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18 results on '"Sudre, C. H."'

1. Automated segmentation and characterisation of white matter hyperintensities

Author

Sudre, C. H., Ourselin, S., Fox, N., and Cardoso, M. J.

Subjects
612.8
Abstract

Neuroimaging has enabled the observation of damage to the white matter that occurs frequently in elderly population and is depicted as hyperintensities in specific magnetic resonance images. Since the pathophysiology underlying the existence of these signal abnormalities and the association with clinical risk factors and outcome is still investigated, a robust and accurate quantification and characterisation of these observations is necessary. In this thesis, I developed a data-driven split and merge model selection framework that results in the joint modelling of normal appearing and outlier observations in a hierarchical Gaussian mixture model. The resulting model can then be used to segment white matter hyperintensities (WMH) in a post-processing step. The validity of the method in terms of robustness to data quality, acquisition protocol and preprocessing and its comparison to the state of the art is evaluated in both simulated and clinical settings. To further characterise the lesions, a subject-specific coordinate frame that divides the WM region according to the relative distance between the ventricular surface and the cortical sheet and to the lobar location is introduced. This coordinate frame is used for the comparison of lesion distributions in a population of twin pairs and for the prediction and standardisation of visual rating scales. Lastly the cross-sectional method is extended into a longitudinal framework, in which a Gaussian Mixture model built on an average image is used to constrain the representation of the individual time points. The method is validated through a purpose-build longitudinal lesion simulator and applied to the investigation of the relationship between APOE genetic status and lesion load progression.

Published
2016

2. Why is the Winner the Best?

Author

Eisenmann, M., primary, Reinke, A., additional, Weru, V., additional, Tizabi, M. D., additional, Isensee, F., additional, Adler, T. J., additional, Ali, S., additional, Andrearczyk, V., additional, Aubreville, M., additional, Baid, U., additional, Bakas, S., additional, Balu, N., additional, Bano, S., additional, Bernal, J., additional, Bodenstedt, S., additional, Casella, A., additional, Cheplygina, V., additional, Daum, M., additional, De Bruijne, M., additional, Depeursinge, A., additional, Dorent, R., additional, Egger, J., additional, Ellis, D. G., additional, Engelhardt, S., additional, Ganz, M., additional, Ghatwary, N., additional, Girard, G., additional, Godau, P., additional, Gupta, A., additional, Hansen, L., additional, Harada, K., additional, Heinrich, M., additional, Heller, N., additional, Hering, A., additional, Huaulmé, A., additional, Jannin, P., additional, Kavur, A. E., additional, Kodym, O., additional, Kozubek, M., additional, Li, J., additional, Li, H., additional, Ma, J., additional, Martín-Isla, C., additional, Menze, B., additional, Noble, A., additional, Oreiller, V., additional, Padoy, N., additional, Pati, S., additional, Payette, K., additional, Rädsch, T., additional, Rafael-Patiño, J., additional, Bawa, V. Singh, additional, Speidel, S., additional, Sudre, C. H., additional, Van Wijnen, K., additional, Wagner, M., additional, Wei, D., additional, Yamlahi, A., additional, Yap, M. H., additional, Yuan, C., additional, Zenk, M., additional, Zia, A., additional, Zimmerer, D., additional, Aydogan, D., additional, Bhattarai, B., additional, Bloch, L., additional, Brüngel, R., additional, Cho, J., additional, Choi, C., additional, Dou, Q., additional, Ezhov, I., additional, Friedrich, C. M., additional, Fuller, C., additional, Gaire, R. R., additional, Galdran, A., additional, Faura, Á. García, additional, Grammatikopoulou, M., additional, Hong, S., additional, Jahanifar, M., additional, Jang, I., additional, Kadkhodamohammadi, A., additional, Kang, I., additional, Kofler, F., additional, Kondo, S., additional, Kuijf, H., additional, Li, M., additional, Luu, M., additional, Martinčič, T., additional, Morais, P., additional, Naser, M. A., additional, Oliveira, B., additional, Owen, D., additional, Pang, S., additional, Park, J., additional, Park, S., additional, Płotka, S., additional, Puybareau, E., additional, Rajpoot, N., additional, Ryu, K., additional, Saeed, N., additional, Shephard, A., additional, Shi, P., additional, Štepec, D., additional, Subedi, R., additional, Tochon, G., additional, Torres, H. R., additional, Urien, H., additional, Vilaça, J. L., additional, Wahid, K. A., additional, Wang, H., additional, Wang, J., additional, Wang, L., additional, Wang, X., additional, Wiestler, B., additional, Wodzinski, M., additional, Xia, F., additional, Xie, J., additional, Xiong, Z., additional, Yang, S., additional, Yang, Y., additional, Zhao, Z., additional, Maier-Hein, K., additional, Jäger, P. F., additional, Kopp-Schneider, A., additional, and Maier-Hein, L., additional

Published
2023
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3. Why is the Winner the Best?

Author

Beeldverwerking ISI, Brain, Cancer, Circulatory Health, Structure and Connections, Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., Ali, S., Andrearczyk, V., Aubreville, M., Baid, U., Bakas, S., Balu, N., Bano, S., Bernal, J., Bodenstedt, S., Casella, A., Cheplygina, V., Daum, M., De Bruijne, M., Depeursinge, A., Dorent, R., Egger, J., Ellis, D. G., Engelhardt, S., Ganz, M., Ghatwary, N., Girard, G., Godau, P., Gupta, A., Hansen, L., Harada, K., Heinrich, M., Heller, N., Hering, A., Huaulmé, A., Jannin, P., Kavur, A. E., Kodym, O., Kozubek, M., Li, J., Li, H., Ma, J., Martín-Isla, C., Menze, B., Noble, A., Oreiller, V., Padoy, N., Pati, S., Payette, K., Rädsch, T., Rafael-Patiño, J., Bawa, V. Singh, Speidel, S., Sudre, C. H., Van Wijnen, K., Wagner, M., Wei, D., Yamlahi, A., Yap, M. H., Yuan, C., Zenk, M., Zia, A., Zimmerer, D., Aydogan, D., Bhattarai, B., Bloch, L., Brüngel, R., Cho, J., Choi, C., Dou, Q., Ezhov, I., Friedrich, C. M., Fuller, C., Gaire, R. R., Galdran, A., García Faura, A., Grammatikopoulou, M., Hong, S., Jahanifar, M., Jang, I., Kadkhodamohammadi, A., Kang, I., Kofler, F., Kondo, S., Kuijf, H., Li, M., Luu, M., Martinčič, T., Morais, P., Naser, M. A., Oliveira, B., Owen, D., Pang, S., Park, J., Park, S., Płotka, S., Puybareau, E., Rajpoot, N., Ryu, K., Saeed, N., Shephard, A., Shi, P., Štepec, D., Subedi, R., Tochon, G., Torres, H. R., Urien, H., Vilaça, J. L., Wahid, K. A., Wang, H., Wang, J., Wang, L., Wang, X., Wiestler, B., Wodzinski, M., Xia, F., Xie, J., Xiong, Z., Yang, S., Yang, Y., Zhao, Z., Maier-Hein, K., Jäger, P. F., Kopp-Schneider, A., Maier-Hein, L., Beeldverwerking ISI, Brain, Cancer, Circulatory Health, Structure and Connections, Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., Ali, S., Andrearczyk, V., Aubreville, M., Baid, U., Bakas, S., Balu, N., Bano, S., Bernal, J., Bodenstedt, S., Casella, A., Cheplygina, V., Daum, M., De Bruijne, M., Depeursinge, A., Dorent, R., Egger, J., Ellis, D. G., Engelhardt, S., Ganz, M., Ghatwary, N., Girard, G., Godau, P., Gupta, A., Hansen, L., Harada, K., Heinrich, M., Heller, N., Hering, A., Huaulmé, A., Jannin, P., Kavur, A. E., Kodym, O., Kozubek, M., Li, J., Li, H., Ma, J., Martín-Isla, C., Menze, B., Noble, A., Oreiller, V., Padoy, N., Pati, S., Payette, K., Rädsch, T., Rafael-Patiño, J., Bawa, V. Singh, Speidel, S., Sudre, C. H., Van Wijnen, K., Wagner, M., Wei, D., Yamlahi, A., Yap, M. H., Yuan, C., Zenk, M., Zia, A., Zimmerer, D., Aydogan, D., Bhattarai, B., Bloch, L., Brüngel, R., Cho, J., Choi, C., Dou, Q., Ezhov, I., Friedrich, C. M., Fuller, C., Gaire, R. R., Galdran, A., García Faura, A., Grammatikopoulou, M., Hong, S., Jahanifar, M., Jang, I., Kadkhodamohammadi, A., Kang, I., Kofler, F., Kondo, S., Kuijf, H., Li, M., Luu, M., Martinčič, T., Morais, P., Naser, M. A., Oliveira, B., Owen, D., Pang, S., Park, J., Park, S., Płotka, S., Puybareau, E., Rajpoot, N., Ryu, K., Saeed, N., Shephard, A., Shi, P., Štepec, D., Subedi, R., Tochon, G., Torres, H. R., Urien, H., Vilaça, J. L., Wahid, K. A., Wang, H., Wang, J., Wang, L., Wang, X., Wiestler, B., Wodzinski, M., Xia, F., Xie, J., Xiong, Z., Yang, S., Yang, Y., Zhao, Z., Maier-Hein, K., Jäger, P. F., Kopp-Schneider, A., and Maier-Hein, L.

