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1. Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement

Author

Yuan, Shuaiyu, Whitmarsh, Tristan, Kessler, Dimitri A, Arponen, Otso, McLean, Mary A, Baxter, Gabrielle, Riemer, Frank, Kennerley, Aneurin J, Brackenbury, William J, Gilbert, Fiona J, and Kaggie, Joshua D

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, I.4.3
Abstract

Sodium MRI is an imaging technique used to visualize and quantify sodium concentrations in vivo, playing a role in many biological processes and potentially aiding in breast cancer characterization. Sodium MRI, however, suffers from inherently low signal-to-noise ratios (SNR) and spatial resolution, compared with conventional proton MRI. A deep-learning method, the Denoising Diffusion Probabilistic Models (DDPM), has demonstrated success across a wide range of denoising tasks, yet struggles with sodium MRI's unique noise profile, as DDPM primarily targets Gaussian noise. DDPM can distort features when applied to sodium MRI. This paper advances the DDPM by introducing the Rician Denoising Diffusion Probabilistic Models (RDDPM) for sodium MRI denoising. RDDPM converts Rician noise to Gaussian noise at each timestep during the denoising process. The model's performance is evaluated using three non-reference image quality assessment metrics, where RDDPM consistently outperforms DDPM and other CNN-based denoising methods., Comment: 3 figures

Published
2024

2. A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising

Author

Yuan, Shuaiyu, Whitmarsh, Tristan, Kessler, Dimitri A, Arponen, Otso, McLean, Mary A, Baxter, Gabrielle, Riemer, Frank, Kennerley, Aneurin J, Brackenbury, William J, Gilbert, Fiona J, and Kaggie, Joshua D

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

New multinuclear MRI techniques, such as sodium MRI, generally suffer from low image quality due to an inherently low signal. Postprocessing methods, such as image denoising, have been developed for image enhancement. However, the assessment of these enhanced images is challenging especially considering when there is a lack of high resolution and high signal images as reference, such as in sodium MRI. No-reference Image Quality Assessment (NR-IQA) metrics are approaches to solve this problem. Existing learning-based NR-IQA metrics rely on labels derived from subjective human opinions or metrics like Signal-to-Noise Ratio (SNR), which are either time-consuming or lack accurate ground truths, resulting in unreliable assessment. We note that deep learning (DL) models have a unique characteristic in that they are specialized to a characteristic training set, meaning that deviations between the input testing data from the training data will reduce prediction accuracy. Therefore, we propose a novel DL-based NR-IQA metric, the Model Specialization Metric (MSM), which does not depend on ground-truth images or labels. MSM measures the difference between the input image and the model's prediction for evaluating the quality of the input image. Experiments conducted on both simulated distorted proton T1-weighted MR images and denoised sodium MR images demonstrate that MSM exhibits a superior evaluation performance on various simulated noises and distortions. MSM also has a substantial agreement with the expert evaluations, achieving an averaged Cohen's Kappa coefficient of 0.6528, outperforming the existing NR-IQA metrics., Comment: 13 pages, 3 figures

Published
2024

5. Preoperative breast MRI positively impacts surgical outcomes of needle biopsy–diagnosed pure DCIS: a patient-matched analysis from the MIPA study

Author

Cozzi, Andrea, Di Leo, Giovanni, Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Álvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, de Lima Docema, Marcos F., Depretto, Catherine, Dominelli, Valeria, Forrai, Gábor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B. I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge-Marie, Aksoy Ozcan, Umit, Pediconi, Federica, Pinker, Katja, Preibsch, Heike, Raya Povedano, José L., Rossi Saccarelli, Carolina, Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schlooz, Margrethe, Szabó, Botond K., Taylor, Donna B., Ulus, Sila Ö., Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, and Sardanelli, Francesco

Published
2024
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6. Risk stratification in breast screening workshop

Author

Anderson, Andrew, Visintin, Cristina, Antoniou, Antonis, Pashayan, Nora, Gilbert, Fiona J., Hackshaw, Allan, Bhatt, Rikesh, Hill, Harry, Wright, Stuart, Payne, Katherine, Rogers, Gabriel, Shinkins, Bethany, Taylor-Phillips, Sian, and Given-Wilson, Rosalind

