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

Publications

Bourbonne V, Fournier G, Vallières 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 . doi: 10.3390/cancers12040814 Cite Download
Zwanenburg A, Vallières 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 . doi: 10.1148/radiol.2020191145 Cite Download
Chatterjee A, Vallières 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 . doi: 10.1016/j.ejmp.2020.01.009 Cite
Nair JKR, Vallières 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. doi: 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, Vallières 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. doi: 10.1158/1078-0432.CCR-19-0374 Cite Download
Zhao Y, Chang M, Wang R, Xi IL, Chang K, Huang RY, Vallières 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. doi: 10.1002/jmri.27153 Cite Download
Ibrahim A, Vallières 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 . doi: 10.1053/j.semnuclmed.2019.06.005 Cite Download
Morin O, Chen WC, Nassiri F, Susko M, Magill ST, Vasudevan HN, Wu A, Vallières 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: . doi: 10.1093/noajnl/vdz011 Cite Download
Wei L, Rosen B, Vallières 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 . doi: 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, Vallières 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 . doi: 10.1007/s11060-019-03096-0 Cite Download
Lucia F, Visvikis D, Vallières 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 . doi: 10.1007/s00259-018-4231-9 Cite Download
Chatterjee A, Vallières M, Dohan A, Levesque IR, Ueno Y, Saif S, Reinhold C, Seuntjens J (2019) Creating robust predictive radiomic models for data from independent institutions using normalization. IEEE Transactions on Radiation and Plasma Medical Sciences 3:210–215 . doi: 10.1109/TRPMS.2019.2893860 Cite Download
Chatterjee A, Vallières M, Dohan A, Levesque IR, Ueno Y, Bist V, Saif S, Reinhold C, Seuntjens J (2019) An empirical approach for avoiding false discoveries when applying high-dimensional radiomics to small datasets. IEEE Transactions on Radiation and Plasma Medical Sciences 3:201–209 . doi: 10.1109/TRPMS.2018.2880617 Cite Download
Upadhaya T, Vallières M, Chatterjee A, Lucia F, Bonaffini PA, Masson I, Mervoyer A, Reinhold C, Schick U, Seuntjens J, Rest CCL, Visvikis D, Hatt M (2019) Comparison of radiomics models built through machine learning in a multicentric context with Independent testing: identical data, similar algorithms, different methodologies. IEEE Transactions on Radiation and Plasma Medical Sciences 3:192–200 . doi: 10.1109/TRPMS.2018.2878934 Cite Download
Diamant A, Chatterjee A, Vallières M, Shenouda G, Seuntjens J (2019) Deep learning in head & neck cancer outcome prediction. Scientific Reports 9:1–10 . doi: 10.1038/s41598-019-39206-1 Cite Download
Bourbonne V, Vallières 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: . doi: 10.3389/fonc.2019.00807 Cite Download
Morin O, Vallières M, Jochems A, Woodruff HC, Valdes G, Braunstein SE, Wildberger JE, Villanueva-Meyer JE, Kearney V, Yom SS, Solberg TD, Lambin P (2018) A deep look into the future of quantitative imaging in oncology: a statement of working principles and proposal for change. International Journal of Radiation Oncology*Biology*Physics 102:1074–1082 . doi: 10.1016/j.ijrobp.2018.08.032 Cite Download
Vallières M, Serban M, Benzyane I, Ahmed Z, Xing S, El Naqa I, Levesque IR, Seuntjens J, Freeman CR (2018) Investigating the role of functional imaging in the management of soft-tissue sarcomas of the extremities. Physics and Imaging in Radiation Oncology 6:53–60 . doi: 10.1016/j.phro.2018.05.003 Cite Download
Vallières M, Zwanenburg A, Badic B, Rest CCL, Visvikis D, Hatt M (2018) Responsible radiomics research for faster clinical translation. J Nucl Med 59:189–193 . doi: 10.2967/jnumed.117.200501 Cite Download
Vallières M, Laberge S, Diamant A, Naqa IE (2017) Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept. Phys Med Biol 62:8536–8565 . doi: 10.1088/1361-6560/aa8a49 Cite Download
Vallières M, Kay-Rivest E, Perrin LJ, Liem X, Furstoss C, Aerts HJWL, Khaouam N, Nguyen-Tan PF, Wang C-S, Sultanem K, Seuntjens J, El Naqa I (2017) Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Scientific Reports 7:1–14 . doi: 10.1038/s41598-017-10371-5 Cite Download
Zhou H, Vallières M, Bai HX, Su C, Tang H, Oldridge D, Zhang Z, Xiao B, Liao W, Tao Y, Zhou J, Zhang P, Yang L (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 19:862–870 . doi: 10.1093/neuonc/now256 Cite Download
Hatt M, Majdoub M, Vallières M, Tixier F, Rest CCL, Groheux D, Hindié E, Martineau A, Pradier O, Hustinx R, Perdrisot R, Guillevin R, Naqa IE, Visvikis D (2015) 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi–cancer site patient cohort. J Nucl Med 56:38–44 . doi: 10.2967/jnumed.114.144055 Cite Download
