Martin Vallières

Martin Vallières - GRIIS

Assistant professor in computer science at Université de Sherbrooke

A specialist in precision oncology, Martin Vallières studies and develops computer models that will make it possible to personalize cancer treatment. Using radiomics (the quantitative analysis of medical images) and machine learning in oncology, he has built predictive models for different types of cancer. His current work focuses on ways to graphically integrate heterogeneous and disparate medical data into models of cancer care.

Martin Vallières is devoting much of his current work to the development of a solution for the integrative modeling of oncology data. He leads the development of MEDomicsLab, an open source platform for end-to-end computation in precision oncology. This platform will integrate heterogeneous data from hospitals using deep learning and machine learning methods based on graph theory. By contributing to the improvement of prediction models in oncology, MEDomicsLab will become a key artificial intelligence tool in medicine. Martin Vallières is also a member of the Mila research institute in artificial intelligence.

Education

Ph.D., McGill University, medical physics

M.Sc., McGill University, medical physics

B.Eng., École Polytechnique de Montréal, physics engineering

Awards

Canada-CIFAR AI Chair, Mila, 2020

Postdoctoral Fellowship, NSERC, 2018

1st prize, Rising Star in Medical Physics Symposium, Medical Physics Research Training Network (MPRTN), 2015

Alexander Graham Bell Canada Graduate Scholarship – Doctoral (CGS D), NSERC, 2012

Doctoral Training Award, FRQ-S, 2012

Alexander Graham Bell Canada Graduate Scholarship – Master’s (CGS M), NSERC, 2010

