0002-4243-032X], Frood, Russell [0000-0003-2681-9922], van Praagh, Gijs D [0000-0002-4396-637X], Slart, Riemer HJA [0000-0002-5565-1164], Tsoumpas, Charalampos [0000-0002-4971-2477], and Apollo - University of Cambridge Repository
- Subjects
machine learning ,ROC Curve ,Fluorodeoxyglucose F18 ,radiomics ,Positron Emission Tomography Computed Tomography ,Humans ,convolutional neural network ,positron emission tomography/computed tomography ,Radiopharmaceuticals ,Molecular Biology ,Biochemistry ,aortitis - Abstract
Peer reviewed: True, The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
- Published
- 2023
19. Clinical Utility of Second-Look FDG PET-CT to Stratify Incomplete Metabolic Response Post (Chemo) Radiotherapy in Oropharyngeal Squamous Cell Carcinoma
- Author
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Billingsley, Sarah, primary, Iyizoba, Zsuzsanna, additional, Frood, Russell, additional, Vaidyanathan, Sriram, additional, Prestwich, Robin, additional, and Scarsbrook, Andrew, additional
- Published
- 2023
- Full Text
- View/download PDF
20. Prediction of patient outcome in locally advanced cervical carcinoma following chemoradiotherapy - comparative effectiveness of MRI and FDG PET-CT
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Frood, Russell
- Subjects
Oncology ,Nuclear medicine ,PET-CT ,Chemotherapy ,MR ,Radiation therapy / Oncology ,Genital / Reproductive system female ,Cancer - Abstract
Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: Cervical cancer is a common malignancy that has a high rate of recurrence within two years of treatment, with a reported incidence of 30% [1]. Treatment response following chemoradiotherapy (CRT) is not standardised amongst institutions and is...
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- 2023
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21. Reporter confidence of an AI-assisted PET/CT reading workflow in pre-treatment assessment of high-grade lymphoma : multi-centre reader study
- Author
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Frood, Russell
- Subjects
Artificial Intelligence ,Computer Applications-Detection, diagnosis ,PET-CT ,Haematologic diseases ,Haematologic ,Cancer - Abstract
Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: The incidence of lymphoma is increasing worldwide [1] with Fluorine-18 fluorodeoxyglucose(FDG)-positron emission tomography-computed tomography (PET-CT) being the gold standard imaging technique for staging and response assessment of high-grade...
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- 2023
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22. Federated learning enables big data for rare cancer boundary detection
- Author
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Pati, Sarthak, primary, Baid, Ujjwal, additional, Edwards, Brandon, additional, Sheller, Micah, additional, Wang, Shih-Han, additional, Reina, G. Anthony, additional, Foley, Patrick, additional, Gruzdev, Alexey, additional, Karkada, Deepthi, additional, Davatzikos, Christos, additional, Sako, Chiharu, additional, Ghodasara, Satyam, additional, Bilello, Michel, additional, Mohan, Suyash, additional, Vollmuth, Philipp, additional, Brugnara, Gianluca, additional, Preetha, Chandrakanth J., additional, Sahm, Felix, additional, Maier-Hein, Klaus, additional, Zenk, Maximilian, additional, Bendszus, Martin, additional, Wick, Wolfgang, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, Villanueva-Meyer, Javier, additional, Cha, Soonmee, additional, Ingalhalikar, Madhura, additional, Jadhav, Manali, additional, Pandey, Umang, additional, Saini, Jitender, additional, Garrett, John, additional, Larson, Matthew, additional, Jeraj, Robert, additional, Currie, Stuart, additional, Frood, Russell, additional, Fatania, Kavi, additional, Huang, Raymond Y., additional, Chang, Ken, additional, Balaña, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Trenkler, Johannes, additional, Pichler, Josef, additional, Necker, Georg, additional, Haunschmidt, Andreas, additional, Meckel, Stephan, additional, Shukla, Gaurav, additional, Liem, Spencer, additional, Alexander, Gregory