Rivard M, Laliberté M, Bertrand-Grenier A, Harnagea C, Pfeffer CP, Vallières M, St-Pierre Y, Pignolet A, Khakani MAE, Légaré F (2011) The structural origin of second harmonic generation in fascia. Biomed Opt Express, BOE 2:26–36 . doi: 10.1364/BOE.2.000026 Cite Download
Harnagea C, Vallières M, Pfeffer CP, Wu D, Olsen BR, Pignolet A, Légaré F, Gruverman A (2010) Two-dimensional nanoscale structural and functional imaging in individual collagen type I fibrils. Biophysical Journal 98:3070–3077 . doi: 10.1016/j.bpj.2010.02.047 Cite Download
Vallières M, Chatterjee A, Lucia F, Bourbonne V, Bonaffini P, Masson I, Mervoyers A, Reinhold C, Visvikis D, Schick U, Seuntjens J, Morin O, Hatt M (2018) Investigating the complementarity of radiomics and clinical information for predicting treatment failure in multiple cancer types. In: Medical Physics Cite
Vallières M, Visvikis D, Hatt M (2018) Dependency of a validated radiomics signature and potential corrections. In: Journal of Nuclear Medicine Cite
Vallières M, Freeman CR, Ahmed Z, Turcotte R, Hickeson M, Skamene S, Jeyaseelan K, Hathout L, Serban M, Xing S, Powell TI, Seuntjens J, Levesque IR, El Naqa I (2015) Early assessment of tumor aggressiveness using joint FDG-PET/MRI textural features: prediction of prospective cohort and potential improvements using hypoxia and perfusion biomarkers. In: International Journal of Radiation Oncology Biology Physics Cite
Vallières M, Boustead A, Laberge S, Levesque IR, El Naqa I (2015) A machine learning approach for creating texture-preserved MRI tumor models from clinical sequences. In: Medical Physics. pp 3323–3324 Cite
Vallières M, Laberge S, Levesque IR, El Naqa I (2014) Enhancement of texture-based metastasis prediction models via the optimization of PET/MRI acquisition protocols. In: Medical Physics. pp 434–435 Cite
Vallières M, Freeman CR, Skamene S, El Naqa I (2014) Early assessment of tumor aggressiveness using joint FDG-PET/MR textural features. In: International Journal of Radiation Oncology Biology Physics. pp 6– 7 Cite
Vallières M, Kumar A, Sultanem K, El Naqa I (2013) FDG-PET Image-derived features can determine HPV status in head and neck cancer. In: International Journal of Radiation Oncology Biology Physics Cite
Vallières M, Kumar A, Sultanem K, El Naqa I (2013) FDG-PET imaging features can predict treatment outcomes in head and neck cancer. In: Medical Physics Cite
Vallières M, Naqa FC, Skamene SR, El Naqa I (2013) Joint FDG-PET/MR imaging for the early prediction of tumor outcomes. In: Medical Physics Cite
Vallières M, Freeman CR, Skamene SR, El Naqa I (2012) Prediction of tumor outcomes through wavelet image fusion and texture analysis of PET/MR imaging. In: Medical Physics Cite
Vallières M, Freeman CR, Skamene SR, El Naqa I (2012) FDG-PET features and outcomes in soft-tissue sarcomas of the extremities. In: International Journal of Radiation Oncology Biology Physics. pp 167– 168 Cite
Vallières M (2019) Radiomics: the Image Biomarker Standardisation Initiative (IBSI) Cite
Vallières M (2019) Radiomics: the Image Biomarker Standardisation Initiative (IBSI) Cite
Vallières M (2019) Radiomics: the Image Biomarker Standardisation Initiative (IBSI) Cite
Vallières M (2019) MEDomicsLab: an open-source computation platform for integrative data modeling in medicine Cite
Vallières M (2019) MEDomics: synergie entre analyse d’images médicales, apprentissage automatique, apprentissage profond, traitement automatique des langues naturelles et apprentissage distribué Cite
Vallières M (2018) Investigating the complementarity of radiomics and clinical information for predicting treatment failure in multiple cancer types Cite
Vallières M (2018) Radiomics in MRI: Getting started Cite
Vallières M (2018) Introduction to convolutional neural networks (CNNs) Cite
Vallières M (2018) Radiomics: the Image Biomarker Standardisation Initiative (IBSI) Cite
Vallières M (2017) Radiomics: Enabling Factors Towards Precision Medicine Cite
Vallières M (2017) IBSI: Current status and beyond Cite
Vallières M (2017) Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept Cite
Vallières M (2016) Analyse texturale pour l’évaluation de l’agressivité des tumeurs Cite
Vallières M (2016) Assessing the risk of tumour recurrences and metastases in head and neck cancer by combining radiomics and clinical variables via imbalance-adjusted machine learning Cite
Vallières M (2015) Statistical methods for the construction of texture-based prediction models Cite
Vallières M (2015) Radiomics: Mais Ou Et Donc Car Ni Or (who, what, when, where, when)? Cite
Vallières M (2012) PET/MR imaging for prediction of tumor outcomes by wavelet image fusion and texture analysis Cite
Vallières M (2012) Prediction of tumour outcomes by wavelet image fusion and texture analysis Cite
Zwanenburg A, Leger S, Vallières M, Löck S (2020) Image biomarker standardisation initiative Cite Download