Master’s Training Scholarship, FRQ-S, 2010

Master’s Research Award, FRQ-NT, 2010

Supervision

Publications

Keek SA, Beuque M, Primakov S, Woodruff HC, Chatterjee A, van Timmeren JE, Vallieres M, Hendriks LEL, Kraft J, Andratschke N, Braunstein SE, Morin O, Lambin P (2022) Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics. Frontiers in Oncology 12. https://doi.org/10.3389/fonc.2022.920393 Cite Download
George E, Flagg E, Chang K, Bai H, Aerts H, Vallieres M, Reardon D, Huang R (2022) Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma. American Journal of Neuroradiology 43:675–681. https://doi.org/10.3174/ajnr.A7488 Cite
Le Rest CC, Elhalawani H, Jreige M, Prior JO, Vallieres M, Visvikis D, Hatt M, Depeursinge A (2022) Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images. Head and Neck Tumor Segmentation and Outcome Prediction: Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 13209:1 Cite
Oreiller V, Andrearczyk V, Jreige M, Boughdad S, Elhalawani H, Castelli J, Vallieres M, Zhu S, Xie J, Peng Y, others (2022) Head and neck tumor segmentation in PET/CT: the HECKTOR challenge. Medical Image Analysis 77:102336 Cite
Fontaine P, Andrearczyk V, Oreiller V, Abler D, Castelli J, Acosta O, De Crevoisier R, Vallieres M, Jreige M, Prior JO, others (2022) Cleaning radiotherapy contours for radiomics studies, is it worth it? A head and neck cancer study. Clinical and Translational Radiation Oncology 33:153–158 Cite
DeCunha JM, Villegas F, Vallieres M, Torres J, Camilleri-Broët S, Enger SA (2021) Patient-specific microdosimetry: a proof of concept. Physics in Medicine & Biology 66:185011. Cite
Purkayastha S, Xiao Y, Jiao Z, Thepumnoeysuk R, Halsey K, Wu J, Tran TML, Hsieh B, Choi JW, Wang D, Vallieres M (2021) Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data. Korean Journal of Radiology 22:1213. https://doi.org/10.3348/kjr.2020.1104 Cite
Chatterjee A, Vallieres M, Forghani R, Seuntjens J (2021) Investigating the impact of the CT Hounsfield unit range on radiomic feature stability using dual energy CT data. Physica Medica 88:272–277 Cite
Cong H, Peng W, Tian Z, Vallieres M, Chuanpei X, Aijun Z, Benxin Z (2021) FDG-PET/CT Radiomics Models for The Early Prediction of Locoregional Recurrence in Head and Neck Cancer. Current Medical Imaging 17:374–383. https://doi.org/10.2174/1573405616666200712181135 Cite
DeCunha JM, Poole CM, Vallieres M, Torres J, Camilleri-Broët S, Rayes RF, Spicer JD, Enger SA (2021) Development of patient-specific 3D models from histopathological samples for applications in radiation therapy. Physica Medica 81:162–169 Cite
Morin O, Vallieres M, Braunstein S, Ginart JB, Upadhaya T, Woodruff HC, Zwanenburg A, Chatterjee A, Villanueva-Meyer JE, Valdes G, others (2021) An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nature Cancer 2:709–722 Cite
Bourbonne V, Fournier G, Vallieres M, Lucia F, Doucet L, Tissot V, Cuvelier G, Hue S, Le Penn Du H, Perdriel L, Bertrand N, Staroz F, Visvikis D, Pradier O, Hatt M, Schick U (2020) External validation of an MRI-derived radiomics model to predict biochemical recurrence after surgery for high-risk prostate cancer. Cancers 12:814. https://doi.org/10.3390/cancers12040814 Cite Download
Zwanenburg A, Vallieres M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit M-C, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Götz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegård A, Maier-Hein KH, Morin O, Müller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers RJHM, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C, Löck S (2020) The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 191145. https://doi.org/10.1148/radiol.2020191145 Cite Download
Chatterjee A, Vallieres M, Seuntjens J (2020) Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them. Physica Medica 70:96–100. https://doi.org/10.1016/j.ejmp.2020.01.009 Cite
Nair JKR, Vallieres M, Mascarella MA, Sabbagh NE, Duchatellier CF, Zeitouni A, Shenouda G, Chankowsky J (2020) Magnetic resonance imaging texture analysis predicts recurrence in patients with nasopharyngeal carcinoma. Canadian Association of Radiologists Journal. https://doi.org/10.1016/j.carj.2019.06.009 Cite Download
Xi IL, Zhao Y, Wang R, Chang M, Purkayastha S, Chang K, Huang RY, Silva AC, Vallieres M, Habibollahi P, Fan Y, Zou B, Gade TP, Zhang PJ, Soulen MC, Zhang Z, Bai HX, Stavropoulos SW (2020) Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clin Cancer Res. https://doi.org/10.1158/1078-0432.CCR-19-0374 Cite Download
