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517 results on '"Lassau, N"'

1. Three artificial intelligence data challenges based on CT and MRI

Author

Lassau, N., Bousaid, I., Chouzenoux, E., Lamarque, J.P., Charmettant, B., Azoulay, M., Cotton, F., Khalil, A., Lucidarme, O., Pigneur, F., Benaceur, Y., Sadate, A., Lederlin, M., Laurent, F., Chassagnon, G., Ernst, O., Ferreti, G., Diascorn, Y., Brillet, P.Y., Creze, M., Cassagnes, L., Caramella, C., Loubet, A., Dallongeville, A., Abassebay, N., Ohana, M., Banaste, N., Cadi, M., Behr, J., Boussel, L., Fournier, L., Zins, M., Beregi, J.P., Luciani, A., Cotten, A., and Meder, J.F.

Published
2020
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2. Artificial intelligence solution to classify pulmonary nodules on CT

Author

Blanc, D., Racine, V., Khalil, A., Deloche, M., Broyelle, J.-A., Hammouamri, I., Sinitambirivoutin, E., Fiammante, M., Verdier, E., Besson, T., Sadate, A., Lederlin, M., Laurent, F., Chassagnon, G., Ferretti, G., Diascorn, Y., Brillet, P.-Y., Cassagnes, Lucie, Caramella, C., Loubet, A., Abassebay, N., Cuingnet, P., Ohana, M., Behr, J., Ginzac, A., Veyssiere, H., Durando, X., Bousaïd, I., Lassau, N., and Brehant, J.

Published
2020
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3. Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI

Author

Brochet, B., Casey, R., Cotton, F., De Sèze, J., Douek, P., Guillemin, F., Laplaud, D., Lebrun-Frenay, C., Mansuy, L., Moreau, T., Olaiz, J., Pelletier, J., Rigaud-Bully, C., Stankoff, B., Vukusic, S., Marignier, R., Debouverie, M., Edan, G., Ciron, J., Ruet, A., Collongues, N., Lubetzki, C., Vermersch, P., Labauge, P., Defer, G., Cohen, M., Fromont, A., Wiertlewsky, S., Berger, E., Clavelou, P., Audoin, B., Giannesini, C., Gout, O., Thouvenot, E., Heinzlef, O., Al-Khedr, A., Bourre, B., Casez, O., Cabre, P., Montcuquet, A., Créange, A., Camdessanché, J.-P., Faure, J., Maurousset, A., Patry, I., Hankiewicz, K., Pottier, C., Maubeuge, N., Labeyrie, C., Nifle, C., Ameli, R., Anxionnat, R., Attye, A., Bannier, E., Barillot, C., Ben Salem, D., Boncoeur-Martel, M.-P., Bonneville, F., Boutet, C., Brisset, J.-C., Cervenanski, F., Claise, B., Commowick, O., Constans, J.-M., Dardel, P., Desal, H., Dousset, Vincent, Durand-Dubief, F., Ferre, J.-C., Gerardin, E., Glattard, T., Grand, S., Grenier, T., Guillevin, R., Guttmann, C., Krainik, A., Kremer, S., Lion, S., Menjot de Champfleur, N., Mondot, L., Outteryck, O., Pyatigorskaya, N., Pruvo, J.-P., Rabaste, S., Ranjeva, J.-P., Roch, J.-A., Sadik, J.C., Sappey-Marinier, D., Savatovsky, J., Tanguy, J.-Y., Tourbah, A., Tourdias, T., Roca, P., Colas, L., Tucholka, A., Rubini, P., Cackowski, S., Ding, J., Budzik, J.-F., Renard, F., Doyle, S., Barbier, E.L., Bousaid, I., Lassau, N., and Verclytte, S.

