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Félix Balazard

Showing results (1-10 of 8) with videos related to

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Journal of the National Cancer Institute|November 12, 2023
Response to SorscherFélix Balazard, Aurélie Bertaut, Ines Vaz-Luis, et al.
Trials|June 6, 2023
More efficient and inclusive time-to-event trials with covariate adjustment: a simulation studyRaphaëlle Momal, Honghao Li, Paul Trichelair, et al.
Future Oncology (London, England)|July 27, 2023
The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumorsMarianne Pavel, Clarisse Dromain, Maxime Ronot, et al.
Future Oncology (London, England)|July 27, 2023
Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumorsClarisse Dromain, Marianne Pavel, Maxime Ronot, et al.
ESC Heart Failure|April 9, 2026
CONFIDENT-HFpEF: A Machine Learning-Based Risk Stratification for Mortality and Hospitalization Using Multimodal Real-World DataMarat Fudim, Vanessa Van Empel, Tobias Zehnder, et al.
Nature Communications|August 13, 2025
FedECA: federated external control arms for causal inference with time-to-event data in distributed settingsJean Ogier du Terrail, Quentin Klopfenstein, Honghao Li, et al.
Journal of the National Cancer Institute|July 12, 2023
Adjuvant endocrine therapy uptake, toxicity, quality of life, and prediction of early discontinuationFélix Balazard, Aurélie Bertaut, Élise Bordet, et al.
Nature Medicine|January 19, 2023
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancerJean Ogier du Terrail, Armand Leopold, Clément Joly, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Journal of the National Cancer Institute|November 12, 2023
Response to SorscherFélix Balazard, Aurélie Bertaut, Ines Vaz-Luis, et al.
Trials|June 6, 2023
More efficient and inclusive time-to-event trials with covariate adjustment: a simulation studyRaphaëlle Momal, Honghao Li, Paul Trichelair, et al.
Future Oncology (London, England)|July 27, 2023
The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumorsMarianne Pavel, Clarisse Dromain, Maxime Ronot, et al.
Future Oncology (London, England)|July 27, 2023
Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumorsClarisse Dromain, Marianne Pavel, Maxime Ronot, et al.
ESC Heart Failure|April 9, 2026
CONFIDENT-HFpEF: A Machine Learning-Based Risk Stratification for Mortality and Hospitalization Using Multimodal Real-World DataMarat Fudim, Vanessa Van Empel, Tobias Zehnder, et al.
Nature Communications|August 13, 2025
FedECA: federated external control arms for causal inference with time-to-event data in distributed settingsJean Ogier du Terrail, Quentin Klopfenstein, Honghao Li, et al.
Journal of the National Cancer Institute|July 12, 2023
Adjuvant endocrine therapy uptake, toxicity, quality of life, and prediction of early discontinuationFélix Balazard, Aurélie Bertaut, Élise Bordet, et al.
Nature Medicine|January 19, 2023
Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancerJean Ogier du Terrail, Armand Leopold, Clément Joly, et al.
Pageof 1