Published
2024
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7. MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening

Author

Yang, Yijun, Wang, Shujun, Liu, Lihao, Hickman, Sarah, Gilbert, Fiona J, Schönlieb, Carola-Bibiane, and Aviles-Rivero, Angelica I.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Breast cancer is a major cause of cancer death among women, emphasising the importance of early detection for improved treatment outcomes and quality of life. Mammography, the primary diagnostic imaging test, poses challenges due to the high variability and patterns in mammograms. Double reading of mammograms is recommended in many screening programs to improve diagnostic accuracy but increases radiologists' workload. Researchers explore Machine Learning models to support expert decision-making. Stand-alone models have shown comparable or superior performance to radiologists, but some studies note decreased sensitivity with multiple datasets, indicating the need for high generalisation and robustness models. This work devises MammoDG, a novel deep-learning framework for generalisable and reliable analysis of cross-domain multi-center mammography data. MammoDG leverages multi-view mammograms and a novel contrastive mechanism to enhance generalisation capabilities. Extensive validation demonstrates MammoDG's superiority, highlighting the critical importance of domain generalisation for trustworthy mammography analysis in imaging protocol variations.

Published
2023

13. Screening and diagnostic breast MRI: how do they impact surgical treatment? Insights from the MIPA study

Author

Cozzi, Andrea, Di Leo, Giovanni, Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Álvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, de Lima Docema, Marcos F., Depretto, Catherine, Dominelli, Valeria, Forrai, Gábor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B. I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge-Marie, Ozcan, Umit A., Pediconi, Federica, Pinker, Katja, Preibsch, Heike, Raya Povedano, José L., Rossi Saccarelli, Carolina, Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schlooz, Margrethe, Szabó, Botond K., Taylor, Donna B., Ulus, Özden S., Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, and Sardanelli, Francesco

Published
2023
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14. European Society of Breast Imaging (EUSOBI) guidelines on the management of axillary lymphadenopathy after COVID-19 vaccination: 2023 revision

Author

Schiaffino, Simone, Pinker, Katja, Cozzi, Andrea, Magni, Veronica, Athanasiou, Alexandra, Baltzer, Pascal A. T., Camps Herrero, Julia, Clauser, Paola, Fallenberg, Eva M., Forrai, Gabor, Fuchsjäger, Michael H., Gilbert, Fiona J., Helbich, Thomas, Kilburn-Toppin, Fleur, Kuhl, Christiane K., Lesaru, Mihai, Mann, Ritse M., Panizza, Pietro, Pediconi, Federica, Sardanelli, Francesco, Sella, Tamar, Thomassin-Naggara, Isabelle, Zackrisson, Sophia, and Pijnappel, Ruud M.

Published
2023
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15. Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations

Author

Wei, Tao, Aviles-Rivero, Angelica I, Wang, Shuo, Huang, Yuan, Gilbert, Fiona J, Schönlieb, Carola-Bibiane, and Chen, Chang Wen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The task of classifying mammograms is very challenging because the lesion is usually small in the high resolution image. The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning. However, there are fundamental differences between natural images and medical images, which based on existing evidence from the literature, limits the overall performance gain when designed with algorithmic approaches. In this paper, we propose to go beyond fine-tuning by introducing a novel framework called MorphHR, in which we highlight a new transfer learning scheme. The idea behind the proposed framework is to integrate function-preserving transformations, for any continuous non-linear activation neurons, to internally regularise the network for improving mammograms classification. The proposed solution offers two major advantages over the existing techniques. Firstly and unlike fine-tuning, the proposed approach allows for modifying not only the last few layers but also several of the first ones on a deep ConvNet. By doing this, we can design the network front to be suitable for learning domain specific features. Secondly, the proposed scheme is scalable to hardware. Therefore, one can fit high resolution images on standard GPU memory. We show that by using high resolution images, one prevents losing relevant information. We demonstrate, through numerical and visual experiments, that the proposed approach yields to a significant improvement in the classification performance over state-of-the-art techniques, and is indeed on a par with radiology experts. Moreover and for generalisation purposes, we show the effectiveness of the proposed learning scheme on another large dataset, the ChestX-ray14, surpassing current state-of-the-art techniques., Comment: 10 pages, 5 figures