Published
2023

4. Multimodal MRI-derived phenotypes in preclinical Alzheimer’s Disease: results from the EPAD cohort

Author

Lorenzini, L., Ingala, S., Wink, A. M., Kuijer, J. P. A., Wottschel, V., Sudre, C. H., Haller, S., Molinuevo, J. L., Gispert, J. D., Cash, D. M., Thomas, D. L., Vos, S. B., Ferran, P., (0000-0002-3201-6002) Petr, J., Wolz, R., Palombit, A., Schwarz, A. J., Chételat, G., Payoux, P., Di Perri, C., Pernet, C., Frisoni, G., Fox, N. C., Ritchie, C., Wardlaw, J., Waldman, A., Barkhof, F., Mutsaerts, H. J. M. M., Lorenzini, L., Ingala, S., Wink, A. M., Kuijer, J. P. A., Wottschel, V., Sudre, C. H., Haller, S., Molinuevo, J. L., Gispert, J. D., Cash, D. M., Thomas, D. L., Vos, S. B., Ferran, P., (0000-0002-3201-6002) Petr, J., Wolz, R., Palombit, A., Schwarz, A. J., Chételat, G., Payoux, P., Di Perri, C., Pernet, C., Frisoni, G., Fox, N. C., Ritchie, C., Wardlaw, J., Waldman, A., Barkhof, F., and Mutsaerts, H. J. M. M.

Abstract

Image-derived phenotypes (IDPs) from multimodal MRI sequences constitute an important resource that allows the characterization of brain alterations in the early stages of Alzheimer diseases and other neurodegenerative conditions. Here, we showed the computation of multimodal IDPs from the European Prevention of Alzheimer Dementia (EPAD) cohort and assessed their relationship with non-imaging markers of neurodegeneration. We demonstrated the clinical relevance of IDPs to uncover early brain alteration in AD by showing expected association with non-imaging data.

Published
2022

5. The Open-Access European Prevention of Alzheimer’s Dementia (EPAD) MRI dataset and processing workflow

Author

Lorenzini, L., Ingala, S., Wink, A. M., Kuijer, J. P. A., Wottschel, V., Dijsselhof, M., Sudre, C. H., Haller, S., Molinuevo, J. L., Gispert, J. D., Cash, D. M., Thomas, D. L., Vos, S. B., Prados, F., (0000-0002-3201-6002) Petr, J., Wolz, R., Palombit, A., Schwarz, A. J., Chételat, G., Payoux, P., Di Perri, C., Wardlaw, J. M., Frisoni, G. B., Foley, C., Fox, N. C., Ritchie, C., Pernet, C., Waldman, A., Barkhof, F., Mutsaerts, H. J. M. M., Lorenzini, L., Ingala, S., Wink, A. M., Kuijer, J. P. A., Wottschel, V., Dijsselhof, M., Sudre, C. H., Haller, S., Molinuevo, J. L., Gispert, J. D., Cash, D. M., Thomas, D. L., Vos, S. B., Prados, F., (0000-0002-3201-6002) Petr, J., Wolz, R., Palombit, A., Schwarz, A. J., Chételat, G., Payoux, P., Di Perri, C., Wardlaw, J. M., Frisoni, G. B., Foley, C., Fox, N. C., Ritchie, C., Pernet, C., Waldman, A., Barkhof, F., and Mutsaerts, H. J. M. M.

Abstract

The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer’s Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI pre- processing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC features — i.e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression. IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500.0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features — Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) — were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.

Published
2022

6. Cancer and Risk of COVID-19 Through a General Community Survey

Author

Lee, K. A., Ma, W., Sikavi, D. R., Drew, D. A., Nguyen, L. H., Bowyer, R. C. E., Cardoso, M. J., Fall, Tove, Freidin, M. B., Gomez, M., Graham, M., Guo, C. G., Joshi, A. D., Kwon, S., Lo, C. H., Lochlainn, M. N., Menni, C., Murray, B., Mehta, R., Song, M., Sudre, C. H., Bataille, V., Varsavsky, T., Visconti, A., Franks, P. W., Wolf, J., Steves, C. J., Ourselin, S., Spector, T. D., Chan, A. T., consortium, Cope, Lee, K. A., Ma, W., Sikavi, D. R., Drew, D. A., Nguyen, L. H., Bowyer, R. C. E., Cardoso, M. J., Fall, Tove, Freidin, M. B., Gomez, M., Graham, M., Guo, C. G., Joshi, A. D., Kwon, S., Lo, C. H., Lochlainn, M. N., Menni, C., Murray, B., Mehta, R., Song, M., Sudre, C. H., Bataille, V., Varsavsky, T., Visconti, A., Franks, P. W., Wolf, J., Steves, C. J., Ourselin, S., Spector, T. D., Chan, A. T., and consortium, Cope

Abstract

Individuals with cancer may be at high risk for coronavirus disease 2019 (COVID-19) and adverse outcomes. However, evidence from large population-based studies examining whether cancer and cancer-related therapy exacerbates the risk of COVID-19 infection is still limited. Data were collected from the COVID Symptom Study smartphone application since March 29 through May 8, 2020. Among 23,266 participants with cancer and 1,784,293 without cancer, we documented 10,404 reports of a positive COVID-19 test. Compared with participants without cancer, those living with cancer had a 60% increased risk of a positive COVID-19 test. Among patients with cancer, current treatment with chemotherapy or immunotherapy was associated with a 2.2-fold increased risk of a positive test. The association between cancer and COVID-19 infection was stronger among participants >65 years and males. Future studies are needed to identify subgroups by tumor types and treatment regimens who are particularly at risk for COVID-19 infection and adverse outcomes.