Depeursinge A, Andrearczyk V, Whybra P, van Griethuysen J, Müller H, Schaer R, Vallieres M, Zwanenburg A (2020) Standardised convolutional filtering for radiomics. arXiv preprint arXiv:200605470 Cite Download
Traverso A, Vallieres M, Van Soest J, Wee L, Morin O, Dekker A (2020) Publishing linked and FAIR radiomics data in radiation oncology via ontologies and Semantic Web. Radiotherapy and Oncology 152:S827–S827 Cite
Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A, Morin O, Mattonen SA, El Naqa I (2020) Machine and deep learning methods for radiomics. Medical physics 47:e185–e202 Cite
Zhao Y, Chang M, Wang R, Xi IL, Chang K, Huang RY, Vallieres M, Habibollahi P, Dagli MS, Palmer M, Zhang PJ, Silva AC, Yang L, Soulen MC, Zhang Z, Bai HX, Stavropoulos SW (2020) Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma. Journal of Magnetic Resonance Imaging. https://doi.org/10.1002/jmri.27153 Cite Download
Ibrahim A, Vallieres M, Woodruff H, Primakov S, Beheshti M, Keek S, Refaee T, Sanduleanu S, Walsh S, Morin O, Lambin P, Hustinx R, Mottaghy FM (2019) Radiomics analysis for clinical decision support in nuclear medicine. Seminars in Nuclear Medicine 49:438–449. https://doi.org/10.1053/j.semnuclmed.2019.06.005 Cite Download
Morin O, Chen WC, Nassiri F, Susko M, Magill ST, Vasudevan HN, Wu A, Vallieres M, Gennatas ED, Valdes G, Pekmezci M, Alcaide-Leon P, Choudhury A, Interian Y, Mortezavi S, Turgutlu K, Bush NAO, Solberg TD, Braunstein SE, Sneed PK, Perry A, Zadeh G, McDermott MW, Villanueva-Meyer JE, Raleigh DR (2019) Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival. Neuro Oncol Adv 1. https://doi.org/10.1093/noajnl/vdz011 Cite Download
Wei L, Rosen B, Vallieres M, Chotchutipan T, Mierzwa M, Eisbruch A, El Naqa I (2019) Automatic recognition and analysis of metal streak artifacts in head and neck computed tomography for radiomics modeling. Physics and Imaging in Radiation Oncology 10:49–54. https://doi.org/10.1016/j.phro.2019.05.001 Cite Download
Zhou H, Chang K, Bai HX, Xiao B, Su C, Bi WL, Zhang PJ, Senders JT, Vallieres M, Kavouridis VK, Boaro A, Arnaout O, Yang L, Huang RY (2019) Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas. J Neurooncol 142:299–307. Cite Download
Lucia F, Visvikis D, Vallieres M, Desseroit M-C, Miranda O, Robin P, Bonaffini PA, Alfieri J, Masson I, Mervoyer A, Reinhold C, Pradier O, Hatt M, Schick U (2019) External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging 46:864–877. Cite Download
Bourbonne V, Vallieres M, Lucia F, Fournier G, Valéri A, Visvikis D, Tissot V, Pradier O, Hatt M, Schick U (2019) PO-0857 MRI-derived radiomics to select patients with high-risk prostate cancer for adjuvant radiotherapy. Radiotherapy and Oncology 133:S451–S452. https://doi.org/10.1016/S0167-8140(19)31277-0 Cite
Bourbonne V, Vallieres M, Lucia F, Doucet L, Visvikis D, Tissot V, Cuvelier G, Hue S, Prigent L, Bertrand N, Staroz F, Pradier O, Hatt M, Schick U (2019) Validation of an MRI-Derived Radiomics Model to Guide Patients Selection for Adjuvant Radiotherapy after Prostatectomy for High-Risk Prostate Cancer. International Journal of Radiation Oncology*Biology*Physics 105:E266–E267. https://doi.org/https://doi.org/10.1016/j.ijrobp.2019.06.1879 Cite
Bourbonne V, Vallieres M, Lucia F, Doucet L, Tissot V, Cuvelier G, Hue S, Du HLP, Perdriel L, Bertrand N, Starroz F, Visvikis D, Pradier O, Hatt M, Schick U (2019) Validation externe d'un modèle radiomique dérivé de l'IRM pour guider la sélection des patients en vue d'une radiothérapie adjuvante après prostatectomie dans le cadre d'un adénocarcinome prostatique à haut risque. Cancer/Radiothérapie 23:791–792. https://doi.org/https://doi.org/10.1016/j.canrad.2019.07.011 Cite Download
Lucia F, Visvikis D, Vallieres M, Desseroit M, Miranda O, Robin P, Bonaffini P, Alfieri J, Masson I, Mervoyer A, Pradier O, Hatt M (2019) EP-1476 Validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer. Radiotherapy and Oncology 133:S800 Cite
Bourbonne V, Vallieres M, Lucia F, Doucet L, Visvikis D, Tissot V, Pradier O, Hatt M, Schick U (2019) MRI-derived radiomics to guide post-operative management for high-risk prostate cancer. Front Oncol 9. https://doi.org/10.3389/fonc.2019.00807 Cite Download
Nano T, Lafrenière M, Ziemer B, Witztum A, Barrios J, Upadhaya T, Vallieres M, Interian Y, Valdes G, Morin O (2020) Artificial Intelligence in Radiation Oncology. In: The Modern Technology of Radiation Oncology. Jacob Van Dyk, p 522 Cite
