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Olivier Gallinato

Numerical workflow of irreversible electroporation for deep-seated tumor
2019 · Olivier Gallinato, Baudouin Denis de Senneville, et al. · Physics in Medicine and Biology · 44
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Evaluation of conventional and deep learning based image harmonization methods in radiomics studies
2021 · F Tixier, V Jaouen, et al. · Physics in Medicine and Biology · 37
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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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Tumor growth model of ductal carcinoma: from in situ phase to stroma invasion
2017 · Olivier Gallinato, Thierry Colin, et al. · Journal of Theoretical Biology · 23
Free boundary problem for cell protrusion formations: theoretical and numerical aspects
2016 · Olivier Gallinato, Masahito Ohta, et al. · Journal of Mathematical Biology · 22
Superconvergent second order Cartesian method for solving free boundary problem for invadopodia formation
2017 · Olivier Gallinato, Clair Poignard · Journal of Computational Physics · 20
Numerical modelling challenges for clinical electroporation ablation technique of liver tumors
2020 · Olivier Gallinato, Baudouin Denis de Senneville, et al. · Mathematical Modelling of Natural Phenomena · 19
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Standardization of Multicentric Image Datasets with Generative Adversarial Networks
2019 · Hognon, Clément, Tixier, Florent, et al. · HAL · 16
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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Contrastive image adaptation for acquisition shift reduction in medical imaging
2023 · Clément Hognon, Pierre-Henri Conze, et al. · Artificial Intelligence in Medicine · 11
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Clinical, Dosimetric and Radiomic Features Predictive of Lung Toxicity After (Chemo)Radiotherapy
2024 · Cécile Evin, Léo Razakamanantsoa, et al. · Clinical Lung Cancer · 10
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Trustworthy AI for stage IV non-small cell lung cancer: Automatic segmentation and uncertainty quantification
2025 · Sacha Dedeken, Pierre-Henri Conze, et al. · Computerized Medical Imaging and Graphics · 5
Irreversible Electroporation: Disappearance of Observable Changes at Imaging Does Not Always Imply Complete Reversibility of the Underlying Causal Tissue Changes
2016 · Olivier Seror, Clair Poignard, et al. · Radiology · 5
Multimodal machine learning model prediction of complete pathological response to neoadjuvant chemotherapy in triple-negative breast cancer.
2022 · David Groheux, Loïc Ferrer, et al. · Journal of Clinical Oncology · 4
Influence of gradient difference loss on MR to PET brain image synthesis using GANs
2020 · Clément Hognon, Florent Tixier, et al. · 3
Méthodes de différences finies superconvergentes sur grille cartésienne pour les équations de Poisson dans des domaines bi-dimensionels
2015 · Gallinato, Olivier, Poignard, Clair · HAL · 3
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
1916P AI-powered multimodal prediction of response in 1L non-mutated mNSCLC using routine pre-treatment data (DEEP Lung-IV)
2025 · J. Cadranel, T. Colin, et al. · Annals of Oncology · 1
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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554: Clinical, dosimetric and radiomics features predictive of lung toxicity after (chemo)radiotherapy
2024 · Cécile Evin, Léo Razakamanantsoa, et al. · Radiotherapy and Oncology · 1
Modeling of cancer phenomena and superconvergent methods for the resolution of interface problems on Cartesian grid
2016 · Olivier Gallinato Contino · theses.fr (ABES) · 1
207P AI-driven digital twins for prognosis prediction using routine multimodal data in mNSCLC across actionable EGFR or ALK alterations, and driver-negative subgroups
2026 · J. Cadranel, A. Zarkua, et al. · ESMO Real World Data and Digital Oncology
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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.
Works count: 34
Citation count: 266
H-index: 10
I10-index: 11
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