Published
2020
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5. 6O Subcutaneous fat mass predicts progression-free survival in patients treated with antibody drug conjugates in early phase clinical trials

Author

Delaye, M., primary, Lawrance, L., additional, Belkouchi, Y., additional, Decazes, P., additional, Wirth, F., additional, Bône, A., additional, Ouali, K., additional, Hollebecque, A., additional, Smolenschi, C., additional, Champiat, S., additional, Danlos, F-X., additional, Beshiri, K., additional, Sakkal, M., additional, Massard, C., additional, Vera, P., additional, Marabelle, A., additional, Aix, S. Ponce, additional, Lassau, N., additional, Ammari, S., additional, and Baldini, C., additional

Published
2024
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6. Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI

Author

Lassau, N., Estienne, T., de Vomecourt, P., Azoulay, M., Cagnol, J., Garcia, G., Majer, M., Jehanno, E., Renard-Penna, R., Balleyguier, C., Bidault, F., Caramella, C., Jacques, T., Dubrulle, F., Behr, J., Poussange, N., Bocquet, J., Montagne, S., Cornelis, F., Faruch, M., Bresson, B., Brunelle, S., Jalaguier-Coudray, A., Amoretti, N., Blum, A., Paisant, A., Herreros, V., Rouviere, O., Si-Mohamed, S., Di Marco, L., Hauger, O., Garetier, M., Pigneur, F., Bergère, A., Cyteval, C., Fournier, L., Malhaire, C., Drape, J.-L., Poncelet, E., Bordonne, C., Cauliez, H., Budzik, J.-F., Boisserie, M., Willaume, T., Molière, S., Peyron Faure, N., Caius Giurca, S., Juhan, V., Caramella, T., Perrey, A., Desmots, F., Faivre-Pierre, M., Abitbol, M., Lotte, R., Istrati, D., Guenoun, D., Luciani, A., Zins, M., Meder, J.-F., and Cotten, A.

Published
2019
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9. Selection of an early biomarker for vascular normalization using dynamic contrast-enhanced ultrasonography to predict outcomes of metastatic patients treated with bevacizumab

Author

Lassau, N., Coiffier, B., Kind, M., Vilgrain, V., Lacroix, J., Cuinet, M., Taieb, S., Aziza, R., Sarran, A., Labbe-Devilliers, C., Gallix, B., Lucidarme, O., Ptak, Y., Rocher, L., Caquot, L.M., Chagnon, S., Marion, D., Luciani, A., Feutray, S., Uzan-Augui, J., Benatsou, B., Bonastre, J., and Koscielny, S.

Published
2016
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12. Better than RECIST and faster than iRECIST: Defining the immunotherapy progression decision score to better manage progressive tumors on immunotherapy

Author

Belkouchi, Y., Talbot, Hugues, Lassau, N., Lawrance, L., Farhane, S., Feki-Mkaouar, R., Vibert, J., Cournède, Paul-Henry, Marabelle, A., Ammari, S., Champiat, S., OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, Cancer Campus, Direction de la recherche [Gustave Roussy], Institut Gustave Roussy (IGR), Mathématiques et Informatique pour la Complexité et les Systèmes (MICS), CentraleSupélec-Université Paris-Saclay, Département d’Innovation Thérapeutique et essais précoces [Gustave Roussy] (DITEP), and Département d'imagerie médicale [Gustave Roussy]

Subjects
[SDV]Life Sciences [q-bio]
Abstract

International audience

Published
2022
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16. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers (Jan , 10.1007/s00330-020-07598-8, 2021)

Author

Fournier, L., Costaridou, L., Bidaut, L., Michoux, N., Lecouvet, F.E., Geus-Oei, L.F. de, Boellaard, R., Oprea-Lager, D.E., Obuchowski, N.A., Caroli, A., Kunz, W.G., Oei, E.H., O'Connor, J.P.B., Mayerhoefer, M.E., Franca, M., Alberich-Bayarri, A., Deroose, C.M., Loewe, C., Manniesing, R., Caramella, C., Lopci, E., Lassau, N., Persson, A., Achten, R., Rosendahl, K., Clement, O., Kotter, E., Golay, X., Smits, M., Dewey, M., Sullivan, D.C., Lugt, A. van der, deSouza, N.M., and European Soc Radiology

Subjects
GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries)
Abstract

A Correction to this paper has been published

Published
2021
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17. Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

Author

Fournier, L., Costaridou, L., Bidaut, L., Michoux, N., Lecouvet, F.E., Geus-Oei, L.F. de, Boellaard, R., Oprea-Lager, D.E., Obuchowski, N.A., Caroli, A., Kunz, W.G., Oei, E.H., O'Connor, J.P.B., Mayerhoefer, M.E., Franca, M., Alberich-Bayarri, A., Deroose, C.M., Loewe, C., Manniesing, R., Caramella, C., Lopci, E., Lassau, N., Persson, A., Achten, R., Rosendahl, K., Clement, O., Kotter, E., Golay, X., Smits, M., Dewey, M., Sullivan, D.C., Lugt, A. van der, and deSouza, N.M.