Published
2021
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7. Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application : a prospective, observational study

Author

Varsavsky, T., Graham, M. S., Canas, L. S., Ganesh, S., Capdevila Pujol, J., Sudre, C. H., Murray, B., Modat, M., Jorge Cardoso, M., Astley, C. M., Drew, D. A., Nguyen, L. H., Fall, Tove, Gomez, M. F., Franks, P. W., Chan, A. T., Davies, R., Wolf, J., Steves, C. J., Spector, T. D., Ourselin, S., Varsavsky, T., Graham, M. S., Canas, L. S., Ganesh, S., Capdevila Pujol, J., Sudre, C. H., Murray, B., Modat, M., Jorge Cardoso, M., Astley, C. M., Drew, D. A., Nguyen, L. H., Fall, Tove, Gomez, M. F., Franks, P. W., Chan, A. T., Davies, R., Wolf, J., Steves, C. J., Spector, T. D., and Ourselin, S.

Abstract

Background As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots. Findings From March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023–17 885) daily cases, a prevalence of 0·53% (0·45–0·60), and R(t) of 1·17 (1·15–1·19) in England. On

Published
2021
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8. Symptom clusters in COVID-19 : A potential clinical prediction tool from the COVID Symptom Study app

Author

Sudre, C. H., Lee, K. A., Lochlainn, M. N., Varsavsky, T., Murray, B., Graham, M. S., Menni, C., Modat, M., Bowyer, R. C. E., Nguyen, L. H., Drew, D. A., Joshi, A. D., Ma, W., Guo, C. G., Lo, C. H., Ganesh, S., Buwe, A., Pujol, J. C., du Cadet, J. L., Visconti, A., Freidin, M. B., El-Sayed Moustafa, J. S., Falchi, M., Davies, R., Gomez, M. F., Fall, Tove, Cardoso, M. J., Wolf, J., Franks, P. W., Chan, A. T., Spector, T. D., Steves, C. J., Ourselin, S., Sudre, C. H., Lee, K. A., Lochlainn, M. N., Varsavsky, T., Murray, B., Graham, M. S., Menni, C., Modat, M., Bowyer, R. C. E., Nguyen, L. H., Drew, D. A., Joshi, A. D., Ma, W., Guo, C. G., Lo, C. H., Ganesh, S., Buwe, A., Pujol, J. C., du Cadet, J. L., Visconti, A., Freidin, M. B., El-Sayed Moustafa, J. S., Falchi, M., Davies, R., Gomez, M. F., Fall, Tove, Cardoso, M. J., Wolf, J., Franks, P. W., Chan, A. T., Spector, T. D., Steves, C. J., and Ourselin, S.

Abstract

As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presenta- tions. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic – area under the curve) of need for respiratory sup- port was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

Published
2021
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9. Single-subject structural cortical networks in clinically isolated syndrome

Author

Collorone S., Prados F., Hagens M. H. J., Tur C., Kanber B., Sudre C. H., Lukas C., Gasperini C., Oreja-Guevara C., Andelova M., Ciccarelli O., Wattjes M. P., Ourselin S., Altmann D. R., Tijms B. M., Barkhof F., Toosy A. T, Magnims SDtudy Group, Filippi M, Rocca MA, Neurology, Amsterdam Neuroscience - Neuroinfection & -inflammation, Radiology and nuclear medicine, Collorone, S., Prados, F., Hagens, M. H. J., Tur, C., Kanber, B., Sudre, C. H., Lukas, C., Gasperini, C., Oreja-Guevara, C., Andelova, M., Ciccarelli, O., Wattjes, M. P., Ourselin, S., Altmann, D. R., Tijms, B. M., Barkhof, F., Toosy A., T, Magnims SDtudy, Group, Filippi, M, and Rocca, Ma

Subjects
graph theory, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Magnetic resonance imaging, 0302 clinical medicine, Cognition, Neural Pathways, medicine, Humans, Prospective Studies, structural cortical networks, Clinically isolated syndrome, medicine.diagnostic_test, business.industry, Multiple sclerosis, Brain, gray matter, medicine.disease, Magnetic Resonance Imaging, multicenter study, Neurology, Multicenter study, clinically isolated syndrome, multiple sclerosi, Neurology (clinical), business, Neuroscience, 030217 neurology & neurosurgery, Demyelinating Diseases
Abstract

Background: Structural cortical networks (SCNs) represent patterns of coordinated morphological modifications in cortical areas, and they present the advantage of being extracted from previously acquired clinical magnetic resonance imaging (MRI) scans. SCNs have shown pathophysiological changes in many brain disorders, including multiple sclerosis. Objective: To investigate alterations of SCNs at the individual level in patients with clinically isolated syndrome (CIS), thereby assessing their clinical relevance. Methods: We analyzed baseline data collected in a prospective multicenter (MAGNIMS) study. CIS patients ( n = 60) and healthy controls ( n = 38) underwent high-resolution 3T MRI. Measures of disability and cognitive processing were obtained for patients. Single-subject SCNs were extracted from brain 3D-T1 weighted sequences; global and local network parameters were computed. Results: Compared to healthy controls, CIS patients showed altered small-world topology, an efficient network organization combining dense local clustering with relatively few long-distance connections. These disruptions were worse for patients with higher lesion load and worse cognitive processing speed. Alterations of centrality measures and clustering of connections were observed in specific cortical areas in CIS patients when compared with healthy controls. Conclusion: Our study indicates that SCNs can be used to demonstrate clinically relevant alterations of connectivity in CIS.

Published
2019

10. 144 Probable Delirium is A Presenting Symptom of COVID-19 in Frail, Older Adults: A Study of Hospitalised and Community-Based Cohorts

Author

Penfold, R S, primary, Zazzara, M B, additional, Roberts, A L, additional, Lee, K A, additional, Dooley, H, additional, Sudre, C H, additional, Welch, C, additional, Bowyer, R C E, additional, Visconti, A, additional, Mangino, M, additional, Freidin, M B, additional, El-Sayed Moustafa, J S, additional, Small, K, additional, Murray, B, additional, Modat, M, additional, Wolf, J, additional, Ourselin, S, additional, Martin, F C, additional, Steves, C J, additional, and Ni Lochlainn, M, additional

Published
2021
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11. ExploreQC: A toolbox for MRI quality control in the EPAD multicentre study

Author

Lorenzini, L., Ingala, S., Wottschel, V., Wink, A. M., Kuijer, J., Sudre, C. H., Haller, S., Molinuevo, J. L., Gispert, J. D., Cash, D. M., Thomas, D. L., Vos, S., Petr, J., Wolz, R., Pernet, C., Waldman, A., Barkhof, F., Mutsaerts, H. J. M. M., and Epad, C.

Abstract

Magnetic Resonance Imaging (MRI) of the brain is prone to artefacts that may worsen image quality and subsequent analyses. Despite the growing number of large-scale multi-institutional imaging studies, standardized approaches for defining inclusion and exclusion criteria on the basis of image data quality are still lacking and quality assessment is often based on visual inspection. We introduce ExploreQC, a MATLAB-based toolbox which implements a semi-automatic pipeline to assess QC metrics, select the most relevant parameters, and to derive informed inclusion thresholds.

Published
2020

12. SARS-CoV-2 (COVID-19) infection in pregnant women : characterization of symptoms and syndromes predictive of disease and severity through real-time, remote participatory epidemiology

Author

Molteni, E., Astley, C. M., Ma, W., Sudre, C. H., Magee, L. A., Murray, B., Fall, Tove, Gomez, M. F., Tsereteli, N., Franks, P. W., Brownstein, J. S., Davies, R., Wolf, J., Spector, T., Ourselin, S., Steves, C., Chan, A. T., Modat, M., Molteni, E., Astley, C. M., Ma, W., Sudre, C. H., Magee, L. A., Murray, B., Fall, Tove, Gomez, M. F., Tsereteli, N., Franks, P. W., Brownstein, J. S., Davies, R., Wolf, J., Spector, T., Ourselin, S., Steves, C., Chan, A. T., and Modat, M.

