Publications

Voici les publications que les membres du GRIIS ont produites dans le cadre de leurs travaux au sein du groupe de recherche.

Zwanenburg A, Leger S, Vallières M, Löck S (2020) Image biomarker standardisation initiative 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
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
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
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
Zhao L, Lim Choi Keung SN, Golby C, Ethier J-F, Curcin V, Bastiaens H, Burgun A, Delaney BC, Arvanitis TN (2015) EU FP7 TRANSFoRm Project: query workbench for participant identification and data extraction. In: AMIA CRI 2015. San Francisco, États-Unis 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
Witham MD, Frost H, McMurdo M, Donnan PT, McGilchrist MM (2015) Construction of a linked health and social care database resource—lessons on process, content and culture. Inform Health Soc Care 40:229–239 . doi: 10.3109/17538157.2014.892491 Cite
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
Verheij RA, Curcin V, Delaney BC, McGilchrist MM (2018) Possible sources of bias in primary care electronic health record data use and reuse. J Med Internet Res 20:e185 . doi: 10.2196/jmir.9134 Cite
Vanasse A, Dault R, Cloutier A-M, Benigeri M, Ethier J-F (2019) Validated case selection algorithms to identify 7 ambulatory case sensitive conditions Cite Download
Vanasse A, Boivin A, Dumez V, Ethier J-F, Leblanc A, Légaré F, Pluye P, Belec G, Pelletier J-G (2019) A learning health system oriented toward care trajectories Cite Download
Vanasse A, Courteau M, Ethier J-F (2018) The 6W multidimensional model of care trajectories for patients with chronic ambulatory care sensitive conditions and hospital readmissions. Public Health 157:53–61 . doi: 10.1016/j.puhe.2018.01.007 Cite Download
Vanasse A, Ethier J-F, Dault R, Benigeri M, Cloutier A-M (2019) Validated case selection algorithms to identify 7 ambulatory care sensitive conditions Cite
Vanasse A, Benigeri M, Courteau J, Dorais M, Ethier J-F (2018) Linking canadian community health surveys to administrative data in Quebec: A unique opportunity for health care trajectories research on ambulatory care sensitive conditions Cite
Vanasse A, Cohen A, Courteau J, Bergeron P, Dault R, Gosselin P, Blais C, Bélanger D, Rochette L, Chebana F (2016) Association between floods and acute cardiovascular diseases: A population-based cohort study using a geographic information system approach. Int J Environ Res Public Health 13:168 . doi: 10.3390/ijerph13020168 Cite Download
Vanasse A, Talbot D, Chebana F, Bélanger D, Blais C, Gamache P, Giroux J-X, Dault R, Gosselin P (2017) Effects of climate and fine particulate matter on hospitalizations and deaths for heart failure in elderly: A population-based cohort study. Environ Int 106:257–266 . doi: 10.1016/j.envint.2017.06.001 Cite
Van Praagh S, Ménard J-F, Montreuil M, Noronha C, Talwar V, Carnevale FA (2018) Learning from JJ: An interdisciplinary conversation about child welfare, health care, and law. McGill JL & Health 12:123 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, 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, 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) 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, 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, 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, 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, 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, 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, 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, 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
Vallières M, Freeman CR, Skamene SR, Naqa IE (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60:5471–5496 . doi: 10.1088/0031-9155/60/14/5471 Cite Download
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 (2012) PET/MR imaging for prediction of tumor outcomes by wavelet image fusion and texture analysis 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 (2016) Analyse texturale pour l’évaluation de l’agressivité des tumeurs Cite
Vallières M (2012) Prediction of tumour outcomes by wavelet image fusion and texture analysis 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 (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 (2019) MEDomicsLab: an open-source computation platform for integrative data modeling in medicine 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