CV du
consultant

BDD

Data

Connaissances fonctionnelles

machine learning statistiques Traitement de données Médical

Langages

PYTHON C

Méthodes

Management

2019-2023 Ph.D. Candidate (Radiothérapie Moléculaire et Innovation Thérapeutique),
Université Paris-Saclay, Gustave Roussy Campus Cancer, France
Radiomics and Artificial Intelligence for personalized treatment of HPV-induced
cancers
• Image preprocessing
• Development of a deep learning-based standardization method for MR
images
• Development of common [18F]-FDG PET radiomic signature to predict
survival in patients with HPV-induced cancers
• Development of a radiomic signature of lymphocyte infiltrate in patients
with locally advanced cervical cancer
2018-2019 Research Engineer (INSERM U1030 Radiothérapie Moléculaire et
Innovation Thérapeutique), Gustave Roussy Campus Cancer, France
Assessment of the validity of clinical margins for Tomotherapy treatments by
means of a Deformable Image Registration (DIR) algorithm [1]
2018 Research trainee (Danish Center for Particle Therapy), Aarhus University
Hospital, Aarhus, Denmark
Dosimetric impact of interfractional target motion in locally advanced cervical
cancer patients treated with spot scanning proton therapy using an internal target
volume strategy [2]
2017 Intern, Dosisoft, France
• Validation of diagnostic algorithms for Alzheimer's and Parkinson's
diseases
• Contributed to the development of the graphical user interface
2 | P a g e
EDUCATION
2019-2023 Ph.D. in Medical Physics, Université Paris-Saclay, France
Thesis: Radiomics and Artificial Intelligence in HPV-induced cancers
Supervisor: Charlotte Robert, Ph.D.
2016-2018 MSc Medical Physics, Université Paris-Saclay, France
Graduated with Honors
2012-2016 BSc Physics, (Physics Department), Voronezh State University, Russia
Graduated with High Honors
2011-2012 Pre-university at Voronezh State University of Technology
2004-2011 High School Diploma Mathematics and Physics (Lycée du Saint Esprit),
Bujumbura, Burundi
Graduated with High Honors
SKILLS
Technical Skills
• Python
• C++
• Machine Learning/Deep Learning
• Data Management
Language Skills
• Kirundi (Native)
• French (Native)
• English (Advanced)
• Russian(Advanced)
TEACHING EXPERIENCE
Lab supervisor, Université Paris-Saclay, France (December 2020-May 2023)
Wave, matter and radiations: Fourier Transform. Faculty of Physics and Applications
Machine learning project supervisor for graduate students, Université Paris-Saclay, France
Radiomics for survival prediction in locally advanced cervical cancers
Lecturer, Université Paris-Saclay, France (December 2020-May 2023)
Applied Physics and Technology in Radiation Therapy
HONORS & AWARDS
2019 Elekta fully funded doctoral thesis
2018 Erasmus scholarship for a research internship at Aarhus University Hospital
2018 Université Paris-Saclay scholarship for best students
2014-2016 Russian Government Scholarship for high achieving students
2011-2016 Burundian Government Scholarship Undergraduate Award.
3 | P a g e
TALKS
• Conference of European Society of Radiation Oncology 2020: Are current margins in locally
advanced cervical cancers treated by Tomotherapy appropriate? ESTRO 2020.
• Interdisciplinary center for Nanotechnologies & Radiation based cancer therapies (INanoTheRad):
[18F]-FDG PET radiomics to predict survival in locally advanced cervical cancers (LACC) and anal
squamous cancer cells (ASCC), University of Paris-Saclay
https://nanotherad.sciencesconf.org/
LIST OF PUBLICATIONS
[1] Are current margins in locally advanced cervical cancers treated by tomotherapy appropriate?
Stephane Niyoteka et al. Radiotherapy and Oncology, 2019.
[2] Impact of interfractional target motion in locally advanced cervical cancer patients treated with spot
scanning proton therapy using an internal target volume strategy.
Stephane Niyoteka et al. Physics and Imaging in Radiation Oncology, 2021.
https://doi: 10.1016/j.phro.2021.01.010.
[3] Automatic Detection and Segmentation of GTV for Locally Advanced Cervical Cancer in T2-
Weighted MRI Images.
Rahimeh Rouhi, Stephane Niyoteka et al. , ESTRO Congress, 2022.
https://www.estro.org/Congresses/ESTRO-2022
[4] AutoComBat: a generic method for harmonizing MRI-based radiomic features.
Alexandre Carré, Enzo Batistella, Stephane Niyoteka, et al. Scientific report, 2022.
https://pubmed-ncbi-nlm-nih-gov.ezproxy.universite-paris-saclay.fr/35882891/
[5] A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced
cancers.
Stephane Niyoteka et al. European Journal of Nuclear Medecine and Molecular Imaging, 2023
(Accepted June 2023).
ARTICLES IN REVIEW:
• Deep learning based MR images standardization for radiomic studies.
Authors: Stephane Niyoteka et al.
• An ensemble deep learning model for automatic segmentation of GTV in locally advanced cervical
cancer.
Authors: Rahimeh Rouhi, Stephane Niyoteka et al.
• Analyzing generalizability and interpretability of automatic GTV segmentation in locally advanced
cervical cancer.
Authors: Rahimeh Rouhi, Stephane Niyoteka et al.


RESEARCH INTERESTS
• Medical Physics
• Artificial Intelligence (Machine learning and Deep learning) applied to images
• Computer vision
• Data management