S., additional, Lombardo, Joseph, additional, Palmer, Joshua D., additional, Flanders, Adam E., additional, Dicker, Adam P., additional, Sair, Haris I., additional, Jones, Craig K., additional, Venkataraman, Archana, additional, Jiang, Meirui, additional, So, Tiffany Y., additional, Chen, Cheng, additional, Heng, Pheng Ann, additional, Dou, Qi, additional, Kozubek, Michal, additional, Lux, Filip, additional, Michálek, Jan, additional, Matula, Petr, additional, Keřkovský, Miloš, additional, Kopřivová, Tereza, additional, Dostál, Marek, additional, Vybíhal, Václav, additional, Vogelbaum, Michael A., additional, Mitchell, J. Ross, additional, Farinhas, Joaquim, additional, Maldjian, Joseph A., additional, Yogananda, Chandan Ganesh Bangalore, additional, Pinho, Marco C., additional, Reddy, Divya, additional, Holcomb, James, additional, Wagner, Benjamin C., additional, Ellingson, Benjamin M., additional, Cloughesy, Timothy F., additional, Raymond, Catalina, additional, Oughourlian, Talia, additional, Hagiwara, Akifumi, additional, Wang, Chencai, additional, To, Minh-Son, additional, Bhardwaj, Sargam, additional, Chong, Chee, additional, Agzarian, Marc, additional, Falcão, Alexandre Xavier, additional, Martins, Samuel B., additional, Teixeira, Bernardo C. A., additional, Sprenger, Flávia, additional, Menotti, David, additional, Lucio, Diego R., additional, LaMontagne, Pamela, additional, Marcus, Daniel, additional, Wiestler, Benedikt, additional, Kofler, Florian, additional, Ezhov, Ivan, additional, Metz, Marie, additional, Jain, Rajan, additional, Lee, Matthew, additional, Lui, Yvonne W., additional, McKinley, Richard, additional, Slotboom, Johannes, additional, Radojewski, Piotr, additional, Meier, Raphael, additional, Wiest, Roland, additional, Murcia, Derrick, additional, Fu, Eric, additional, Haas, Rourke, additional, Thompson, John, additional, Ormond, David Ryan, additional, Badve, Chaitra, additional, Sloan, Andrew E., additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Colen, Rivka R., additional, Pei, Linmin, additional, Ak, Murat, additional, Srinivasan, Ashok, additional, Bapuraj, J. Rajiv, additional, Rao, Arvind, additional, Wang, Nicholas, additional, Yoshiaki, Ota, additional, Moritani, Toshio, additional, Turk, Sevcan, additional, Lee, Joonsang, additional, Prabhudesai, Snehal, additional, Morón, Fanny, additional, Mandel, Jacob, additional, Kamnitsas, Konstantinos, additional, Glocker, Ben, additional, Dixon, Luke V. M., additional, Williams, Matthew, additional, Zampakis, Peter, additional, Panagiotopoulos, Vasileios, additional, Tsiganos, Panagiotis, additional, Alexiou, Sotiris, additional, Haliassos, Ilias, additional, Zacharaki, Evangelia I., additional, Moustakas, Konstantinos, additional, Kalogeropoulou, Christina, additional, Kardamakis, Dimitrios M., additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Chang, Jong Hee, additional, Ahn, Sung Soo, additional, Luo, Bing, additional, Poisson, Laila, additional, Wen, Ning, additional, Tiwari, Pallavi, additional, Verma, Ruchika, additional, Bareja, Rohan, additional, Yadav, Ipsa, additional, Chen, Jonathan, additional, Kumar, Neeraj, additional, Smits, Marion, additional, van der Voort, Sebastian R., additional, Alafandi, Ahmed, additional, Incekara, Fatih, additional, Wijnenga, Maarten M. J., additional, Kapsas, Georgios, additional, Gahrmann, Renske, additional, Schouten, Joost W., additional, Dubbink, Hendrikus J., additional, Vincent, Arnaud J. P. E., additional, van den Bent, Martin J., additional, French, Pim J., additional, Klein, Stefan, additional, Yuan, Yading, additional, Sharma, Sonam, additional, Tseng, Tzu-Chi, additional, Adabi, Saba, additional, Niclou, Simone P., additional, Keunen, Olivier, additional, Hau, Ann-Christin, additional, Vallières, Martin, additional, Fortin, David, additional, Lepage, Martin, additional, Landman, Bennett, additional, Ramadass, Karthik, additional, Xu, Kaiwen, additional, Chotai, Silky, additional, Chambless, Lola