缅北强奸

狈补尘别:听Martin Valli猫res,PhD

Level at MPU:听Postdoctoral Fellow

Email:听martin.carrier-vallieres [at] mail.mcgill.ca

Website:听

Supervisor(s):听Dr. Jan Seuntjens

Research interests:听PET/MR imaging, Texture analysis, Tumour outcome prediction, Radiomics

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Research summary

The combination of Positron Emission Tomography (PET) and Magnetic Resonance (MR) imaging is nowadays playing an increasing role in monitoring tumour characteristics that are essential to the efficiency of treatments. In fact, specific imaging features of tumours (e.g. textures) can be extracted to quantify intratumoural heterogeneity and evaluate how patients will respond to treatments. This information is thereafter used to adapt treatments based on patients' predicted failure risk. Our research aims at developing PET/MR image analysis and simulation techniques for optimal prediction of treatment outcomes in soft-tissue sarcomas (e.g. lung metastases development), an aggressive type of cancer that develops in connective tissues of the body. For this purpose, novel image analysis techniques based on the texture features of images are developed to study the information embedded in joint PET/MR images of tumours. PET/MR imaging simulations are then used to investigate the physical factors affecting these features in the acquisition process of PET/MR images. The simulation pipeline allowd to optimize PET/MR imaging acquisition parameters in order to produce images with the optimal texture features needed to provide optimal prediction of tumour outcomes (i.e. optimal assessment of tumour aggressiveness). These tools should impact our understanding of tumour progression and treatment response in soft-tissue sarcoma cancer, potentially improving patient survival. The proposed methodology could afterwards be extended to other types of cancer and help more patients to overcome this deadly disease.

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Key publications

1. Valli猫res, M., Laberge, S., Diamant, A., & El Naqa, I. (2017). Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept.听Physics in Medicine and Biology.
2. Zhou, H., Valli猫res, M., Bai, H.X. et al. (2016). MR imaging features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro-Oncology (IN PRESS)
3. Valli猫res, M., Freeman, C.R., Skamene, S.R. et al. (2015).听 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(14), 5471-5496.听doi:10.1088/0031-9155/60/14/5471
4. Hatt, M., Majdoub, M., Valli猫res, M. et al. (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. Nuc. Med., 56(1), 38-44. doi:10.2967/jnumed.114.144055

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Awards

(2015)听缅北强奸 - Faculty of Medicine

(2012-2015) NSERC Graduate Scholarship Doctoral Award (CGS D)

(2013) AAPM 2013 Science Council Session Award

(2011) FRSQ - Bourse de formation de ma卯trise

(2010) NSERC - Graduate Scholarship Master Award (CGS M)