Published
2021

16. Federated learning for predicting clinical outcomes in patients with COVID-19

Author

Dayan, Ittai, Roth, Holger R, Zhong, Aoxiao, Harouni, Ahmed, Gentili, Amilcare, Abidin, Anas Z, Liu, Andrew, Costa, Anthony Beardsworth, Wood, Bradford J, Tsai, Chien-Sung, Wang, Chih-Hung, Hsu, Chun-Nan, Lee, CK, Ruan, Peiying, Xu, Daguang, Wu, Dufan, Huang, Eddie, Kitamura, Felipe Campos, Lacey, Griffin, de Antônio Corradi, Gustavo César, Nino, Gustavo, Shin, Hao-Hsin, Obinata, Hirofumi, Ren, Hui, Crane, Jason C, Tetreault, Jesse, Guan, Jiahui, Garrett, John W, Kaggie, Joshua D, Park, Jung Gil, Dreyer, Keith, Juluru, Krishna, Kersten, Kristopher, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, Linguraru, Marius George, Haider, Masoom A, AbdelMaseeh, Meena, Rieke, Nicola, Damasceno, Pablo F, e Silva, Pedro Mario Cruz, Wang, Pochuan, Xu, Sheng, Kawano, Shuichi, Sriswasdi, Sira, Park, Soo Young, Grist, Thomas M, Buch, Varun, Jantarabenjakul, Watsamon, Wang, Weichung, Tak, Won Young, Li, Xiang, Lin, Xihong, Kwon, Young Joon, Quraini, Abood, Feng, Andrew, Priest, Andrew N, Turkbey, Baris, Glicksberg, Benjamin, Bizzo, Bernardo, Kim, Byung Seok, Tor-Díez, Carlos, Lee, Chia-Cheng, Hsu, Chia-Jung, Lin, Chin, Lai, Chiu-Ling, Hess, Christopher P, Compas, Colin, Bhatia, Deepeksha, Oermann, Eric K, Leibovitz, Evan, Sasaki, Hisashi, Mori, Hitoshi, Yang, Isaac, Sohn, Jae Ho, Murthy, Krishna Nand Keshava, Fu, Li-Chen, de Mendonça, Matheus Ribeiro Furtado, Fralick, Mike, Kang, Min Kyu, Adil, Mohammad, Gangai, Natalie, Vateekul, Peerapon, Elnajjar, Pierre, Hickman, Sarah, Majumdar, Sharmila, McLeod, Shelley L, Reed, Sheridan, Gräf, Stefan, Harmon, Stephanie, Kodama, Tatsuya, Puthanakit, Thanyawee, Mazzulli, Tony, de Lavor, Vitor Lima, Rakvongthai, Yothin, Lee, Yu Rim, Wen, Yuhong, Gilbert, Fiona J, Flores, Mona G, and Li, Quanzheng

Subjects
Patient Safety, Good Health and Well Being, COVID-19, Electronic Health Records, Humans, Machine Learning, Outcome Assessment, Health Care, Prognosis, SARS-CoV-2, Medical and Health Sciences, Immunology
Abstract

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Published
2021

17. A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption

Author

Assi, Hisham, Cao, Rui, Castelino, Madhura, Cox, Ben, Gilbert, Fiona J., Gröhl, Janek, Gurusamy, Kurinchi, Hacker, Lina, Ivory, Aoife M., Joseph, James, Knieling, Ferdinand, Leahy, Martin J., Lilaj, Ledia, Manohar, Srirang, Meglinski, Igor, Moran, Carmel, Murray, Andrea, Oraevsky, Alexander A., Pagel, Mark D., Pramanik, Manojit, Raymond, Jason, Singh, Mithun Kuniyil Ajith, Vogt, William C., Wang, Lihong, Yang, Shufan, Members of IPASC, and Bohndiek, Sarah E.