B., additional, Mistry, Akshitkumar, additional, Thompson, Reid C., additional, Gusev, Yuriy, additional, Bhuvaneshwar, Krithika, additional, Sayah, Anousheh, additional, Bencheqroun, Camelia, additional, Belouali, Anas, additional, Madhavan, Subha, additional, Booth, Thomas C., additional, Chelliah, Alysha, additional, Modat, Marc, additional, Shuaib, Haris, additional, Dragos, Carmen, additional, Abayazeed, Aly, additional, Kolodziej, Kenneth, additional, Hill, Michael, additional, Abbassy, Ahmed, additional, Gamal, Shady, additional, Mekhaimar, Mahmoud, additional, Qayati, Mohamed, additional, Reyes, Mauricio, additional, Park, Ji Eun, additional, Yun, Jihye, additional, Kim, Ho Sung, additional, Mahajan, Abhishek, additional, Muzi, Mark, additional, Benson, Sean, additional, Beets-Tan, Regina G. H., additional, Teuwen, Jonas, additional, Herrera-Trujillo, Alejandro, additional, Trujillo, Maria, additional, Escobar, William, additional, Abello, Ana, additional, Bernal, Jose, additional, Gómez, Jhon, additional, Choi, Joseph, additional, Baek, Stephen, additional, Kim, Yusung, additional, Ismael, Heba, additional, Allen, Bryan, additional, Buatti, John M., additional, Kotrotsou, Aikaterini, additional, Li, Hongwei, additional, Weiss, Tobias, additional, Weller, Michael, additional, Bink, Andrea, additional, Pouymayou, Bertrand, additional, Shaykh, Hassan F., additional, Saltz, Joel, additional, Prasanna, Prateek, additional, Shrestha, Sampurna, additional, Mani, Kartik M., additional, Payne, David, additional, Kurc, Tahsin, additional, Pelaez, Enrique, additional, Franco-Maldonado, Heydy, additional, Loayza, Francis, additional, Quevedo, Sebastian, additional, Guevara, Pamela, additional, Torche, Esteban, additional, Mendoza, Cristobal, additional, Vera, Franco, additional, Ríos, Elvis, additional, López, Eduardo, additional, Velastin, Sergio A., additional, Ogbole, Godwin, additional, Soneye, Mayowa, additional, Oyekunle, Dotun, additional, Odafe-Oyibotha, Olubunmi, additional, Osobu, Babatunde, additional, Shu’aibu, Mustapha, additional, Dorcas, Adeleye, additional, Dako, Farouk, additional, Simpson, Amber L., additional, Hamghalam, Mohammad, additional, Peoples, Jacob J., additional, Hu, Ricky, additional, Tran, Anh, additional, Cutler, Danielle, additional, Moraes, Fabio Y., additional, Boss, Michael A., additional, Gimpel, James, additional, Veettil, Deepak Kattil, additional, Schmidt, Kendall, additional, Bialecki, Brian, additional, Marella, Sailaja, additional, Price, Cynthia, additional, Cimino, Lisa, additional, Apgar, Charles, additional, Shah, Prashant, additional, Menze, Bjoern, additional, Barnholtz-Sloan, Jill S., additional, Martin, Jason, additional, and Bakas, Spyridon, additional
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- 2022
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- View/download PDF
23. Accuracy of response assessment FDG PET-CT post-(chemo)radiotherapy in HPV-negative oropharynx squamous cell carcinoma
- Author
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Ebozue, Zsuzsanna Iyizoba, primary, Billingsley, Sarah, additional, Frood, Russell, additional, Vaidyanathan, Sriram, additional, Scarsbrook, Andrew, additional, and Prestwich, Robin, additional
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- 2022
- Full Text
- View/download PDF
24. Accuracy of Response Assessment FDG PET-CT Post (Chemo)Radiotherapy in HPV Negative Oropharynx Squamous Cell Carcinoma
- Author
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Iyizoba-Ebozue, Zsuzsanna, primary, Billingsley, Sarah, additional, Frood, Russell, additional, Vaidyanathan, Sriram, additional, Scarsbrook, Andrew, additional, and Prestwich, Robin J. D., additional
- Published
- 2022
- Full Text
- View/download PDF
25. Exploratory Analysis of Serial 18F-fluciclovine PET-CT and Multiparametric MRI during Chemoradiation for Glioblastoma
- Author