Subjects
Clinical trial, Statistics and numerical data, Validation studies, Radiology, Standardization
Abstract

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory. ispartof: EUROPEAN RADIOLOGY vol:31 issue:8 pages:6001-6012 ispartof: location:Germany status: published

Published
2021
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18. Prolonged SARS-CoV-2 RNA virus shedding and lymphopenia are hallmarks of COVID-19 in cancer patients with poor prognosis

Author

Goubet AG, Dubuisson A, Geraud A, Danlos FX, Terrisse S, Silva CAC, Drubay D, Touri L, Picard M, Mazzenga M, Silvin A, Dunsmore G, Haddad Y, Pizzato E, Ly P, Flament C, Melenotte C, Solary E, Fontenay M, Garcia G, Balleyguier C, Lassau N, Maeurer M, Grajeda-Iglesias C, Nirmalathasan N, Aprahamian F, Durand S, Kepp O, Ferrere G, Thelemaque C, Lahmar I, Fahrner JE, Meziani L, Ahmed-Belkacem A, Saïd

Published
2021
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19. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers

Author

Fournier, L., Costaridou, L., Bidaut, L., Michoux, N., Lecouvet, F.E., Geus-Oei, L.F. de, Boellaard, R., Oprea-Lager, D.E., Obuchowski, N.A., Caroli, A., Kunz, W.G., Oei, E.H., O'Connor, J.P.B., Mayerhoefer, M.E., Franca, M., Alberich-Bayarri, A., Deroose, C.M., Loewe, C., Manniesing, R., Caramella, C., Lopci, E., Lassau, N., Persson, A., Achten, R., Rosendahl, K., Clement, O., Kotter, E., Golay, X., Smits, Marion, Dewey, M., Sullivan, D.C., Lugt, A. van der, Desouza, N.M., Fournier, L., Costaridou, L., Bidaut, L., Michoux, N., Lecouvet, F.E., Geus-Oei, L.F. de, Boellaard, R., Oprea-Lager, D.E., Obuchowski, N.A., Caroli, A., Kunz, W.G., Oei, E.H., O'Connor, J.P.B., Mayerhoefer, M.E., Franca, M., Alberich-Bayarri, A., Deroose, C.M., Loewe, C., Manniesing, R., Caramella, C., Lopci, E., Lassau, N., Persson, A., Achten, R., Rosendahl, K., Clement, O., Kotter, E., Golay, X., Smits, Marion, Dewey, M., Sullivan, D.C., Lugt, A. van der, and Desouza, N.M.

Abstract

Contains fulltext : 238654.pdf (Publisher’s version ) (Open Access), Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: * Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the da

