Portrait of Martin Vallières

Martin Vallières

Associate Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université Sherbrooke, Department of Computer Science
Research Topics
Medical Machine Learning
Website
Google Scholar
LinkedIn
X

Biography

Martin Vallières is an assistant professor in the Department of Computer Science at Université de Sherbrooke and a Canada CIFAR AI Chair since April 2020.

He received a PhD in medical physics from McGill University in 2017, and completed postdoctoral training in France and the U.S. in 2018 and 2019.

Vallières is an expert in the field of radiomics and machine learning in oncology. Over the course of his career, he has developed multiple prediction models for different types of cancers. His main research interest is now focused on the graph-based modelling of heterogeneous medical data for improved precision medicine.

Current Students

Hakima Laribi
PhD - Université de Sherbrooke
Principal supervisor :
Dan Poenaru
Website
Github
Olivier Lefebvre
PhD - Université de Sherbrooke
Principal supervisor :
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… (see more)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