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Fatania, Kavi, primary, Frood, Russell, additional, Tyyger, Marcus, additional, McDermott, Garry, additional, Fernandez, Sharon, additional, Shaw, Gary C., additional, Boissinot, Marjorie, additional, Salvatore, Daniela, additional, Ottobrini, Luisa, additional, Teh, Irvin, additional, Wright, John, additional, Bailey, Marc A., additional, Koch-Paszkowski, Joanna, additional, Schneider, Jurgen E., additional, Buckley, David L., additional, Murray, Louise, additional, Scarsbrook, Andrew, additional, Short, Susan C., additional, and Currie, Stuart, additional
- Published
- 2022
- Full Text
- View/download PDF
26. Evaluation of a tertiary centre specialist adrenal MDT: The first 900 patients
- Author
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Child, Louisa, primary, Sagar, Rebecca, additional, Fraser, Sheila, additional, Collins, Emma, additional, Frood, Russell, additional, Scarsbrook, Andrew, additional, and Abbas, Afroze, additional
- Published
- 2022
- Full Text
- View/download PDF
27. Mild autonomous cortisol secretion in patients with adrenal incidentalomas and raised cardiovascular risk
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Sagar, Rebecca, primary, Fraser, Sheila, additional, Collins, Emma, additional, Frood, Russell, additional, Scarsbrook, Andrew, additional, M, Stewart Paul, additional, and Abbas, Afroze, additional
- Published
- 2022
- Full Text
- View/download PDF
28. Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell Lymphoma
- Author
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Frood, Russell, primary, Clark, Matthew, additional, Burton, Cathy, additional, Tsoumpas, Charalampos, additional, Frangi, Alejandro F., additional, Gleeson, Fergus, additional, Patel, Chirag, additional, and Scarsbrook, Andrew F., additional
- Published
- 2022
- Full Text
- View/download PDF
29. Texture analysis: The not so new tool
- Author
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Frood, Russell, primary
- Published
- 2021
- Full Text
- View/download PDF
30. Exploratory Analysis of Serial 18 F-fluciclovine PET-CT and Multiparametric MRI during Chemoradiation for Glioblastoma.
- Author
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Fatania, Kavi, Frood, Russell, Tyyger, Marcus, McDermott, Garry, Fernandez, Sharon, Shaw, Gary C., Boissinot, Marjorie, Salvatore, Daniela, Ottobrini, Luisa, Teh, Irvin, Wright, John, Bailey, Marc A., Koch-Paszkowski, Joanna, Schneider, Jurgen E., Buckley, David L., Murray, Louise, Scarsbrook, Andrew, Short, Susan C., and Currie, Stuart
- Subjects
- *
GLIOMA treatment , *PILOT projects , *ANIMAL experimentation , *IMMUNOHISTOCHEMISTRY , *MAGNETIC resonance imaging , *ADJUVANT treatment of cancer , *CHEMORADIOTHERAPY , *SURVIVAL analysis (Biometry) , *COMPUTED tomography , *AMINO acids , *MICE , *CARRIER proteins - Abstract
Simple Summary: Glioblastoma (GBM), the most common malignant adult primary brain tumour, has a prognosis of ~12–15 months. Poor prognosis is partly due to the inability to accurately define the extent of tumour infiltration; currently demarcated using magnetic resonance imaging (MRI) sequences (e.g., post-contrast T1-weighted (Gd-T1) and dynamic contrast-enhanced (DCE-MRI)). Anti-1-amino-3-18fluorine-fluorocyclobutane-1-carboxylic acid (18F-fluciclovine) positron emission tomography (PET) may depict GBM better than MRI. This prospective pilot study aimed to explore the relationship of 18F-fluciclovine PET, DCE-MRI and Gd-T1 in patients with GBM undergoing standard-of-care adjuvant chemoradiotherapy. A parallel mouse glioma model was used to investigate the relationship between 18F-fluciclovine PET, MRI and tumour biology. Clinical results showed that GBM volume on 18F-fluciclovine PET tended to be larger than Gd-T1 and DCE-MRI in patients with shorter overall survival (OS) but smaller in patients with longer OS. The preclinical study confirmed that 18F-fluciclovine uptake reflected biologically active tumour. Results suggest that 18F-fluciclovine PET may better define GBM infiltration than MRI. Anti-1-amino-3-18fluorine-fluorocyclobutane-1-carboxylic acid (18F-fluciclovine) positron emission tomography (PET) shows preferential glioma uptake but there is little data on how uptake correlates with post-contrast T1-weighted (Gd-T1) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) activity during adjuvant treatment. This pilot study aimed to compare 18F-fluciclovine PET, DCE-MRI and Gd-T1 in patients undergoing chemoradiotherapy for glioblastoma (GBM), and in a parallel pre-clinical GBM model, to investigate correlation between 18F-fluciclovine uptake, MRI findings, and tumour biology. 