Published
2020
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13. COVID-19 due to the B.1.617.2 (Delta) variant compared to B.1.1.7 (Alpha) variant of SARS-CoV-2: a prospective observational cohort study

Author

Kläser, K., Molteni, E., Graham, M., Canas, L. S., Österdahl, M. F., Antonelli, M., Chen, L., Deng, J., Murray, B., Kerfoot, E., Wolf, J., May, A., Fox, B., Capdevila, J., Aanensen, D. M., Abudahab, K., Adams, H., Adams, A., Afifi, S., Aggarwal, D., Ahmad, S. S. Y., Aigrain, L., Alcolea-Medina, A., Alikhan, N-F, Allara, E., Amato, R., Angyal, A., Annett, T., Aplin, S., Ariani, C. V., Asad, H., Ash, A., Ashfield, P., Ashford, F., Atkinson, L., Attwood, S. W., Auckland, C., Aydin, A., Baker, D. J., Baker, P., Balcazar, C. E., Ball, J., Barrett, J. C., Barrow, M., Barton, E., Bashton, M., Bassett, A. R., Batra, R., Baxter, C., Bayzid, N., Beaver, C., Beckett, A. H., Beckwith, S. M., Bedford, L., Beer, R., Beggs, A., Bellis, K. L., Berry, L., Bertolusso, B., Best, A., Betteridge, E., Bibby, D., Bicknell, K., Binns, D., Birchley, A., Bird, P. W., Bishop, C., Blacow, R., Blakey, V., Blane, B., Bolt, F., Bonfield, J., Bonner, S., Bonsall, D., Boswell, T., Bosworth, A., Bourgeois, Y., Boyd, O., Bradley, D. T., Breen, C., Bresner, C., Breuer, J., Bridgett, S., Bronner, I. F., Brooks, E., Broos, A., Brown, J. R., Bucca, G., Buchan, S. L., Buck, D., Bull, M., Burns, P. J., Burton-Fanning, S., Byaruhanga, T., Byott, M., Campbell, S., Carabelli, A. M., Cargill, J. S., Carlile, M., Carvalho, S. F., Casey, A., Castigador, A., Catalan, J., Chalker, V., Chaloner, N. J., Chand, M., Chappell, J. G., Charalampous, T., Chatterton, W., Chaudhry, Y., Churcher, C. M., Clark, G., Clarke, P., Cogger, B. J., Cole, K., Collins, J., Colquhoun, R., Connor, T. R., Cook, K. F., Coombes, J., Corden, S., Cormie, C., Cortes, N., Cotic, M., Cotton, S., Cottrell, S., Coupland, L., Cox, M. G., Cox, A., Craine, N., Crawford, L., Cross, A., Crown, M. R., Crudgington, D., Cumley, N., Curran, T., Curran, M. D., da Silva Filipe, A., Dabrera, G., Darby, A. C., Davidson, R. K., Davies, A., Davies, R. M., Davis, T., de Angelis, D., De Lacy, E., de Oliveira Martins, L., de Silva, T. I., Debebe, J., Denton-Smith, R., Dervisevic, S., Dewar, R., Dey, J., Dias, J., Dobie, D., Dorman, M. J., Downing, F., Driscoll, M., du Plessis, L., Duckworth, N., Durham, J., Eastick, K., Easton, L. J., Eccles, R., Edgeworth, J., Edwards, S., Bouzidi, K. E., Eldirdiri, S., Ellaby, N., Elliott, S., Eltringham, G., Ensell, L., Erkiert, M. J., Zamudio, M. E., Essex, S., Evans, J. M., Evans, C., Everson, W., Fairley, D. J., Fallon, K., Fanaie, A., Farr, B. W., Fearn, C., Feltwell, T., Ferguson, L., Fina, L., Flaviani, F., Fleming, V. M., Forrest, S., Foster-Nyarko, E., Foulkes, B. H., Foulser, L., Fragakis, M., Frampton, D., Francois, S., Fraser, C., Freeman, T. M., Fryer, H., Fuchs, M., Fuller, W., Gajee, K., Galai, K., Gallagher, A., Gallagher, E., Gallagher, M. D., Gallis, M., Gaskin, A., Gatica-Wilcox, B., Geidelberg, L., Gemmell, M., Georgana, I., George, R. P., Gifford, L., Gilbert, L., Girgis, S. T., Glaysher, S., Goldstein, E. J., Golubchik, T., Gomes, A. N., Gonçalves, S., Goodfellow, I. G., Goodwin, S., Goudarzi, S., Gourtovaia, M., Graham, C. A., Graham, L., Grant, P. R., Green, L. R., Green, A., Greenaway, J., Gregory, R., Guest, M., Gunson, R. N., Gupta, R. K., Gutierrez, B., Haldenby, S. T., Hamilton, W. L., Hansford, S. E., Haque, T., Harris, K. A., Harrison, I., Harrison, E. M., Hart, J., Hartley, .J. A., Harvey, W. T., Harvey, M., Hassan-Ibrahim, M. O., Heaney, J., Helmer, T., Henderson, J. H., Hesketh, A. R., Hey, J., Heyburn, D., Higginson, E. E., Hill, V., Hill, J. D., Hilson, R. A., Hilvers, E., Holden, M. T. G., Hollis, A., Holmes, C. W., Holmes, N., Holmes, A. H., Hopes, R., Hornsby, H. R., Hosmillo, M., Houlihan, C., Howson-Wells, H. C., Hsu, S. N., Hubb, J., Huckson, H., Hughes, W., Hughes, J., Hughes, M., Hutchings, S., Idle, G, Illingworth, CJ, Impey, R, Irish-Tavares, D, Iturriza-Gomara, M, Izuagbe, R, Jackson, C, Jackson, B, Jackson, LM, Jackson, KA, Jackson, DK, Jahun, AS, James, V, James, K, Jeanes, C, Jeffries, AR, Jeremiah, S, Jermy, A, John, M, Johnson, R, Johnson, K, Johnston, I, Jones, O, Jones, S, Jones, H, Jones, CR, Jones, N, Joseph, A, Judges, S, Kay, GL, Kay, S, Keatley, J - P, Keeley, AJ, Kenyon, A, Kermack, LM, Khakh, M, Kidd, SP, Kimuli, M, Kirk, S, Kitchen, C, Kitchman, K, Knight, BA, Koshy, C, Kraemer, MUG, Kumziene-Summerhayes, S, Kwiatkowski, D, Lackenby, A, Laing, KG, Lampejo, T, Langford, CF, Lavin, D, Lawton, AI, Lee, JCD, Lee, D, Lensing, SV, Leonard, S, Levett, LJ, Le-Viet, T, Lewis, J, Lewis, K, Liddle, J, Liggett, S, Lillie, PJ, Lindsey, BB, Lister, MM, Livett, R, Lo, S, Loman, NJ, Loose, MW, Louka, SF, Loveson, KF, Lowdon, S, Lowe, H, Lowe, HL, Lucaci, AO, Ludden, C, Lynch, J, Lyons, RA, Lythgoe, K, Machin, NW, MacIntyre-Cockett, G, Mack, A, Macklin, B, Maclean, A, Macnaughton, E, Madona, P, Maes, M, Maftei, L, Mahanama, AIK, Mahungu, TW, Mair, D, Maksimovic, J, Malone, CS, Maloney, D, Manesis, N, Manley, R, Mantzouratou, A, Marchbank, A, Mariappan, A, Martincorena, I, Nunez, RTM, Mather, AE, Maxwell, P, Mayhew, M, Mbisa, T, McCann, CM, McCarthy, SA, McCluggage, K, McClure, PC, McCrone, JT, McHugh, MP, McKenna, JP, McKerr, C, McManus, GM, McMurray, CL, McMurray, C, McNally, A, Meadows, L, Medd, N, Megram, O, Menegazzo, M, Merrick, I, Michell, SL, Michelsen, ML, Mirfenderesky, M, Mirza, J, Miskelly, J, Moles-Garcia, E, Moll, RJ, Molnar, Z, Monahan, IM, Mondani, M, Mookerjee, S, Moore, C, Moore, J, Moore, N, Morcrette, H, Morgan, S, Morgan, M, Mori, M, Morriss, A, Moses, S, Mower, C, Muir, P, Mukaddas, A, Munemo, F, Munn, R, Murray, A, Murray, LJ, Murray, DR, Mutingwende, M, Myers, R, Nastouli, E, Nebbia, G, Nelson, A, Nelson, C, Nicholls, S, Nichols, J, Nicodemi, R, Nomikou, K, O’Grady, J, O’Brien, S, Odedra, M, Ohemeng-Kumi, N, Oliver, K, Orton, RJ, Osman, H, O’Toole, Á, Pacchiarini, N, Padgett, D, Page, AJ, Park, EJ, Park, NR, Parker, MD, Parmar, S, Partridge, DG, Pascall, D, Patel, A, Patel, B, Paterson, S, Payne, BAI, Peacock, SJ, Pearson, C, Pelosi, E, Percival, B, Perkins, J, Perry, M, Pinckert, ML, Platt, S, Podplomyk, O, Pohare, M, Pond, M, Pope, CF, Poplawski, R, Powell, J, Poyner, J, Prestwood, L, Price, A, Price, JR, Prieto, JA, Pritchard, DT, Prosolek, SJ, Pugh, G, Pusok, M, Pybus, OG, Pymont, HM, Quail, MA, Quick, J, Radulescu, C, Raghwani, J, Ragonnet-Cronin, M, Rainbow, L, Rajan, D, Rajatileka, S, Ramadan, NA, Rambaut, A, Ramble, J, Randell, PA, Randell, P, Ratcliffe, L, Raviprakash, V, Raza, M, Redshaw, NM, Rey, S, Reynolds, N, Richter, A, Robertson, DL, Robinson, E, Robson, SC, Rogan, F, Rooke, S, Rowe, W, Roy, S, Rudder, S, Ruis, C, Rushton, S, Ryan, F, Saeed, K, Samaraweera, B, Sambles, CM, Sanderson, R, Sanderson, T, Sang, F, Sass, T, Scher, E, Scott, G, Scott, C, Sehmi, J, Shaaban, S, Shah, D, Shaw, J, Shelest, E, Shepherd, JG, Sheridan, LA, Sheriff, N, Shirley, L, Sillitoe, J, Silviera, S, Simpson, DA, Singh, A, Singleton, D, Skvortsov, T, Sloan, TJ, Sluga, G, Smith, K, Smith, KS, Smith, P, Smith, DL, Smith, L, Smith, CP, Smith, N, Smollett, KL, Snell, LB, Somassa, T, Southgate, J, Spellman, K, Chapman, MHS, Spurgin, LG, Spyer, MJ, Stanley, R, Stanley, W, Stanton, TD, Starinskij, I, Stockton, J, Stonehouse, S, Storey, N, Studholme, DJ, Sudhanva, M, Swindells, E, Taha, Y, Tan, NK, Tang, JW, Tang, M, Taylor, BEW, Taylor, JF, Taylor, S, Temperton, B, Templeton, KE, Thomas, C, Thomson, L, Thomson, EC, Thornton, A, Thurston, SAJ, Todd, JA, Tomb, R, Tong, L, Tonkin-Hill, G, Torok, ME, Tovar-Corona, JM, Trebes, A, Trotter, AJ, Tsatsani, I, Turnbull, R, Turtle, L, Twohig, KA, Umpleby, H, Underwood, AP, Vamos, EE, Vasylyeva, TI, Vattipally, S, Vernet, G, Vipond, BB, Volz, EM, Walsh, S, Wang, D, Warne, B, Warwick-Dugdale, J, Wastnedge, E, Watkins, J, Watson, LK, Waugh, S, Webster, HJ, Weldon, D, Westwick, E, Whalley, T, Wheeler, H, Whitehead, M, Whiteley, M, Whitwham, A, Wierzbicki, C, Willford, NJ, Williams, L-A, Williams, R, Williams, C, Williams, CA, Williams, RJ, Williams, T, Williamson, KA, Wilson-Davies, E, Witele, E, Withell, KT, Witney, AA, Wolverson, P, Wong, N, Workman, T, Wright, V, Wright, DW, Wyatt, T, Wyllie, S, Xu-McCrae, L., Yavus, M., Yaze, G., Yeats, C. A., Yebra, G., Yew, W. C., Young, G. R., Young, J., Zarebski, A. E., Zhang, P., Modat, M., Hammers, A., Spector, T. D., Steves, C. J., Sudre, C. H., Ourselin, S., Duncan, E. L., Kläser, K., Molteni, E., Graham, M., Canas, L. S., Österdahl, M. F., Antonelli, M., Chen, L., Deng, J., Murray, B., Kerfoot, E., Wolf, J., May, A., Fox, B., Capdevila, J., Aanensen, D. M., Abudahab, K., Adams, H., Adams, A., Afifi, S., Aggarwal, D., Ahmad, S. S. Y., Aigrain, L., Alcolea-Medina, A., Alikhan, N-F, Allara, E., Amato, R., Angyal, A., Annett, T., Aplin, S., Ariani, C. V., Asad, H., Ash, A., Ashfield, P., Ashford, F., Atkinson, L., Attwood, S. W., Auckland, C., Aydin, A., Baker, D. J., Baker, P., Balcazar, C. E., Ball, J., Barrett, J. C., Barrow, M., Barton, E., Bashton, M., Bassett, A. R., Batra, R., Baxter, C., Bayzid, N., Beaver, C., Beckett, A. H., Beckwith, S. M., Bedford, L., Beer, R., Beggs, A., Bellis, K. L., Berry, L., Bertolusso, B., Best, A., Betteridge, E., Bibby, D., Bicknell, K., Binns, D., Birchley, A., Bird, P. W., Bishop, C., Blacow, R., Blakey, V., Blane, B., Bolt, F., Bonfield, J., Bonner, S., Bonsall, D., Boswell, T., Bosworth, A., Bourgeois, Y., Boyd, O., Bradley, D. T., Breen, C., Bresner, C., Breuer, J., Bridgett, S., Bronner, I. F., Brooks, E., Broos, A., Brown, J. R., Bucca, G., Buchan, S. L., Buck, D., Bull, M., Burns, P. J., Burton-Fanning, S., Byaruhanga, T., Byott, M., Campbell, S., Carabelli, A. M., Cargill, J. S., Carlile, M., Carvalho, S. F., Casey, A., Castigador, A., Catalan, J., Chalker, V., Chaloner, N. J., Chand, M., Chappell, J. G., Charalampous, T., Chatterton, W., Chaudhry, Y., Churcher, C. M., Clark, G., Clarke, P., Cogger, B. J., Cole, K., Collins, J., Colquhoun, R., Connor, T. R., Cook, K. F., Coombes, J., Corden, S., Cormie, C., Cortes, N., Cotic, M., Cotton, S., Cottrell, S., Coupland, L., Cox, M. G., Cox, A., Craine, N., Crawford, L., Cross, A., Crown, M. R., Crudgington, D., Cumley, N., Curran, T., Curran, M. D., da Silva Filipe, A., Dabrera, G., Darby, A. C., Davidson, R. K., Davies, A., Davies, R. M., Davis, T., de Angelis, D., De Lacy, E., de Oliveira Martins, L., de Silva, T. I., Debebe, J., Denton-Smith, R., Dervisevic, S., Dewar, R., Dey, J., Dias, J., Dobie, D., Dorman, M. J., Downing, F., Driscoll, M., du