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Author

Guillaume Etchepare

Observed name: G. Etchepare
Observed institution: Sophia Genetics (France)
ORCID: 0009-0004-9410-0252
Observed ORCID: 0009-0004-9410-0252
OpenAlex ID: A5091121396
UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120)
2024 · Gaëlle Margue, Loïc Ferrer, et al. · npj Precision Oncology · 29
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Multimodal machine learning model prediction of “individual” response to immunotherapy in 1L stage IV NSCLC.
2022 · Jacques Cadranel, Louise Sebane, et al. · Journal of Clinical Oncology · 2
1325P TRIDENT: Machine learning (ML) multimodal signatures to identify patients that would benefit most from tremelimumab (T) addition to durvalumab (D) + chemotherapy (CT) with data from the POSEIDON trial
2024 · F. Skoulidis, S. Jabbour, et al. · Annals of Oncology · 1
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Supplementary Figure S2 from Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT)
2026 · Ferdinandos Skoulidis, Salma K. Jabbour, et al.
Supplementary Figure S1 from Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT)
2026 · Ferdinandos Skoulidis, Salma K. Jabbour, et al.
Supplementary Tables S1-S11 from Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT)
2026 · Ferdinandos Skoulidis, Salma K. Jabbour, et al.
Data from Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT)
2026 · Ferdinandos Skoulidis, Salma K. Jabbour, et al.
Supplementary Figure S3 from Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT)
2026 · Ferdinandos Skoulidis, Salma K. Jabbour, et al.
Supplementary Methods from Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT)
2026 · Ferdinandos Skoulidis, Salma K. Jabbour, et al.
Utilizing Machine Learning to Identify Multimodal Signatures for Patients Who Would Benefit from the Addition of Tremelimumab to Durvalumab and Chemotherapy (TRIDENT)
2026 · Ferdinandos Skoulidis, Salma K. Jabbour, et al. · Clinical Cancer Research
Reply to: machine learning in renal cell carcinoma research: promise and pitfalls of ‘renal‐izing’ the potential of artificial intelligence
2023 · Astrid Boulenger de Hauteclocque, Loïc Ferrer, et al. · British Journal of Urology
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Disease-free survival in operated patients with nmRCC using machine learning.
2023 · Gaëlle Margue, Loïc Ferrer, et al. · Journal of Clinical Oncology
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MP64-07 DEVELOPMENT OF AN INDIVIDUAL POSTOPERATIVE PREDICTION MODEL FOR KIDNEY CANCER RECURRENCE USING MACHINE LEARNING (UroCCR STUDY 120)
2023 · Gaëlle Margue, Loïc Ferrer, et al. · The Journal of Urology
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Development of an individual postoperative prediction model for kidney cancer recurrence using machine learning (UroCCR study 120)
2023 · G. Margue, L. Ferrer, et al. · European Urology
Individualized prediction of post-surgical pathologic T3a (pT3a) upstaging risk in localized renal tumors undergoing nephrectomy (UroCCR 15 study).
2022 · Astrid Boulenger de Hautecloque, Loïc Ferrer, et al. · Journal of Clinical Oncology
Works count: 15
Citation count: 32
H-index: 2
I10-index: 1
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