18F-fluciclovine-PET-computed tomography (PET-CT) and MRI including DCE-MRI were acquired before, during and after adjuvant chemoradiotherapy (60 Gy in 30 fractions with temozolomide) in GBM patients. MRI volumes were manually contoured; PET volumes were defined using semi-automatic thresholding. The similarity of the PET and DCE-MRI volumes outside the Gd-T1 volume boundary was measured using the Dice similarity coefficient (DSC). CT-2A tumour-bearing mice underwent MRI and 18F-fluciclovine PET-CT. Post-mortem mice brains underwent immunohistochemistry staining for ASCT2 (amino acid transporter), nestin (stemness) and Ki-67 (proliferation) to assess for biologically active tumour. 6 patients were recruited (GBM 1–6) and grouped according to overall survival (OS)—short survival (GBM-SS, median OS 249 days) and long survival (GBM-LS, median 903 days). For GBM-SS, PET tumour volumes were greater than DCE-MRI, in turn greater than Gd-T1. For GBM-LS, Gd-T1 and DCE-MRI were greater than PET. Tumour-specific 18F-fluciclovine uptake on pre-clinical PET-CT corresponded to immunostaining for Ki-67, nestin and ASCT2. Results suggest volumes of 18F-fluciclovine-PET activity beyond that depicted by DCE-MRI and Gd-T1 are associated with poorer prognosis in patients undergoing chemoradiotherapy for GBM. The pre-clinical model confirmed 18F-fluciclovine uptake reflected biologically active tumour. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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31. NIMG-16. FEASIBLITY OF FLUORINE-18 FLUCICLOVINE PET-CT AND MRI FOR MONITORING OF CHEMO-RADIATION IN GLIOBLASTOMA: INITIAL RESULTS FROM A PILOT STUDY
- Author
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Short, Susan C, primary, Frood, Russell, additional, Broadbent, David, additional, Fernandez, Sharon, additional, McDermott, Garry, additional, Al-Qaisieh, Bashar, additional, Buckley, David, additional, Currie, Stuart, additional, Murray, Louise, additional, and Scarsbrook, Andrew, additional
- Published
- 2019
- Full Text
- View/download PDF
32. Vascular interventions for renal transplants
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Frood, Russell
- Subjects
Transplantation ,genetic structures ,MR-Angiography ,Ultrasound ,Angioplasty ,Interventional vascular ,Stents ,Kidney ,Diagnostic procedure ,Catheter arteriography - Abstract
Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: Vascular complications following renal transplant can affect both the arterial and venous supply. Arterial complications may include stenosis, arteriovenous fistula,arteriocalyceal fistula,thrombosis,pseudoaneurysm and kinking of the transplant...
- Published
- 2018
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- View/download PDF
33. 18F-FDG PET-CT in paediatric oncology: established and emerging applications
- Author
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Chambers, Greg, primary, Frood, Russell, additional, Patel, Chirag, additional, and Scarsbrook, Andrew, additional
- Published
- 2019
- Full Text
- View/download PDF
34. Respiratory-gated PET/CT for pulmonary lesion characterisation—promises and problems
- Author
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Frood, Russell, primary, McDermott, Garry, additional, and Scarsbrook, Andrew, additional
- Published
- 2018
- Full Text
- View/download PDF
35. Paediatric PET-CT: a ten-year service review
- Author
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Chambers, Greg, primary, Frood, Russell, additional, Nejadhamzeeigilani, Hamed, additional, and Patel, Chirag, additional