Plessis, L., Duckworth, N., Durham, J., Eastick, K., Easton, L. J., Eccles, R., Edgeworth, J., Edwards, S., Bouzidi, K. E., Eldirdiri, S., Ellaby, N., Elliott, S., Eltringham, G., Ensell, L., Erkiert, M. J., Zamudio, M. E., Essex, S., Evans, J. M., Evans, C., Everson, W., Fairley, D. J., Fallon, K., Fanaie, A., Farr, B. W., Fearn, C., Feltwell, T., Ferguson, L., Fina, L., Flaviani, F., Fleming, V. M., Forrest, S., Foster-Nyarko, E., Foulkes, B. H., Foulser, L., Fragakis, M., Frampton, D., Francois, S., Fraser, C., Freeman, T. M., Fryer, H., Fuchs, M., Fuller, W., Gajee, K., Galai, K., Gallagher, A., Gallagher, E., Gallagher, M. D., Gallis, M., Gaskin, A., Gatica-Wilcox, B., Geidelberg, L., Gemmell, M., Georgana, I., George, R. P., Gifford, L., Gilbert, L., Girgis, S. T., Glaysher, S., Goldstein, E. J., Golubchik, T., Gomes, A. N., Gonçalves, S., Goodfellow, I. G., Goodwin, S., Goudarzi, S., Gourtovaia, M., Graham, C. A., Graham, L., Grant, P. R., Green, L. R., Green, A., Greenaway, J., Gregory, R., Guest, M., Gunson, R. N., Gupta, R. K., Gutierrez, B., Haldenby, S. T., Hamilton, W. L., Hansford, S. E., Haque, T., Harris, K. A., Harrison, I., Harrison, E. M., Hart, J., Hartley, .J. A., Harvey, W. T., Harvey, M., Hassan-Ibrahim, M. O., Heaney, J., Helmer, T., Henderson, J. H., Hesketh, A. R., Hey, J., Heyburn, D., Higginson, E. E., Hill, V., Hill, J. D., Hilson, R. A., Hilvers, E., Holden, M. T. G., Hollis, A., Holmes, C. W., Holmes, N., Holmes, A. H., Hopes, R., Hornsby, H. R., Hosmillo, M., Houlihan, C., Howson-Wells, H. C., Hsu, S. N., Hubb, J., Huckson, H., Hughes, W., Hughes, J., Hughes, M., Hutchings, S., Idle, G, Illingworth, CJ, Impey, R, Irish-Tavares, D, Iturriza-Gomara, M, Izuagbe, R, Jackson, C, Jackson, B, Jackson, LM, Jackson, KA, Jackson, DK, Jahun, AS, James, V, James, K, Jeanes, C, Jeffries, AR, Jeremiah, S, Jermy, A, John, M, Johnson, R, Johnson, K, Johnston, I, Jones, O, Jones, S, Jones, H, Jones, CR, Jones, N, Joseph, A, Judges, S, Kay, GL, Kay, S, Keatley, J - P, Keeley, AJ, Kenyon, A, Kermack, LM, Khakh, M, Kidd, SP, Kimuli, M, Kirk, S, Kitchen, C, Kitchman, K, Knight, BA, Koshy, C, Kraemer, MUG, Kumziene-Summerhayes, S, Kwiatkowski, D, Lackenby, A, Laing, KG, Lampejo, T, Langford, CF, Lavin, D, Lawton, AI, Lee, JCD, Lee, D, Lensing, SV, Leonard, S, Levett, LJ, Le-Viet, T, Lewis, J, Lewis, K, Liddle, J, Liggett, S, Lillie, PJ, Lindsey, BB, Lister, MM, Livett, R, Lo, S, Loman, NJ, Loose, MW, Louka, SF, Loveson, KF, Lowdon, S, Lowe, H, Lowe, HL, Lucaci, AO, Ludden, C, Lynch, J, Lyons, RA, Lythgoe, K, Machin, NW, MacIntyre-Cockett, G, Mack, A, Macklin, B, Maclean, A, Macnaughton, E, Madona, P, Maes, M, Maftei, L, Mahanama, AIK, Mahungu, TW, Mair, D, Maksimovic, J, Malone, CS, Maloney, D, Manesis, N, Manley, R, Mantzouratou, A, Marchbank, A, Mariappan, A, Martincorena, I, Nunez, RTM, Mather, AE, Maxwell, P, Mayhew, M, Mbisa, T, McCann, CM, McCarthy, SA, McCluggage, K, McClure, PC, McCrone, JT, McHugh, MP, McKenna, JP, McKerr, C, McManus, GM, McMurray, CL, McMurray, C, McNally, A, Meadows, L, Medd, N, Megram, O, Menegazzo, M, Merrick, I, Michell, SL, Michelsen, ML, Mirfenderesky, M, Mirza, J, Miskelly, J, Moles-Garcia, E, Moll, RJ, Molnar, Z, Monahan, IM, Mondani, M, Mookerjee, S, Moore, C, Moore, J, Moore, N, Morcrette, H, Morgan, S, Morgan, M, Mori, M, Morriss, A, Moses, S, Mower, C, Muir, P, Mukaddas, A, Munemo, F, Munn, R, Murray, A, Murray, LJ, Murray, DR, Mutingwende, M, Myers, R, Nastouli, E, Nebbia, G, Nelson, A, Nelson, C, Nicholls, S, Nichols, J, Nicodemi, R, Nomikou, K, O’Grady, J, O’Brien, S, Odedra, M, Ohemeng-Kumi, N, Oliver, K, Orton, RJ, Osman, H, O’Toole, Á, Pacchiarini, N, Padgett, D, Page, AJ, Park, EJ, Park, NR, Parker, MD, Parmar, S, Partridge, DG, Pascall, D, Patel, A, Patel, B, Paterson, S, Payne, BAI, Peacock, SJ, Pearson, C, Pelosi, E, Percival, B, Perkins, J, Perry, M, Pinckert, ML, Platt, S, Podplomyk, O, Pohare, M, Pond, M, Pope, CF, Poplawski, R, Powell, J, Poyner, J, Prestwood, L, Price, A, Price, JR, Prieto, JA, Pritchard, DT, Prosolek, SJ, Pugh, G, Pusok, M, Pybus, OG, Pymont, HM, Quail, MA, Quick, J, Radulescu, C, Raghwani, J, Ragonnet-Cronin, M, Rainbow, L, Rajan, D, Rajatileka, S, Ramadan, NA, Rambaut, A, Ramble, J, Randell, PA, Randell, P, Ratcliffe, L, Raviprakash, V, Raza, M, Redshaw, NM, Rey, S, Reynolds, N, Richter, A, Robertson, DL, Robinson, E, Robson, SC, Rogan, F, Rooke, S, Rowe, W, Roy, S, Rudder, S, Ruis, C, Rushton, S, Ryan, F, Saeed, K, Samaraweera, B, Sambles, CM, Sanderson, R, Sanderson, T, Sang, F, Sass, T, Scher, E, Scott, G, Scott, C, Sehmi, J, Shaaban, S, Shah, D, Shaw, J, Shelest, E, Shepherd, JG, Sheridan, LA, Sheriff, N, Shirley, L, Sillitoe, J, Silviera, S, Simpson, DA, Singh, A, Singleton, D, Skvortsov, T, Sloan, TJ, Sluga, G, Smith, K, Smith, KS, Smith, P, Smith, DL, Smith, L, Smith, CP, Smith, N, Smollett, KL, Snell, LB, Somassa, T, Southgate, J, Spellman, K, Chapman, MHS, Spurgin, LG, Spyer, MJ, Stanley, R, Stanley, W, Stanton, TD, Starinskij, I, Stockton, J, Stonehouse, S, Storey, N, Studholme, DJ, Sudhanva, M, Swindells, E, Taha, Y, Tan, NK, Tang, JW, Tang, M, Taylor, BEW, Taylor, JF, Taylor, S, Temperton, B, Templeton, KE, Thomas, C, Thomson, L, Thomson, EC, Thornton, A, Thurston, SAJ, Todd, JA, Tomb, R, Tong, L, Tonkin-Hill, G, Torok, ME, Tovar-Corona, JM, Trebes, A, Trotter, AJ, Tsatsani, I, Turnbull, R, Turtle, L, Twohig, KA, Umpleby, H, Underwood, AP, Vamos, EE, Vasylyeva, TI, Vattipally, S, Vernet, G, Vipond, BB, Volz, EM, Walsh, S, Wang, D, Warne, B, Warwick-Dugdale, J, Wastnedge, E, Watkins, J, Watson, LK, Waugh, S, Webster, HJ, Weldon, D, Westwick, E, Whalley, T, Wheeler, H, Whitehead, M, Whiteley, M, Whitwham, A, Wierzbicki, C, Willford, NJ, Williams, L-A, Williams, R, Williams, C, Williams, CA, Williams, RJ, Williams, T, Williamson, KA, Wilson-Davies, E, Witele, E, Withell, KT, Witney, AA, Wolverson, P, Wong, N, Workman, T, Wright, V, Wright, DW, Wyatt, T, Wyllie, S, Xu-McCrae, L., Yavus, M., Yaze, G., Yeats, C. A., Yebra, G., Yew, W. C., Young, G. R., Young, J., Zarebski, A. E., Zhang, P., Modat, M., Hammers, A., Spector, T. D., Steves, C. J., Sudre, C. H., Ourselin, S., and Duncan, E. L.