Published
2023
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19. List of Contributors

Author

Amornsiripanitch, Nita, primary, Anaby, Debbie, additional, Arango-Lievano, Margarita, additional, Baltzer, Pascal A.T., additional, Bathen, Tone Frost, additional, Bauer, Ethan Henry, additional, Baxter, Gabrielle C., additional, Bayram, Ersin, additional, Benkert, Thomas, additional, Bildhauer, Petra, additional, Bitencourt, Almir, additional, Boutelier, Timothé, additional, Brun, Lucile, additional, Bucciarelli, Brianna, additional, Campana, Sophie, additional, Chenevert, Thomas L., additional, Daniel, Bruce L., additional, Davis, Adam J., additional, Eskreis-Winkler, Sarah, additional, Feret, Florence, additional, Furman-Haran, Edna, additional, Geerts, Liesbeth, additional, Gibbs, Peter, additional, Gilbert, Fiona J., additional, Grimm, Robert, additional, Hargreaves, Brian, additional, Heacock, Laura, additional, Hermoso, Aurélia, additional, Honda, Maya, additional, Hylton, Nola M., additional, Iima, Mami, additional, Jerome, Neil Peter, additional, Kataoka, Masako, additional, Kazama, Toshiki, additional, Kita, Miho, additional, Kwee, Thomas, additional, Le Bihan, Denis, additional, Li, Wen, additional, Liu, Wei, additional, Lo Gullo, Roberto, additional, Malyarenko, Dariya, additional, Mann, Ritse, additional, McKay, Jessica A., additional, Mitulescu, Anca, additional, Moon, Woo Kyung, additional, Moran, Catherine J., additional, Moy, Linda, additional, Neelavalli, Jaladhar, additional, Nissan, Noam, additional, Partridge, Savannah C., additional, Patterson, Andrew J., additional, Peeters, Johannes M., additional, Pinker, Katja, additional, Reig, Beatriu, additional, Rubie, Ilse, additional, Shimakawa, Ann, additional, Shin, Hee Jung, additional, Sigmund, Eric E., additional, Sklair-Levy, Miri, additional, Takahara, Taro, additional, Thakur, Sunitha B., additional, Thoermer, Gregor, additional, Weiland, Elisabeth, additional, Wilmes, Lisa J., additional, and Woitek, Ramona, additional

Published
2023
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21. Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging

Author

Lucy Brindle, Anindo Banerjee, Callister, Matthew, Clegg, Andrew, Cook, Andrew, Cozens, Kelly, Crosbie, Philip, Dizdarevic, Sabina, Eaton, Rosemary, Eichhorst, Kathrin, Frew, Anthony, Groves, Ashley, Han, Sai, Jones, Jeremy, Kankam, Osie, Karunasaagarar, Kavitasagary, Kurban, Lutfi, Little, Louisa, Madden, Jackie, McClement, Chris, Miles, Ken, Moate, Patricia, Peebles, Charles, Pike, Lucy, Poon, Fat-Wui, Sinclair, Donald, Shah, Andrew, Vale, Luke, George, Steve, Riley, Richard, Lodge, Andrea, Buscombe, John, Green, Theresa, Stone, Amanda, Navani, Neal, Shortman, Robert, Azzopardi, Gabriella, Doffman, Sarah, Bush, Janice, Lyttle, Jane, Jacob, Kenneth, Horst, Joris van der, Sarvesvaran, Joseph, McLaren, Barbara, Gomersall, Lesley, Sharma, Ravi, Collie, Kathleen, O’Hickey, Steve, Tyler, Jayne, King, Sue, O’Brien, John, Srivastava, Rajiv, Lloyd-Jones, Hugh, Beech, Sandra, Scarsbrook, Andrew, Ashford-Turner, Victoria, Smith, Elaine, Mbale, Susan, Adams, Nick, Pottinger, Gail, Weir-McCall, Jonathan R., Debruyn, Elise, Harris, Scott, Qureshi, Nagmi R., Rintoul, Robert C., Gleeson, Fergus V., and Gilbert, Fiona J.

