Portrait de Martin Vallières

Martin Vallières

Membre académique associé
Chaire en IA Canada-CIFAR
Professeur adjoint, Université Sherbrooke, Département d'informatique
Sujets de recherche
Apprentissage automatique médical
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Google Scholar
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Biographie

Martin Vallières est professeur adjoint au Département d'informatique de l'Université de Sherbrooke et titulaire d'une chaire en IA Canada-CIFAR depuis avril 2020. Il a obtenu un doctorat en physique médicale de l'Université McGill en 2017, et a suivi une formation postdoctorale en France et aux États-Unis en 2018 et 2019. Il est expert dans le domaine de la radiomique et de l'apprentissage automatique en oncologie. Au cours de sa carrière, il a développé de multiples modèles de prédiction pour différents types de cancer. Son principal intérêt de recherche consiste désormais en la modélisation à base de graphes de données médicales hétérogènes pour améliorer la médecine de précision.

Étudiants actuels

Hakima Laribi
Doctorat - Université de Sherbrooke
Superviseur⋅e principal⋅e :
Dan Poenaru
Site web
Github
Olivier Lefebvre
Doctorat - Université de Sherbrooke
Superviseur⋅e principal⋅e :
Dan Poenaru
Github

Publications

Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma
Elizabeth George
Elizabeth Flagg
Kuan-chun Chang
H. Bai
H. Aerts
Martin Vallières
D. Reardon
R.Y. Huang
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of t… (voir plus)reatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites (n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites (n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index  = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
2022-04-28
American Journal of Neuroradiology (published)
doi.org