Abstract

The Delta (B.1.617.2) variant was the predominant UK circulating SARS-CoV-2 strain between May and December 2021. How Delta infection compares with previous variants is unknown. This prospective observational cohort study assessed symptomatic adults participating in the app-based COVID Symptom Study who tested positive for SARS-CoV-2 from May 26 to July 1, 2021 (Delta overwhelmingly the predominant circulating UK variant), compared (1:1, age- and sex-matched) with individuals presenting from December 28, 2020 to May 6, 2021 (Alpha (B.1.1.7) the predominant variant). We assessed illness (symptoms, duration, presentation to hospital) during Alpha- and Delta-predominant timeframes; and transmission, reinfection, and vaccine effectiveness during the Delta-predominant period. 3581 individuals (aged 18 to 100 years) from each timeframe were assessed. The seven most frequent symptoms were common to both variants. Within the first 28 days of illness, some symptoms were more common with Delta versus Alpha infection (including fever, sore throat, and headache) and some vice versa (dyspnoea). Symptom burden in the first week was higher with Delta versus Alpha infection; however, the odds of any given symptom lasting ≥ 7 days was either lower or unchanged. Illness duration ≥ 28 days was lower with Delta versus Alpha infection, though unchanged in unvaccinated individuals. Hospitalisation for COVID-19 was unchanged. The Delta variant appeared more (1.49) transmissible than Alpha. Re-infections were low in all UK regions. Vaccination markedly reduced the risk of Delta infection (by 69-84%). We conclude that COVID-19 from Delta or Alpha infections is similar. The Delta variant is more transmissible than Alpha; however, current vaccines showed good efficacy against disease. This research framework can be useful for future comparisons with new emerging variants. © 2022, The Author(s).

14. FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis

Author

Sara Collorone, Marloes Hagens, Sjoerd B. Vos, Ferran Prados, Carole H. Sudre, John S. Thornton, Frederik Barkhof, Olivia Goodkin, Hugh G. Pemberton, Tarek A. Yousry, Manuel Jorge Cardoso, Goodkin, O., Prados, F., Vos, S. B., Pemberton, H., Collorone, S., Hagens, M. H. J., Cardoso, M. J., Yousry, T. A., Thornton, J. S., Sudre, C. H., Barkhof, F., MAGNIMS study, Group, Filippi, M, Rocca, M. A., Neurology, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, and Amsterdam Neuroscience - Neuroinfection & -inflammation

Subjects
medicine.medical_specialty, Cognitive Neuroscience, Fluid-attenuated inversion recovery, lcsh:Computer applications to medicine. Medical informatics, lcsh:RC346-429, 050105 experimental psychology, White matter, Lesion, Multiple sclerosis, 03 medical and health sciences, 0302 clinical medicine, Magnetic resonance imaging, medicine, Humans, Brain segmentation, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Gray Matter, lcsh:Neurology. Diseases of the nervous system, Clinically isolated syndrome, medicine.diagnostic_test, business.industry, 05 social sciences, Neurodegenerative diseases, Leukoaraiosis, Brain, Regular Article, Hyperintensity, Demyelinating diseases, medicine.anatomical_structure, Neurology, Brain size, lcsh:R858-859.7, Neurology (clinical), Radiology, Atrophy, medicine.symptom, business, 030217 neurology & neurosurgery
Abstract

Background MRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification. Objectives We address an aspect of this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations. Methods We used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, ‘manual 1′ guided by consensus reading of CIS-specific lesions and ‘manual 2′ by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1 + FLAIR) and FLAIR-only methods. Results WM lesion volumes were comparable at 1.5T between ‘manual 2′ and FLAIR-only methods and at 3T between ‘manual 2′, T1 + FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α = 1.029, R2 = 0.997, standard error (SE) = 0.007; 3T: α = 1.019, R2 = 0.998, SE = 0.006). Age-associated change in cortical GM volume was a significant covariate in both T1 (p = 0.001) and FLAIR-only (p = 0.005) methods, confirming the expected relationship between age and GM volume for FLAIR-only segmentations. Conclusions FLAIR-only automated segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. Imaging protocol harmonisation and validation with other MS phenotypes could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting.

Published
2021

15. Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Author

Eser Sanverdi, Sotirios Bisdas, Katarina Surlan-Popovic, Sylvie Grand, Gian Marco Conte, Mario Nigro, M. Jorge Cardoso, Maria Vittoria Spampinato, Alexandre Krainik, Diana Roettger, Javier Gonzalez, Nicoletta Anzalone, Jernej Avsenik, Timothé Boutelier, Claudio Ghimenton, Lorenzo Ugga, Valeria Romeo, Arnaud Attyé, Jasmina Panovska-Griffiths, Eftychia Kapsalaki, Arindam R. Chatterjee, Vasileios K. Katsaros, George Stranjalis, Elisa Ciceri, Sebastian Brandner, Elia Guadagno, Francesca B. Pizzini, Andrea Elefante, Carole H. Sudre, Sudre, C. H., Panovska-Griffiths, J., Sanverdi, E., Brandner, S., Katsaros, V. K., Stranjalis, G., Pizzini, F. B., Ghimenton, C., Surlan-Popovic, K., Avsenik, J., Spampinato, M. V., Nigro, M., Chatterjee, A. R., Attye, A., Grand, S., Krainik, A., Anzalone, N., Conte, G. M., Romeo, V., Ugga, L., Elefante, A., Ciceri, E. F., Guadagno, E., Kapsalaki, E., Roettger, D., Gonzalez, J., Boutelier, T., Cardoso, M. J., and Bisdas, S.

