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CBB Member Karim Karim in lab, Electrical and Computer Engineering

CBB Member Karim Karim, Electrical and Computer Engineering, in lab

Lab of CBB Member Frank Gu, Chemical Engineering

Lab of CBB Member Frank Gu, Chemical Engineering

CBB Researcher Alex Wong, Canada Research Chair in Medical Imaging, Systems Design Engineering

CBB Researcher Alex Wong, Canada Research Chair in Medical Imaging, Systems Design Engineering

CBB Member Karim Karim in lab, Electrical and Computer Engineering
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CBB Member Karim Karim, Electrical and Computer Engineering, in lab
Lab of CBB Member Frank Gu, Chemical Engineering
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Lab of CBB Member Frank Gu, Chemical Engineering
CBB Researcher Alex Wong, Canada Research Chair in Medical Imaging, Systems Design Engineering
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CBB Researcher Alex Wong, Canada Research Chair in Medical Imaging, Systems Design Engineering

Learn how CBB researchers are tackling Cancer:

CBB Researchers

Stories

Press/Media
  • "Mathematicians set sights on cancer therapy," , Mohammad Kohandel and Sivabal Sivaloganathan, January 19, 2018
  • "Image-based models of solid tumors behaviour in diagnosis, treatment, prediction," , , December 1, 2016
  • "A tissue mechanist found in translation," , Thomas Willett, November 19, 2015
  • "Nanomedicines - The Way of the future?" , Emmanuel Ho, January 21, 2015
  • "BIG Data, medical imaging, and machine intelligence," , Hamid Tizhoosh, January 21, 2015
  • "Medical imaging: pixels, consensus, and learning," , Hamid Tizhoosh, October 1, 2014
  • "Integrative systems for biomedical imaging and analysis," , Alex Wong, October 1, 2014
  • "An engineering prospective on cancer," , Adil Al-Mayah, September 30, 2014
  • "Model-based design in synthetic biology," , Brian Ingalls, September 30, 2014
  • "Bending the cost curve: Building a $1000 diagnostic x-ray imager for scalable and sustainable healthcare," , Karim Karim, September 30, 2014

Publications by CBB Researchers
  • Moradian S, Krzyzanowska MK, Maguire R, Morita PP, Kukreti V, Avery J, Liu G, Cafazzo J, Howell D. . JMIR Cancer 2018;4(2):e10932

  • Al-Mayah, A., Moseley, J., Hunter, S., & Brock, K. (2015). Radiation dose response simulation for biomechanical-based deformable image registration of head and neck cancer treatment.

  • Mohamed, A. S., Wong, A. J., Fuller, C. D., Kamal, M., Gunn, G. B., Phan, J., ... & Quinlan-Davidson, S. R. (2017). Patterns of locoregional failure following post-operative intensity-modulated radiotherapy to oral cavity cancer: quantitative spatial and dosimetric analysis using a deformable image registration workflow.