Published
2023
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22. Somatostatin Receptor PET/MR Imaging of Inflammation in Patients With Large Vessel Vasculitis and Atherosclerosis

Author

Ćorović, Andrej, Wall, Christopher, Nus, Meritxell, Gopalan, Deepa, Huang, Yuan, Imaz, Maria, Zulcinski, Michal, Peverelli, Marta, Uryga, Anna, Lambert, Jordi, Bressan, Dario, Maughan, Robert T., Pericleous, Charis, Dubash, Suraiya, Jordan, Natasha, Jayne, David R., Hoole, Stephen P., Calvert, Patrick A., Dean, Andrew F., Rassl, Doris, Barwick, Tara, Iles, Mark, Frontini, Mattia, Hannon, Greg, Manavaki, Roido, Fryer, Tim D., Aloj, Luigi, Graves, Martin J., Gilbert, Fiona J., Dweck, Marc R., Newby, David E., Fayad, Zahi A., Reynolds, Gary, Morgan, Ann W., Aboagye, Eric O., Davenport, Anthony P., Jørgensen, Helle F., Mallat, Ziad, Bennett, Martin R., Peters, James E., Rudd, James H.F., Mason, Justin C., and Tarkin, Jason M.

Published
2023
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24. Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI)

Author

Mann, Ritse M., Athanasiou, Alexandra, Baltzer, Pascal A. T., Camps-Herrero, Julia, Clauser, Paola, Fallenberg, Eva M., Forrai, Gabor, Fuchsjäger, Michael H., Helbich, Thomas H., Killburn-Toppin, Fleur, Lesaru, Mihai, Panizza, Pietro, Pediconi, Federica, Pijnappel, Ruud M., Pinker, Katja, Sardanelli, Francesco, Sella, Tamar, Thomassin-Naggara, Isabelle, Zackrisson, Sophia, Gilbert, Fiona J., and Kuhl, Christiane K.

Published
2022
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25. MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis

Author

Kyono, Trent, Gilbert, Fiona J., and van der Schaar, Mihaela

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

With an aging and growing population, the number of women requiring either screening or symptomatic mammograms is increasing. To reduce the number of mammograms that need to be read by a radiologist while keeping the diagnostic accuracy the same or better than current clinical practice, we develop Man and Machine Mammography Oracle (MAMMO) - a clinical decision support system capable of triaging mammograms into those that can be confidently classified by a machine and those that cannot be, thus requiring the reading of a radiologist. The first component of MAMMO is a novel multi-view convolutional neural network (CNN) with multi-task learning (MTL). MTL enables the CNN to learn the radiological assessments known to be associated with cancer, such as breast density, conspicuity, suspicion, etc., in addition to learning the primary task of cancer diagnosis. We show that MTL has two advantages: 1) learning refined feature representations associated with cancer improves the classification performance of the diagnosis task and 2) issuing radiological assessments provides an additional layer of model interpretability that a radiologist can use to debug and scrutinize the diagnoses provided by the CNN. The second component of MAMMO is a triage network, which takes as input the radiological assessment and diagnostic predictions of the first network's MTL outputs and determines which mammograms can be correctly and confidently diagnosed by the CNN and which mammograms cannot, thus needing to be read by a radiologist. Results obtained on a private dataset of 8,162 patients show that MAMMO reduced the number of radiologist readings by 42.8% while improving the overall diagnostic accuracy in comparison to readings done by radiologists alone. We analyze the triage of patients decided by MAMMO to gain a better understanding of what unique mammogram characteristics require radiologists' expertise., Comment: 18 pages, 7 figures

Published
2018

28. Magnetic resonance imaging before breast cancer surgery: results of an observational multicenter international prospective analysis (MIPA)

Author

Sardanelli, Francesco, Trimboli, Rubina M., Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Álvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, Cozzi, Andrea, de Andrade, Danúbia A., de Lima Docema, Marcos F., Depretto, Catherine, Dominelli, Valeria, Forrai, Gábor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B. I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge-Marie, Ozcan, Umit A., Pediconi, Federica, Pinker, Katja, Preibsch, Heike, Raya Povedano, José L., Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schiaffino, Simone, Schlooz, Margrethe, Szabó, Botond K., Taylor, Donna B., Ulus, Özden S., Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, and Di Leo, Giovanni