Subjects
Wilcoxon signed-rank test, DSC-MRI, Diagnostic machine learning, Glioma stratification, Isocitrate dehydrogenase, Health Informatics, Machine learning, computer.software_genre, Diagnostic tools, lcsh:Computer applications to medicine. Medical informatics, 030218 nuclear medicine & medical imaging, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Radiomics, Glioma, medicine, Humans, Grading (tumors), Retrospective Studies, Mri techniques, business.industry, Brain Neoplasms, Health Policy, medicine.disease, Magnetic Resonance Imaging, Subtyping, Computer Science Applications, Random forest, Cerebral blood volume, Mutation, lcsh:R858-859.7, Artificial intelligence, Neoplasm Grading, business, computer, 030217 neurology & neurosurgery, Dynamic susceptibility, Research Article
Abstract

BackgroundMachine learning assisted MRI radiomics, which combines MRI techniques with machine learning methodology, is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patient pool into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.Methods333 patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant=151 or IDH-wildtype=182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features between IDH-wildtype and IDH-mutant gliomas and across three glioma grades were tested using the Wilcoxon two-sample test. A random forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.ResultsFeatures from all types (shape, distribution, texture) showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by IDH mutation status in 71% of the cases and by grade in 53% of the cases. In addition, 87% of the gliomas grades predicted with an error distance up to 1.ConclusionDespite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.Key points-On highly heterogenous, multi-centre data, machine learning on DSC-MRI features can correctly predict glioma IDH subtyping in 71% of cases and glioma grade II-IV in 53% of the cases (87% -Shape features distinguish best grade II from grade III gliomas.-Texture and distribution features distinguish best grade III from grade IV tumours.Importance of studyThis work illustrates the diagnostic value of combining machine learning and dynamic susceptibility contrast-enhanced MRI (DSC-MRI) radiomics in classifying gliomas into WHO grades II-IV as well as across their isocitrate dehydrogenase (IDH) mutation status. Despite the data heterogeneity inherent to the multi-centre design of the studied cohort (333 subjects, 6 centres) that greatly increases the theoretical challenges of machine learning frameworks, good classification performance (accuracy of 53% across grades (87% With its strong generalisability property, its ability to further incorporate participating centres and its possible use to identify borderline cases, the proposed machine learning framework has the potential to contribute to the clinical translation of machine-learning assisted diagnostic tools in neuro-oncology.

Published
2019

16. Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort.

Author

Antonelli M, Capdevila J, Chaudhari A, Granerod J, Canas LS, Graham MS, Klaser K, Modat M, Molteni E, Murray B, Sudre CH, Davies R, May A, Nguyen LH, Drew DA, Joshi A, Chan AT, Cramer JP, Spector T, Wolf J, Ourselin S, Steves CJ, and Loeliger AE

Subjects
COVID-19 Vaccines, Fever, Humans, Prospective Studies, SARS-CoV-2, COVID-19
Abstract

Objectives: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health., Methods: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity., Findings: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC., Interpretation: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings., Competing Interests: Declaration of Competing Interest Potential conflicts of interest. JW, RD, JCP, and AM are employees of Zoe Global Ltd. ATC reports grants from Massachusetts Consortium on Pathogen Readiness during the conduct of the study, personal fees from Pfizer Inc., and grants and personal fees from Bayer Pharma; CEPI (authors AC, JG, JPC, AEL) funds clinical trials of COVID-19 vaccines. All other authors declare no competing interests., (Copyright © 2021. Published by Elsevier Ltd.)

Published
2021
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17. FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis.

Author

Goodkin O, Prados F, Vos SB, Pemberton H, Collorone S, Hagens MHJ, Cardoso MJ, Yousry TA, Thornton JS, Sudre CH, and Barkhof F

Subjects
Atrophy pathology, Brain diagnostic imaging, Brain pathology, Gray Matter diagnostic imaging, Gray Matter pathology, Humans, Magnetic Resonance Imaging, Leukoaraiosis, Multiple Sclerosis diagnostic imaging, Multiple Sclerosis pathology
Abstract

Background: MRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification., Objectives: We address an aspect of this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations., Methods: We used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, 'manual 1' guided by consensus reading of CIS-specific lesions and 'manual 2' by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1 + FLAIR) and FLAIR-only methods., Results: WM lesion volumes were comparable at 1.5T between 'manual 2' and FLAIR-only methods and at 3T between 'manual 2', T1 + FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α = 1.029, R 2  = 0.997, standard error (SE) = 0.007; 3T: α = 1.019, R 2  = 0.998, SE = 0.006). Age-associated change in cortical GM volume was a significant covariate in both T1 (p = 0.001) and FLAIR-only (p = 0.005) methods, confirming the expected relationship between age and GM volume for FLAIR-only segmentations., Conclusions: FLAIR-only automated segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. Imaging protocol harmonisation and validation with other MS phenotypes could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2021
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18. Bullseye's representation of cerebral white matter hyperintensities.

Author

Sudre CH, Gomez Anson B, Davagnanam I, Schmitt A, Mendelson AF, Prados F, Smith L, Atkinson D, Hughes AD, Chaturvedi N, Cardoso MJ, Barkhof F, Jaeger HR, and Ourselin S

Subjects
Aged, Female, Humans, Imaging, Three-Dimensional, Male, Leukoaraiosis diagnostic imaging, Leukoaraiosis pathology, Magnetic Resonance Imaging methods
Abstract

Background and Purpose: Visual rating scales have limited capacities to depict the regional distribution of cerebral white matter hyperintensities (WMH). We present a regional-zonal volumetric analysis alongside a visualization tool to compare and deconstruct visual rating scales., Materials and Methods: 3D T1-weighted, T2-weighted spin-echo and FLAIR images were acquired on a 3T system, from 82 elderly participants in a population-based study. Images were automatically segmented for WMH. Lobar boundaries and distance to ventricular surface were used to define white matter regions. Regional-zonal WMH loads were displayed using bullseye plots. Four raters assessed all images applying three scales. Correlations between visual scales and regional WMH as well as inter and intra-rater variability were assessed. A multinomial ordinal regression model was used to predict scores based on regional volumes and global WMH burdens., Results: On average, the bullseye plot depicted a right-left symmetry in the distribution and concentration of damage in the periventricular zone, especially in frontal regions. WMH loads correlated well with the average visual rating scores (e.g. Kendall's tau [Volume, Scheltens]=0.59 CI=[0.53 0.62]). Local correlations allowed comparison of loading patterns between scales and between raters. Regional measurements had more predictive power than global WMH burden (e.g. frontal caps prediction with local features: ICC=0.67 CI=[0.53 0.77], global volume=0.50 CI=[0.32 0.65], intra-rater=0.44 CI=[0.23 0.60])., Conclusion: Regional-zonal representation of WMH burden highlights similarities and differences between visual rating scales and raters. The bullseye infographic tool provides a simple visual representation of regional lesion load that can be used for rater calibration and training., (Copyright © 2017 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Published
2018
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