Andrearczyk V, Oreiller V, Boughdad S, Rest CCL, Elhalawani H, Jreige M, Prior JO, Vallieres M, Visvikis D, Hatt M, others (2021) Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images. In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge. Springer, Cham, pp 1–37 Cite
Andrearczyk V, Oreiller V, Jreige M, Vallieres M, Castelli J, Elhalawani H, Boughdad S, Prior JO, Depeursinge A (2021) Overview of the HECKTOR challenge at MICCAI 2020: automatic head and neck tumor segmentation in PET/CT. In: 3D Head and Neck Tumor Segmentation in PET/CT Challenge. Springer, Cham, pp 1–21 Cite
Chatterjee A, Vallieres M, Seuntjens J, Forghani R (2020) Advantages of Spectral Energy CT Data for Deep Learning Applications. In: MEDICAL PHYSICS. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, pp E575–E575 Cite
Da Silva Ferreira M, LOVINFOSSE P, DE CUYPERE M, Rovira R, Lucia F, Schick U, Vallieres M, Bonaffini P, Reinhold C, Visvikis D, others (2019) Radiomics for Disease Free Survival prediction using pre-treatment FDG PET images. In: " Imaging of diagnostic and therapeutic biomarkers in Oncology “workshop Manoir de Kerdréan, Le Bono, France, September 25th-28th, 2019. Le Bono, France Cite
Chatterjee A, Vallieres M, Dohan A, Levesque I, Ueno Y, Saif S, Reinhold C, Seuntjens J (2019) Using Dataset-Specific Feature Standardization to Improve Predictive Performance of Radiomic Models. In: MEDICAL PHYSICS. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, pp E174–E174 Cite
Traverso A, Vallieres M, van Soest J, Wee L, Morin O, Dekker A (2019) Publishing Linked and FAIR-compliant Radiomics Data in Radiation Oncology via Ontologies and Semantic Web Techniques. In: SWAT4HCLS. pp 143–144 Cite
Diamant A, Chatterjee A, Vallieres M, Shenouda G, Seuntjens J (2019) Multi-Modal Deep Learning Framework for Head & Neck Cancer Outcome Prediction. In: MEDICAL PHYSICS. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, pp 5372–5372 Cite
Chatterjee A, Vallieres M, Romero-Sanchez G, Perez-Lara A, Forghani R, Seuntjens J (2019) Multi-Energy Study of Impact of CT Hounsfield Unit Range in Gray Level Discretization On Radiomic Feature Stability. In: MEDICAL PHYSICS. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, pp E233–E233 Cite
Diamant A, Chatterjee A, Vallieres M, Shenouda G, Seuntjens J (2019) Multi-Branch Convolutional Neural Network Combines Unregistered PET and CT Images for Head & Neck Cancer Outcome Prediction. In: MEDICAL PHYSICS. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, pp E294–E294 Cite
Bourbonne V, Vallieres M, Lucia F, Doucet L, Visvikis D, Tissot V, Pradier O, Hatt M, Schick U (2019) MRI-based Radiomics adjusts adjuvant Treatment after Prostatectomy in Patients with High Risk-Prostate Cancer. In: STRAHLENTHERAPIE UND ONKOLOGIE. SPRINGER HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY, pp S32–S33 Cite
Chatterjee A, Vallieres M, Dohan A, Levesque IR, Ueno Y, Saif S, Reinhold C, Seuntjens J (2019) Improved external validation performance of predictive radiomics models using statistical methods. In: Radiotherapy and Oncology. ELSEVIER IRELAND LTD ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO …, pp S513–S513 Cite
Da-ano R, Lucia F, Vallieres M, Bonaffini P, Masson I, Mervoyer A, Reinhold C, Schick U, Visvikis D, Hatt M (2019) Harmonization strategies based on ComBat for mutlicentric radiomics studies. In: EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING. SPRINGER 233 SPRING ST, NEW YORK, NY 10013 USA, pp S254–S254 Cite
Da Silva Ferreira M, LOVINFOSSE P, DE CUYPERE M, Rovira RR, Lucia F, Schick U, Vallieres M, Bonaffini P, Reinhold C, Visvikis D, others (2019) FDG PET radiomics to predict disease-free survival in Cervical Cancer. In: 2019 IEEE Nuclear Science Symposium & Medical Imaging Conference Cite
Vallieres M (2019) MEDomics: synergie entre analyse d’images médicales, apprentissage automatique, apprentissage profond, traitement automatique des langues naturelles et apprentissage distribué Cite
Vallieres M (2019) Radiomics: the Image Biomarker Standardisation Initiative (IBSI) Cite
Vallieres M (2019) Radiomics: the Image Biomarker Standardisation Initiative (IBSI) Cite
Vallieres M (2019) MEDomicsLab: an open-source computation platform for integrative data modeling in medicine Cite
Vallieres M (2019) Radiomics: the Image Biomarker Standardisation Initiative (IBSI) Cite
Zwanenburg A, Leger S, Vallieres M, Löck S (2020) Image biomarker standardisation initiative Cite Download