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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

7. Misdiagnosis in breast imaging: a statement paper from European Society Breast Imaging (EUSOBI)—Part 2: Main causes of errors in breast imaging and recommendations from European Society of Breast Imaging to limit misdiagnosis

Author

Thomassin-Naggara, Isabelle, Athanasiou, Alexandra, Kilburn-Toppin, Fleur, Forrai, Gabor, Ispas, Miruna, Lesaru, Mihai, Giannotti, Elisabetta, Pinker-Domenig, Katja, Van Ongeval, Chantal, Mann, Ritse M., Gilbert, Fiona, and Pediconi, Federica

Published
2024
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11. 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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12. 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. 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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14. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis.

Author

Yoon, Jung, Strand, Fredrik, Baltzer, Pascal, Conant, Emily, Gilbert, Fiona, Lehman, Constance, Mullen, Lisa, Nishikawa, Robert, Sharma, Nisha, Vejborg, Ilse, Moy, Linda, Mann, Ritse, and Morris, Elizabeth

Subjects
Female, Humans, Artificial Intelligence, Breast Neoplasms, Early Detection of Cancer, Mammography, Breast, Retrospective Studies
Abstract

Background There is considerable interest in the potential use of artificial intelligence (AI) systems in mammographic screening. However, it is essential to critically evaluate the performance of AI before it can become a modality used for independent mammographic interpretation. Purpose To evaluate the reported standalone performances of AI for interpretation of digital mammography and digital breast tomosynthesis (DBT). Materials and Methods A systematic search was conducted in PubMed, Google Scholar, Embase (Ovid), and Web of Science databases for studies published from January 2017 to June 2022. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) values were reviewed. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Comparative (QUADAS-2 and QUADAS-C, respectively). A random effects meta-analysis and meta-regression analysis were performed for overall studies and for different study types (reader studies vs historic cohort studies) and imaging techniques (digital mammography vs DBT). Results In total, 16 studies that include 1 108 328 examinations in 497 091 women were analyzed (six reader studies, seven historic cohort studies on digital mammography, and four studies on DBT). Pooled AUCs were significantly higher for standalone AI than radiologists in the six reader studies on digital mammography (0.87 vs 0.81, P = .002), but not for historic cohort studies (0.89 vs 0.96, P = .152). Four studies on DBT showed significantly higher AUCs in AI compared with radiologists (0.90 vs 0.79, P < .001). Higher sensitivity and lower specificity were seen for standalone AI compared with radiologists. Conclusion Standalone AI for screening digital mammography performed as well as or better than radiologists. Compared with digital mammography, there is an insufficient number of studies to assess the performance of AI systems in the interpretation of DBT screening examinations. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Scaranelo in this issue.

Published
2023

19. 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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20. 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

21. 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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22. 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

23. 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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24. 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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25. 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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27. 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. 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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32. 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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34. Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation.

Author

Whitmarsh, Tristan, Cope, Wei, Carmona-Bozo, Julia, Manavaki, Roido, Sammut, Stephen-John, Woitek, Ramona, Provenzano, Elena, Brown, Emma L., Bohndiek, Sarah E., Gallagher, Ferdia A., Caldas, Carlos, Gilbert, Fiona J., and Markowetz, Florian

Subjects
CONVOLUTIONAL neural networks, VASCULAR smooth muscle, MEDICAL sciences, TUMOR microenvironment, DEEP learning
Abstract