- Published
- 2017
- Full Text
- View/download PDF
36. The use of treadmill training to recover locomotor ability in patients with spinal cord injury.
- Author
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Frood, Russell Thomas
- Subjects
- *
SPINAL cord injuries , *GAIT disorders , *TREADMILL exercise , *ELECTROMYOGRAPHY , *ELECTRIC stimulation - Abstract
Spinal cord injury (SCI) affects over 1000 people a year in the UK and has severe consequences for their independence and quality of life. Treatments to address SCI focus on techniques that aim to restore some degree of walking or locomotor activity. One such technique is treadmill training of SCI patients. This paper reviews the use of treadmill training in the recovery of locomotor ability in patients with SCI. Outcomes from treadmill training are variable; for patients with incomplete SCI (where some degree of connection between the brain and the spinal cord is spared from injury), treadmill training only enabled limited full weight-bearing locomotion. In patients suffering a complete SCI (where communication between the brain and spinal cord is lost), no weight-bearing locomotion at all was achieved with training. However, treadmill training does influence the activity of the leg muscles in the acute patients, observed by recordings made from the muscles (electromyography). The improvements achieved by treadmill training are not significantly different from other techniques such as overground training and functional electrical stimulation. The most effective way of restoring locomotion is through complete repair; however, regeneration techniques are still being developed. For regeneration to take place, the neurons within the spinal cord that are important in generating rhythmic movements (the central pattern generator (CPG) circuits) still need to be functioning, as these circuits have been shown to decline through long periods of inactivation. Treadmill training has therefore an important role in keeping neurons active until regenerative techniques become viable. Furthermore, in spinalized rats, it has been shown that by combining treadmill training with pharmaceutical and electrical stimulation therapies, greater improvements are seen. This suggests that the treatment of spinal cord injury should not be limited to one method. Techniques that repair the damage are the ultimate goal and it is important that patients keep active in order to increase chances of recovery. [ABSTRACT FROM PUBLISHER]
- Published
- 2011
- Full Text
- View/download PDF
37. 18F-FDG PET-CT in paediatric oncology: established and emerging applications.
- Author
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Scarsbrook, Andrew, Chambers, Greg, Frood, Russell, and Patel, Chirag
- Subjects
CHILDHOOD cancer ,FLUORINE ,POSITRON emission tomography ,ONCOLOGY ,CANCER radiotherapy - Abstract
Accurate staging and response assessment is vital in the management of childhood malignancies. Fluorine-18 fluorodeoxyglucose positron emission tomography/CT (FDG PET-CT) provides complimentary anatomical and functional information. Oncological applications of FDG PET-CT are not as well-established within the paediatric population compared to adults. This article will comprehensively review established oncological PET-CT applications in paediatric oncology and provide an overview of emerging and future developments in this domain. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
38. An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images.
- Author
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Duff LM, Scarsbrook AF, Ravikumar N, Frood R, van Praagh GD, Mackie SL, Bailey MA, Tarkin JM, Mason JC, van der Geest KSM, Slart RHJA, Morgan AW, and Tsoumpas C
- Subjects
- Humans, Fluorodeoxyglucose F18, Radiopharmaceuticals, ROC Curve, Positron Emission Tomography Computed Tomography methods, Aortitis
- Abstract
The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
- Published
- 2023
- Full Text
- View/download PDF
39. 18 F-FDG PET-CT in paediatric oncology: established and emerging applications.
- Author
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Chambers G, Frood R, Patel C, and Scarsbrook A
- Subjects
- Adolescent, Child, Fluorodeoxyglucose F18, Humans, Medical Oncology, Neoplasm Staging methods, Pediatrics, Radiopharmaceuticals, Neoplasms diagnostic imaging, Positron Emission Tomography Computed Tomography methods
- Abstract
Accurate staging and response assessment is vital in the management of childhood malignancies. Fluorine-18 fluorodeoxyglucose positron emission tomography/CT (FDG PET-CT) provides complimentary anatomical and functional information. Oncological applications of FDG PET-CT are not as well-established within the paediatric population compared to adults. This article will comprehensively review established oncological PET-CT applications in paediatric oncology and provide an overview of emerging and future developments in this domain.
- Published
- 2019
- Full Text
- View/download PDF
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