Published
2022
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29. Study protocol comparing the ethical, psychological and socio-economic impact of personalised breast cancer screening to that of standard screening in the “My Personal Breast Screening” (MyPeBS) randomised clinical trial

Author

Roux, Alexandra, Cholerton, Rachel, Sicsic, Jonathan, Moumjid, Nora, French, David P., Giorgi Rossi, Paolo, Balleyguier, Corinne, Guindy, Michal, Gilbert, Fiona J., Burrion, Jean-Benoit, Castells, Xavier, Ritchie, David, Keatley, Debbie, Baron, Camille, Delaloge, Suzette, and de Montgolfier, Sandrine

Published
2022
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32. Imaging breast cancer using hyperpolarized carbon-13 MRI

Author

Gallagher, Ferdia A., Woitek, Ramona, McLean, Mary A., Gill, Andrew B., Garcia, Raquel Manzano, Provenzano, Elena, Riemer, Frank, Kaggie, Joshua, Chhabra, Anita, Ursprung, Stephan, Grist, James T., Daniels, Charlie J., Zaccagna, Fulvio, Laurent, Marie-Christine, Locke, Matthew, Hilborne, Sarah, Frary, Amy, Torheim, Turid, Boursnell, Chris, Schiller, Amy, Patterson, Ilse, Slough, Rhys, Carmo, Bruno, Kane, Justine, Biggs, Heather, Harrison, Emma, Deen, Surrin S., Patterson, Andrew, Lanz, Titus, Kingsbury, Zoya, Ross, Mark, Basu, Bristi, Baird, Richard, Lomas, David J., Sala, Evis, Wason, James, Rueda, Oscar M., Chin, Suet-Feung, Wilkinson, Ian B., Graves, Martin J., Abraham, Jean E., Gilbert, Fiona J., Caldas, Carlos, and Brindle, Kevin M.

Published
2020

37. Preoperative breast MRI positively impacts surgical outcomes of needle biopsy–diagnosed pure DCIS:a patient-matched analysis from the MIPA study

Author

Cozzi, Andrea, Di Leo, Giovanni, Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Álvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, de Lima Docema, Marcos F., Depretto, Catherine, Dominelli, Valeria, Forrai, Gábor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B.I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge Marie, Aksoy Ozcan, Umit, Pediconi, Federica, Pinker, Katja, Preibsch, Heike, Raya Povedano, José L., Rossi Saccarelli, Carolina, Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schlooz, Margrethe, Szabó, Botond K., Taylor, Donna B., Ulus, Sila, Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, Sardanelli, Francesco, Cozzi, Andrea, Di Leo, Giovanni, Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Álvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, de Lima Docema, Marcos F., Depretto, Catherine, Dominelli, Valeria, Forrai, Gábor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B.I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge Marie, Aksoy Ozcan, Umit, Pediconi, Federica, Pinker, Katja, Preibsch, Heike, Raya Povedano, José L., Rossi Saccarelli, Carolina, Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schlooz, Margrethe, Szabó, Botond K., Taylor, Donna B., Ulus, Sila, Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, and Sardanelli, Francesco

Abstract

Objectives: To investigate the influence of preoperative breast MRI on mastectomy and reoperation rates in patients with pure ductal carcinoma in situ (DCIS). Methods: The MIPA observational study database (7245 patients) was searched for patients aged 18–80 years with pure unilateral DCIS diagnosed at core needle or vacuum-assisted biopsy (CNB/VAB) and planned for primary surgery. Patients who underwent preoperative MRI (MRI group) were matched (1:1) to those who did not receive MRI (noMRI group) according to 8 confounding covariates that drive referral to MRI (age; hormonal status; familial risk; posterior-to-nipple diameter; BI-RADS category; lesion diameter; lesion presentation; surgical planning at conventional imaging). Surgical outcomes were compared between the matched groups with nonparametric statistics after calculating odds ratios (ORs). Results: Of 1005 women with pure unilateral DCIS at CNB/VAB (507 MRI group, 498 noMRI group), 309 remained in each group after matching. First-line mastectomy rate in the MRI group was 20.1% (62/309 patients, OR 2.03) compared to 11.0% in the noMRI group (34/309 patients, p = 0.003). The reoperation rate was 10.0% in the MRI group (31/309, OR for reoperation 0.40) and 22.0% in the noMRI group (68/309, p < 0.001), with a 2.53 OR of avoiding reoperation in the MRI group. The overall mastectomy rate was 23.3% in the MRI group (72/309, OR 1.40) and 17.8% in the noMRI group (55/309, p = 0.111). Conclusions: Compared to those going directly to surgery, patients with pure DCIS at CNB/VAB who underwent preoperative MRI had a higher OR for first-line mastectomy but a substantially lower OR for reoperation. Clinical relevance statement: When confounding factors behind MRI referral are accounted for in the comparison of patients with CNB/VAB-diagnosed pure unilateral DCIS, preoperative MRI yields a reduction of reoperations that is more than twice as high as the increase in overall mastectomies. Key Points: • Confounding fact

