CV

You can also find a more detailed version of my CV here.

Education

2020-2024 - Ph.D in Computer science, Institut Curie, PSL University, Paris, France
Multimodal analysis of radiological, pathological, and transcriptomic data for the prediction of immunotherapy outcome in Non-Small Cell Lung Cancer patients
2019-2020 - Master MVA (Mathematics, Vision, Learning), M.Sc, Ecole Normale Supérieure, Paris-Saclay, France
2016-2020 - Diplôme d’ingénieur de l’Ecole polytechnique, Ecole polytechnique, Palaiseau, France
2013-2016 - B.Sc. in Physics & B.Sc. in Mathematics, Sorbonne University, Paris, France

Experience

10/2020-06/2024: Institut Curie, PhD student
- PhD funded by the PaRis Artificial Intelligence Research InstitutE (PRAIRIE)
- Multidisciplinary research project for the multimodal prediction of immunotherapy response in lung cancer (funded by Fondation ARC: 600,000€)
- Supervisors: Irène Buvat & Emmanuel Barillot
04/2020-09/2020: Institut Curie, Research Intern
- Development of inference methods for unraveling associations between radiomic and transcriptomic features for lung cancer patients
- Supervisors: Irène Buvat & Emmanuel Barillot
04/2019-08/2019: Institute for Applied Mathematics, Bonn University, Research Intern
- Development of a stochastic model for the interplay of melanoma cells and immmune cells under immunotherapy
- Prize of the “Mathematical Modeling and Biodiversity” Chair of Ecole polytechnique
- Supervisor: Anton Bovier

Skills

Languages - French (Native) - English (Fluent)
Coding - Python (pytorch, scikit, pandas), R, Java, HTML, LaTeX

Publications

Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer Permalink

Captier, N., Lerousseau, M., Orlhac, F. et al. Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer. Nat Commun 16, 614 (2025).

Similar performance of 8 machine learning models on 71 censored medical datasets: a case for simplicity Permalink

RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models Permalink

Captier N, Orlhac F, Hovhannisyan-Baghdasarian N, Luporsi M, Girard N, Buvat I. RadShap: An Explanation Tool for Highlighting the Contributions of Multiple Regions of Interest to the Prediction of Radiomic Models. J Nucl Med. 2024.

Promising Candidate Prognostic Biomarkers in [18F]FDG PET Images: Evaluation in Independent Cohorts of Non–Small Cell Lung Cancer Patients Permalink

Hovhannisyan-Baghdasarian N, Luporsi M, Captier N, Nioche C, Cuplov V, Woff E, Hegarat N, Livartowski A, Girard N, Buvat I, Orlhac F. Promising Candidate Prognostic Biomarkers in [18F]FDG PET Images: Evaluation in Independent Cohorts of Non-Small Cell Lung Cancer Patients. J Nucl Med. 2024.

BIODICA: a computational environment for Independent Component Analysis of omics data Permalink

Captier N, Merlevede J, Molkenov A, Seisenova A, Zhubanchaliyev A, Nazarov PV, Barillot E, Kairov U, Zinovyev A. BIODICA: a computational environment for Independent Component Analysis of omics data. Bioinformatics. 2022.

Multimodal and integrative analysis of genomics, radiomics and clinical data for the prediction of response to immunotherapy in lung cancer Permalink

October 27, 2021

Invited talk at Conference IBOMAN, Paris, France

Teaching

Machine Learning with Orange Permalink

Service activities

2020-Present - Peer-reviewing for Bioinformatics advances, BMC Bioinformatics, and the Journal of Nuclear Medicine (JNM)

Nicolas Captier

CV