Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Gerlinde Averous"'
Autor:
Samuel Aymard, Edmond Rust, Ashjan Kaseb, David Liu, Fabrice Hubele, Benoit Romain, Gerlinde Averous, Cecile Brigand, Alessio Imperiale
Publikováno v:
Cancers, Vol 16, Iss 1, p 233 (2024)
We analyzed whether preoperative 18F-FDG PET/CT adds to conventional primary staging in patients with presumed non-metastatic colonic cancer (CC). The prognostic role of 18F-FDG uptake in the primary tumor was evaluated after a mean follow-up of 15 y
Externí odkaz:
https://doaj.org/article/94e3a136ace848738714cb4f9fbe0664
Autor:
Samuel Aymard, Benjamin Leroy-Freschini, Ashjan Kaseb, David Marx, Mehdi Helali, Gerlinde Averous, Valérie Betz, Sophie Riehm, Michel Vix, Peggy Perrin, Alessio Imperiale
Publikováno v:
Diagnostics, Vol 13, Iss 8, p 1374 (2023)
Hyperparathyroidism (HPT) in patients with chronic kidney disease (CKD) includes secondary (sHPT) and tertiary hyperparathyroidism (tHPT). Considering that the role of preoperative imaging in the clinical setting is controversial, in the present stud
Externí odkaz:
https://doaj.org/article/e8176f3468d941bf8cbdb509049f438a
Autor:
Adrien Latgé, Frédéric Boisson, Ali Ouadi, Gerlinde Averous, Lionel Thomas, Alessio Imperiale, David Brasse
Publikováno v:
Molecules, Vol 27, Iss 15, p 4869 (2022)
64CuCl2 is an economic radiotracer for oncologic PET investigations. In the present study, we characterized the uptake of 64CuCl2 in vivo by µPET/CT in an allograft 4T1-related mouse model (BALB/c) of advanced breast cancer. 18F-FDG was used as a co
Externí odkaz:
https://doaj.org/article/c12838ef0ad248ffa56dc48e8e7ebd3e
Autor:
Kleanthis Marios Papadopoulos, Panagiotis Barmpoutis, Tania Stathaki, Vahan Kepenekian, Peggy Dartigues, Séverine Valmary-Degano, Claire Illac-Vauquelin, Gerlinde Avérous, Anne Chevallier, Marie-Hélène Laverriere, Laurent Villeneuve, Olivier Glehen, Sylvie Isaac, Juliette Hommell-Fontaine, Francois Ng Kee Kwong, Nazim Benzerdjeb
Publikováno v:
BioMedInformatics, Vol 4, Iss 1, Pp 823-836 (2024)
Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning’s potential in this domain, no prior rese
Externí odkaz:
https://doaj.org/article/bfe1281c8ad24125b9372ac31f61caee