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Loïc Ferrer

Observed names: L. Ferrer, Loic Ferrer
Observed institutions: Université de Bordeaux, Inserm, Bordeaux Population Health, Hôpital René Huguenin, Sophia Genetics (France), Sophia Genetics (Switzerland), Institut Curie
ORCID: 0000-0002-2107-0118
Observed ORCID: 0000-0002-2107-0118
OpenAlex ID: A5102944358
Individual dynamic predictions using landmarking and joint modelling: Validation of estimators and robustness assessment
2018 · Loïc Ferrer, Hein Putter, et al. · Statistical Methods in Medical Research · 83
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Joint Model for Left-Censored Longitudinal Data, Recurrent Events and Terminal Event: Predictive Abilities of Tumor Burden for Cancer Evolution With Application to the FFCD 2000–05 Trial
2016 · Agnieszka Król, Loïc Ferrer, et al. · Biometrics · 52
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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FDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
2025 · David Groheux, Loïc Ferrer, et al. · Cancers · 23
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Role of 3D volume growth rate for drug activity evaluation in meningioma clinical trials: the example of the CEVOREM study
2021 · Thomas Graillon, Loic Ferrer, et al. · Neuro-Oncology · 22
Machine‐learning approach for prediction of pT3a upstaging and outcomes of localized renal cell carcinoma ( UroCCR ‐15)
2023 · Astrid Boulenger de Hauteclocque, Loïc Ferrer, et al. · British Journal of Urology · 19
A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)
2023 · Cassandre Garnier, Loïc Ferrer, et al. · Diagnostics · 14
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Text Mining in Electronic Medical Records Enables Quick and Efficient Identification of Pregnancy Cases Occurring After Breast Cancer
2019 · Julie Labrosse, Thanh Lam, et al. · JCO Clinical Cancer Informatics · 11
Impact of model choice when studying the relationship between blood pressure variability and risk of stroke
2021 · H. de Courson, L. Ferrer, et al. · Revue d Épidémiologie et de Santé Publique · 2
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Modélisation et prédiction conjointe de différents risques de progression de cancer à partir des mesures répétées de biomarqueurs
2017 · Loic Ferrer · theses.fr (ABES) · 2
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Prediction of real-world progression free survival (rwPFS) using a multimodal machine learning (ML) model for patients with HR+ HER2- metastatic breast cancer (mBC) undergoing first line (1L) treatment with cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) and endocrine therapy (ET).
2025 · Pedram Razavi, Julia Ah-Reum An, et al. · Journal of Clinical Oncology · 1
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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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Joint Models for Longitudinal and Time to Event Data
2021 · Hélène Jacqmin-Gadda, Loïc Ferrer, et al. · Chapman & Hall/CRC eBooks · 1
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.
Machine Learning Models Combining [68Ga]Ga-PSMA-11 PET Radiomics and Clinical Features to Predict Overall Survival for Metastatic Castration-resistant Prostate Cancer Treated with [177Lu]Lu-PSMA Radioligand Therapy
2026 · Lucas Edouard, Léa Papillon, et al. · European Urology Oncology
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
Individualized recurrence risk prediction as a challenge to conventional eligibility criteria in phase III adjuvant immunotherapy trials for renal cell carcinoma.
2026 · Thierry Colin, Gaëlle Margue, et al. · Journal of Clinical Oncology
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318P AI-driven dynamic prediction of prognosis in mNSCLC using CT scans and multimodal real-world data
2025 · T. Colin, J. Cadranel, et al. · ESMO Real World Data and Digital Oncology
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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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Works count: 32
Citation count: 260
H-index: 8
I10-index: 8
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