Published
2021

20. Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

Author

Fournier, L. (Laure), Costaridou, L. (Lena), Bidaut, L. (Luc), Michoux, N. (Nicolas), Lecouvet, F.E. (Frédéric), de Geus-Oei, L.-F. (Lioe-Fee), Boellaard, R. (Ronald), Oprea-Lager, D.E. (Daniela E.), Obuchowski, N. (Nancy), Caroli, A. (Anna), Kunz, W.G. (Wolfgang), Oei, E.H.G. (Edwin), O’Connor, J.P.B. (James P. B.), Mayerhoefer, M.E. (Marius E.), Franca, M. (Manuela), Alberich-Bayarri, A. (Angel), Deroose, C.M. (Christophe M.), Loewe, C. (Christian), Manniesing, R. (Rashindra), Caramella, C. (Caroline), Lopci, E. (Egesta), Lassau, N. (Nathalie), Persson, A. (Anders), Achten, R. (Rik), Rosendahl, K. (Karen), Clement, O. (Olivier), Kotter, E. (Elmar), Golay, A. (Alain), Smits, M. (Marion), Dewey, T.M. (Todd M.), Sullivan, D.C. (Daniel C.), Lugt, A. (Aad) van der, DeSouza, N.M. (Nandita M.), Fournier, L. (Laure), Costaridou, L. (Lena), Bidaut, L. (Luc), Michoux, N. (Nicolas), Lecouvet, F.E. (Frédéric), de Geus-Oei, L.-F. (Lioe-Fee), Boellaard, R. (Ronald), Oprea-Lager, D.E. (Daniela E.), Obuchowski, N. (Nancy), Caroli, A. (Anna), Kunz, W.G. (Wolfgang), Oei, E.H.G. (Edwin), O’Connor, J.P.B. (James P. B.), Mayerhoefer, M.E. (Marius E.), Franca, M. (Manuela), Alberich-Bayarri, A. (Angel), Deroose, C.M. (Christophe M.), Loewe, C. (Christian), Manniesing, R. (Rashindra), Caramella, C. (Caroline), Lopci, E. (Egesta), Lassau, N. (Nathalie), Persson, A. (Anders), Achten, R. (Rik), Rosendahl, K. (Karen), Clement, O. (Olivier), Kotter, E. (Elmar), Golay, A. (Alain), Smits, M. (Marion), Dewey, T.M. (Todd M.), Sullivan, D.C. (Daniel C.), Lugt, A. (Aad) van der, and DeSouza, N.M. (Nandita M.)

Abstract

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality

Published
2021
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35. Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI

Author

Roca, P., primary, Attye, A., additional, Colas, L., additional, Tucholka, A., additional, Rubini, P., additional, Cackowski, S., additional, Ding, J., additional, Budzik, J.-F., additional, Renard, F., additional, Doyle, S., additional, Barbier, E.L., additional, Bousaid, I., additional, Casey, R., additional, Vukusic, S., additional, Lassau, N., additional, Verclytte, S., additional, Cotton, F., additional, Brochet, B., additional, De Sèze, J., additional, Douek, P., additional, Guillemin, F., additional, Laplaud, D., additional, Lebrun-Frenay, C., additional, Mansuy, L., additional, Moreau, T., additional, Olaiz, J., additional, Pelletier, J., additional, Rigaud-Bully, C., additional, Stankoff, B., additional, Marignier, R., additional, Debouverie, M., additional, Edan, G., additional, Ciron, J., additional, Ruet, A., additional, Collongues, N., additional, Lubetzki, C., additional, Vermersch, P., additional, Labauge, P., additional, Defer, G., additional, Cohen, M., additional, Fromont, A., additional, Wiertlewsky, S., additional, Berger, E., additional, Clavelou, P., additional, Audoin, B., additional, Giannesini, C., additional, Gout, O., additional, Thouvenot, E., additional, Heinzlef, O., additional, Al-Khedr, A., additional, Bourre, B., additional, Casez, O., additional, Cabre, P., additional, Montcuquet, A., additional, Créange, A., additional, Camdessanché, J.-P., additional, Faure, J., additional, Maurousset, A., additional, Patry, I., additional, Hankiewicz, K., additional, Pottier, C., additional, Maubeuge, N., additional, Labeyrie, C., additional, Nifle, C., additional, Ameli, R., additional, Anxionnat, R., additional, Bannier, E., additional, Barillot, C., additional, Ben Salem, D., additional, Boncoeur-Martel, M.-P., additional, Bonneville, F., additional, Boutet, C., additional, Brisset, J.-C., additional, Cervenanski, F., additional, Claise, B., additional, Commowick, O., additional, Constans, J.-M., additional, Dardel, P., additional, Desal, H., additional, Dousset, Vincent, additional, Durand-Dubief, F., additional, Ferre, J.-C., additional, Gerardin, E., additional, Glattard, T., additional, Grand, S., additional, Grenier, T., additional, Guillevin, R., additional, Guttmann, C., additional, Krainik, A., additional, Kremer, S., additional, Lion, S., additional, Menjot de Champfleur, N., additional, Mondot, L., additional, Outteryck, O., additional, Pyatigorskaya, N., additional, Pruvo, J.-P., additional, Rabaste, S., additional, Ranjeva, J.-P., additional, Roch, J.-A., additional, Sadik, J.C., additional, Sappey-Marinier, D., additional, Savatovsky, J., additional, Tanguy, J.-Y., additional, Tourbah, A., additional, and Tourdias, T., additional