Published
2024

39. 23Na MRI: inter-reader reproducibility of normal fibroglandular sodium concentration measurements at 3 T.

Author

Arponen, Otso, McLean, Mary A., Nanaa, Muzna, Manavaki, Roido, Baxter, Gabrielle C., Gill, Andrew B., Riemer, Frank, Kennerley, Aneurin J., Woitek, Ramona, Kaggie, Joshua D., Brackenbury, William J., and Gilbert, Fiona J.

Subjects
MAGNETIC resonance imaging, SODIUM, DIFFUSION coefficients
Abstract

Background: To study the reproducibility of 23Na magnetic resonance imaging (MRI) measurements from breast tissue in healthy volunteers. Methods: Using a dual-tuned bilateral 23Na/1H breast coil at 3-T MRI, high-resolution 23Na MRI three-dimensional cones sequences were used to quantify total sodium concentration (TSC) and fluid-attenuated sodium concentration (FASC). B1-corrected TSC and FASC maps were created. Two readers manually measured mean, minimum and maximum TSC and mean FASC values using two sampling methods: large regions of interest (LROIs) and small regions of interest (SROIs) encompassing fibroglandular tissue (FGT) and the highest signal area at the level of the nipple, respectively. The reproducibility of the measurements and correlations between density, age and FGT apparent diffusion coefficient (ADC) values were evaluatedss. Results: Nine healthy volunteers were included. The inter-reader reproducibility of TSC and FASC using SROIs and LROIs was excellent (intraclass coefficient range 0.945−0.979, p < 0.001), except for the minimum TSC LROI measurements (p = 0.369). The mean/minimum LROI TSC and mean LROI FASC values were lower than the respective SROI values (p < 0.001); the maximum LROI TSC values were higher than the SROI TSC values (p = 0.009). TSC correlated inversely with age but not with FGT ADCs. The mean and maximum FGT TSC and FASC values were higher in dense breasts in comparison to non-dense breasts (p < 0.020). Conclusions: The chosen sampling method and the selected descriptive value affect the measured TSC and FASC values, although the inter-reader reproducibility of the measurements is in general excellent. Relevance statement: 23Na MRI at 3 T allows the quantification of TSC and FASC sodium concentrations. The sodium measurements should be obtained consistently in a uniform manner. Key points: • 23Na MRI allows the quantification of total and fluid-attenuated sodium concentrations (TSC/FASC). • Sampling method (large/small region of interest) affects the TSC and FASC values. • Dense breasts have higher TSC and FASC values than non-dense breasts. • The inter-reader reproducibility of TSC and FASC measurements was, in general, excellent. • The results suggest the importance of stratifying the sodium measurements protocol. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Axillary lymphadenopathy at the time of COVID-19 vaccination: ten recommendations from the European Society of Breast Imaging (EUSOBI)

Author

Schiaffino, Simone, Pinker, Katja, Magni, Veronica, Cozzi, Andrea, Athanasiou, Alexandra, Baltzer, Pascal A. T., Camps Herrero, Julia, Clauser, Paola, Fallenberg, Eva M., Forrai, Gábor, Fuchsjäger, Michael H., Helbich, Thomas H., Kilburn-Toppin, Fleur, Kuhl, Christiane K., Lesaru, Mihai, Mann, Ritse M., Panizza, Pietro, Pediconi, Federica, Pijnappel, Ruud M., Sella, Tamar, Thomassin-Naggara, Isabelle, Zackrisson, Sophia, Gilbert, Fiona J., and Sardanelli, Francesco

