At the radiobiology laboratory, the general research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. The primary motivation is to design and develop novel approaches to unravel the response of cancer patients to chemo-radiotherapy treatment by integrating physical, biological, and imaging information into advanced mathematical models. These models could be used to personalize the chemo-radiotherapy treatment of cancer patients based on predicted benefit/risk. We also investigate the use of bone marrow derived stem cells in the reduction of radiation pneumonitis, one of the most common side effects observed in lung cancer patients after irradiation. For this, we developed an animal (rat) model of radiation pneumonitis, and tested different administration routes of Mesenchymal stem cells derived from bone marrow. We are also interested in investigation alternative therapies that potentially will reduce radiotherapy side effects.

The radiobiology research group, supervised by Issam El Naqa (computer screen). From left to right : Jessica Perez, Seema Ambereen, Daniel Cooper, Simon Vallières, Sangkyu Lee, André Diamant Boustead, Norma Ybarra, Asha Jeyaseelan.

Key Publications

  1. Maria, O. M., Maria, A. M., Ybarra, N., Jeyaseelan, K., Lee, S., Perez, J., ... & El Naqa, I. (2015). Mesenchymal Stem Cells Adopt Lung Cell Phenotype in Normal and Radiation-induced Lung Injury Conditions. Applied immunohistochemistry & molecular morphology: AIMM/official publication of the Society for Applied Immunohistochemistry.
  2. Vallieres, M., Boustead, A., Laberge, S., Levesque, I. R., & El Naqa, I. (2015). SU-EJ-250: A Machine Learning Approach for Creating Texture-Preserved MRI Tumor Models From Clinical Sequences. Medical physics, 42(6), 3323-3324.
  3. Vallières, M., Freeman, C. R., Skamene, S. R., & El Naqa, I. (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. Physics in medicine and biology, 60(14), 5471.
  4. Lee, S., Ybarra, N., Jeyaseelan, K., Seuntjens, J., El Naqa, I., Faria, S., ... & Robinson, C. (2015). Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk. Medical physics, 42(5), 2421-2430.
  5. Coates, J., Jeyaseelan, A. K., Ybarra, N., David, M., Faria, S., Souhami, L., ... & El Naqa, I. (2015). Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer. Radiotherapy and Oncology.
  6. Hatt, M., Majdoub, M., Vallieres, M., Tixier, F., Le Rest, C. C., Groheux, D., ... & 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. Journal of Nuclear Medicine, 56(1), 38-44.
  7. Lee, S., Stroian, G., Kopek, N., AlBahhar, M., Seuntjens, J., & El Naqa, I. (2012). Analytical modelling of regional radiotherapy dose response of lung. Physics in medicine and biology, 57(11), 3309.


Back to top