Published
2020
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41. Kindey Cortex Segmentation in 2D CT with U-Nets Ensemble Aggregation

Author

Couteaux, Vincent, Si-Mohamed, S., Renard-Penna, R., Nempont, O., Lefevre, T., Popoff, A., Pizaine, Guillaume, Villain, N., Bloch, Isabelle, Behr, J., Bellin, M.-F., Roy, C., Rouvière, O., Montagne, S., Lassau, N., Boussel, L., Image, Modélisation, Analyse, GEométrie, Synthèse (IMAGES), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), and Télécom ParisTech

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

47. Die EFSUMB-Leitlinien und Empfehlungen für den klinischen Einsatz des kontrastverstärkten Ultraschalls (CEUS) bei nicht-hepatischen Anwendungen: Update 2017 (Kurzversion)

Author

Sidhu, P.S., Cantisani, V., Dietrich, C.F., Gilja, O.H., Saftoiu, A., Bartels, E., Bertolotto, M., Calliada, F., Clevert, D.-A., Cosgrove, D., Deganello, A., D'Onofrio, M., Drudi, F.M., Freeman, S., Harvey, C., Jenssen, C., Jung, E.-M., Klauser, A.S., Lassau, N., Meloni, M.F., Leen, E., Nicolau, C., Nolsoe, C., Piscaglia, F., Prada, F., Prosch, H., Radzina, M., Savelli, L., Weskott, H.-P., Wijkstra, H., Signal Processing Systems, and Biomedical Diagnostics Lab

Subjects
urinary tract, Adult, Male, neurology, Liver Neoplasms, head/neck, Sulfur Hexafluoride, Contrast Media, musculoskeletal system, Liver/diagnostic imaging, head, Image Enhancement, neck, vascular, Ultrasonography/standards, Practice Guidelines as Topic, Humans, Female, Phospholipids
Abstract

The updated version of the EFSUMB guidelines on the application of non-hepatic contrast-enhanced ultrasound (CEUS) deals with the use of microbubble ultrasound contrast outside the liver in the many established and emerging applications.

Published
2018

48. Échographie moléculaire dans le cancer du rein : modèle pré-clinique de suivi des traitements anti-angiogéniques à partir de microbulles couplées au VEGFR1 et FSHR

Author

Alexandre, I., primary, Leguerney, I., additional, Cournède, P., additional, Irani, J., additional, Ferlicot, S., additional, Sebrié, C., additional, Benatsou, B., additional, Jourdain, L., additional, Guillot, G., additional, Pitre-champagnat, S., additional, Patard, J., additional, and Lassau, N., additional

Published
2019
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50. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Short Version)

Author

Sidhu, P.S., Cantisani, V., Dietrich, C.F., Gilja, O.H., Saftoiu, A., Bartels, E., Bertolotto, M., Calliada, F., Clevert, D.-A., Cosgrove, D., Deganello, A., D'Onofrio, M., Drudi, F.M., Freeman, S., Harvey, C., Jenssen, C., Jung, E.-M., Klauser, A.S., Lassau, N., Meloni, M.F., Leen, E., Nicolau, C., Nolsoe, C., Piscaglia, F., Prada, F., Prosch, H., Radzina, M., Savelli, L., Weskott, H.-P., Wijkstra, H., Sidhu, P.S., Cantisani, V., Dietrich, C.F., Gilja, O.H., Saftoiu, A., Bartels, E., Bertolotto, M., Calliada, F., Clevert, D.-A., Cosgrove, D., Deganello, A., D'Onofrio, M., Drudi, F.M., Freeman, S., Harvey, C., Jenssen, C., Jung, E.-M., Klauser, A.S., Lassau, N., Meloni, M.F., Leen, E., Nicolau, C., Nolsoe, C., Piscaglia, F., Prada, F., Prosch, H., Radzina, M., Savelli, L., Weskott, H.-P., and Wijkstra, H.

Abstract

The updated version of the EFSUMB guidelines on the application of non-hepatic contrast-enhanced ultrasound (CEUS) deals with the use of microbubble ultrasound contrast outside the liver in the many established and emerging applications.

Published
2018

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