Background: Tumour vascular density assessed from CD-31 immunohistochemistry (IHC) images has previously been shown to have prognostic value in breast cancer. Current methods to measure vascular density, however, are time-consuming, suffer from high inter-observer variability and are limited in describing the complex tumour vasculature morphometry. Methods: We propose a method for automatically measuring a range of vascular parameters from CD-31 IHC images, which together provide a detailed description of the vasculature morphology. We first used a U-Net based convolutional neural network, trained and validated using 36 partially annotated whole slide images from 27 patients, to segment vessel structures and tumour regions from which the measurements are taken. The model also segments the vascular smooth muscle, benign epithelium, adipose tissue, stroma, lymphocyte clusters, nerves and CD-31 positive leukocytes, and we applied it to an additional 21 images from 15 patients. Using these segmentations, we investigated the relationship between the various tissue types and the vasculature and studied the relationship of various vascular parameters with clinical parameters. We also performed a 3D histology analysis on a separate tumour sample as a proof of principle, providing a more comprehensive visualization of vasculature morphology compared to the standard 2D cross-section of a tissue sample. Results: Using two-way cross-validation, we show that vessels were accurately segmented, with Dice scores of 0.875 and 0.856, and were accurately identified, with F1 scores of 0.777 and 0.748. All vascular parameters exhibit strong ( r > 0.7 ) and significant (p<0.001) correlations with measurements taken from the manual ground truth vessel segmentations. A significant relationship between the major/minor axis ratio, a measure of elongation, and the tumour grade was found. Conclusion: Our proposed method shows promise as a tool for studying the tumour vasculature and its relationship with surrounding cells and tissue types. Furthermore, the correlation with tumour grade highlights the clinical relevance of our approach. These findings suggest that our method could have substantial implications for improving prognostic assessments and personalizing therapeutic strategies in breast cancer treatment. [ABSTRACT FROM AUTHOR]

Published
2025
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36. 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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37. 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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39. Diagnostic performance of tomosynthesis plus synthetic mammography versus full‐field digital mammography with or without tomosynthesis in breast cancer screening: A systematic review and meta‐analysis.

Author

Hamad, Wasim, Michell, Michael J., Myles, Jonathan P., Gilbert, Fiona J., Chen, Yan, Jin, Huajie, Loveland, John, Halling‐Brown, Mark, Satchithananda, Keshthra, Morel, Juliet, Wasan, Rema, Taylor, Caroline, Sharma, Nisha, Valencia, Alexandra, Teh, Will, Majid, Faisal, De Visser, Ronald M, Iqbal, Asif, and Duffy, Stephen W.

Subjects
TOMOSYNTHESIS, DIGITAL mammography, MEDICAL screening, DIAGNOSTIC imaging, EARLY detection of cancer
Abstract

Digital breast tomosynthesis (DBT) with full‐field digital mammography (FFDM) exposes women to a higher radiation dose. A synthetic 2D mammogram (S2D) is a two‐dimensional image constructed from DBT. We aim to evaluate the S2D performance when used alone or combined with DBT compared to FFDM alone or with DBT. Studies were included if they recruited screening participants and reported on S2D performance. Studies were excluded if they included symptomatic patients, imaging was for diagnostic purposes, or if participants had a breast cancer history. Meta‐analyses for cancer detection rates (CDR) and Specificities were conducted where available. Differences in the performance of imaging modalities were calculated within individual studies, and these were pooled by meta‐analysis. Out of 3241 records identified, 17 studies were included in the review and 13 in the meta‐analysis. The estimated combined difference in CDRs per thousand among individual studies that reported on DBT plus S2D vs. FFDM and those reporting on DBT plus S2D versus DBT plus FFDM was 2.03 (95% CI 0.81–3.25) and − 0.15 (95% CI −1.17 to 0.86), respectively. The estimated difference in percent specificities was 1.13 (95% CI −0.06 to 2.31) in studies comparing DBT plus S2D and FFDM. In studies comparing DBT plus S2D and DBT plus FFDM, the estimated difference in specificities was 1.08 (95% CI 0.59–1.56). DBT plus S2D showed comparable accuracy to FFDM plus DPT and improved cancer detection to FFDM alone. Integrating S2D with DBT in breast cancer screening is safe and preserves performance. [ABSTRACT FROM AUTHOR]

Published
2025
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47. 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

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