Published
2021
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44. Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

Author

Le, Elizabeth P. V., Rundo, Leonardo, Tarkin, Jason M., Evans, Nicholas R., Chowdhury, Mohammed M., Coughlin, Patrick A., Pavey, Holly, Wall, Chris, Zaccagna, Fulvio, Gallagher, Ferdia A., Huang, Yuan, Sriranjan, Rouchelle, Le, Anthony, Weir-McCall, Jonathan R., Roberts, Michael, Gilbert, Fiona J., Warburton, Elizabeth A., Schönlieb, Carola-Bibiane, Sala, Evis, and Rudd, James H. F.

Published
2021
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47. Solving the preoperative breast MRI conundrum: design and protocol of the MIPA study

Author

Sardanelli, Francesco, Trimboli, Rubina M., Houssami, Nehmat, Gilbert, Fiona J., Helbich, Thomas H., Alvarez Benito, Marina, Balleyguier, Corinne, Bazzocchi, Massimo, Bult, Peter, Calabrese, Massimo, Camps Herrero, Julia, Cartia, Francesco, Cassano, Enrico, Clauser, Paola, de Andrade, Danubia A., de Lima Docema, Marcos F., Depretto, Catherine, Forrai, Gabor, Girometti, Rossano, Harms, Steven E., Hilborne, Sarah, Ienzi, Raffaele, Lobbes, Marc B. I., Losio, Claudio, Mann, Ritse M., Montemezzi, Stefania, Obdeijn, Inge-Marie, Ozcan, Umit Aksoy, Pediconi, Federica, Preibsch, Heike, Raya-Povedano, José L., Sacchetto, Daniela, Scaperrotta, Gianfranco P., Schlooz, Margrethe, Szabo, Botond K., Ulus, Ozden S., Taylor, Donna B., Van Goethem, Mireille, Veltman, Jeroen, Weigel, Stefanie, Wenkel, Evelyn, Zuiani, Chiara, and Di Leo, Giovanni

Published
2020
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49. International evaluation of an AI system for breast cancer screening

Author

McKinney, Scott Mayer, Sieniek, Marcin, Godbole, Varun, Godwin, Jonathan, Antropova, Natasha, Ashrafian, Hutan, Back, Trevor, Chesus, Mary, Corrado, Greg S., Darzi, Ara, Etemadi, Mozziyar, Garcia-Vicente, Florencia, Gilbert, Fiona J., Halling-Brown, Mark, Hassabis, Demis, Jansen, Sunny, Karthikesalingam, Alan, Kelly, Christopher J., King, Dominic, Ledsam, Joseph R., Melnick, David, Mostofi, Hormuz, Peng, Lily, Reicher, Joshua Jay, Romera-Paredes, Bernardino, Sidebottom, Richard, Suleyman, Mustafa, Tse, Daniel, Young, Kenneth C., De Fauw, Jeffrey, and Shetty, Shravya

Published
2020
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50. A method for exploratory repeated-measures analysis applied to a breast-cancer screening study

Author

Brentnall, Adam R., Duffy, Stephen W., Crowder, Martin J., Gillan, Maureen G. C., Astley, Susan M., Wallis, Matthew G., James, Jonathan, Boggis, Caroline R. M., and Gilbert, Fiona J.

Subjects
Statistics - Applications
Abstract

When a model may be fitted separately to each individual statistical unit, inspection of the point estimates may help the statistician to understand between-individual variability and to identify possible relationships. However, some information will be lost in such an approach because estimation uncertainty is disregarded. We present a comparative method for exploratory repeated-measures analysis to complement the point estimates that was motivated by and is demonstrated by analysis of data from the CADET II breast-cancer screening study. The approach helped to flag up some unusual reader behavior, to assess differences in performance, and to identify potential random-effects models for further analysis., Comment: Published in at http://dx.doi.org/10.1214/11-AOAS481 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2012
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