  • Shafiee, M. J., Chung, A. G., Khalvati, F., Haider, M. A., & Wong, A. (2017). Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.
  • Shafiee, M. J., & Wong, A. (2017). Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection.
  • Cho, D. S., Khalvati, F., Clausi, D. A., & Wong, A. (2017, July). A Machine Learning-Driven Approach to Computational Physiological Modeling of Skin Cancer.
  • Karimi, A. H., Chung, A. G., Shafiee, M. J., Khalvati, F., Haider, M. A., Ghodsi, A., & Wong, A. (2017, July). Discovery radiomics via a mixture of deep convnet sequencers for multi-parametric MRI prostate cancer classification.
  • Kumar, D., Chung, A. G., Shaifee, M. J., Khalvati, F., Haider, M. A., & Wong, A. (2017, July). Discovery radiomics for pathologically-proven computed tomography lung cancer prediction.
  • Khalvati, F., Haider, M. A., Ghodsi, A., & Wong, A. (2017, June). Discovery Radiomics via a Mixture of Deep ConvNet Sequencers for Multi-parametric MRI Prostate Cancer Classification.
  • Cho, D. S., Khalvati, F., Clausi, D. A., & Wong, A. (2017, July). A Machine Learning-Driven Approach to Computational Physiological Modeling of Skin Cancer.
  • Zhang, Y., Oikonomou, A., Wong, A., Haider, M. A., & Khalvati, F. (2017). Radiomics-based prognosis analysis for non-small cell lung cancer.
  • Khalvati, F., Zhang, J., Wong, A., & Haider, M. A. (2016, December). Bag of Bags: Nested Multi Instance Classification for Prostate Cancer Detection.
  • Mooney, S. M., Talebian, V., Jolly, M. K., Jia, D., Gromala, M., Levine, H., & McConkey, B. J. (2017). The GRHL2/ZEB feedback ǴDZ—a key axis in the regulation of EMT in breast cancer.
  • Seo, B. B., Jahed, Z., Coggan, J. A., Chau, Y. Y., Rogowski, J. L., Gu, F. X., ... & Tsui, T. Y. (2017). Mechanical Contact Characteristics of PC3 Human Prostate Cancer Cells on Complex-Shaped Silicon Micropillars.
  • Yang, S., Chen, D., Li, N., Xu, Q., Li, H., Gu, F., ... & Lu, J. (2016). Hollow mesoporous silica nanocarriers with multifunctional capping agents for in vivo cancer imaging and therapy.
  • Gangeh, M. J., Tizhoosh, H. R., Wu, K., Huang, D., Tadayyon, H., & Czarnota, G. J. (2017, February). Tumour ellipsification in ultrasound images for treatment prediction in breast cancer. .
  • Lu, M., Lu, Q. B., & Honek, J. F. (2017). Squarate-based carbocyclic nucleosides: Syntheses, computational analyses and anticancer/antiviral evaluation.
  • Malik, P., Phipps, C., Edginton, A., & Blay, J. (2017). Pharmacokinetic Considerations for Antibody-Drug Conjugates against Cancer.
  • Tulsieram, K. L., Arocha, J. F., & Lee, J. (2018). Readability and coherence of Department/Ministry of health HPV information.
  • Bizheva, K., Tan, B., Fisher, C. J., Mason, E., & Lilge, L. D. (2017, April). In-vivo imaging of the morphology and blood perfusion of brain tumours in rats with UHR-OCT.
  • Asgari, H., Soltani, M., & Sefidgar, M. (2018). Modeling of FMISO [F 18] nanoparticle PET tracer in normal-cancerous tissue based on real clinical image.
  • Forouzannia, F., Enderling, H., & Kohandel, M. (2018). Mathematical Modeling of the Effects of Tumor Heterogeneity on the Efficiency of Radiation Treatment Schedule.
  • Mahdipour-Shirayeh, A., Kaveh, K., Kohandel, M., & Sivaloganathan, S. (2017). Phenotypic heterogeneity in modeling cancer evolution.
  • Goldman, A., Kohandel, M., & Clairambault, J. (2017). Integrating Biological and Mathematical Models to Explain and Overcome Drug Resistance in Cancer, Part 2: From Theoretical Biology to Mathematical Models.
  • Goldman, A., Kohandel, M., & Clairambault, J. (2017). ntegrating Biological and Mathematical Models to Explain and Overcome Drug Resistance in Cancer,Part 1: Biological Facts and Studies in Drug Resistance.
  • Manem, V. S., Kohandel, M., Hodgson, D. C., & Sivaloganathan, S. (2017). Predictive modeling of therapy induced secondary thyroid malignancies in childhood cancer survivors.
  • Tonekaboni, S. A. M., Dhawan, A., & Kohandel, M. (2017). Mathematical modelling of plasticity and phenotype switching in cancer cell populations.
  • Manem, V. S., Kohandel, M., Hodgson, D. C., & Sivaloganathan, S. (2017). Predictive modeling of therapy induced secondary thyroid malignancies in childhood cancer survivors.
  • Mahdipour-Shirayeh, A., Kaveh, K., Kohandel, M., & Sivaloganathan, S. (2017). Phenotypic heterogeneity in modeling cancer evolution.
  • Forouzannia, F., & Sivaloganathan, S. (2017). Cancer Stem Cells, the Tipping Point: Minority Rules?.
  • Gelband, H., Horton, S., Watkins, D., Jamison, D. T., Wu, D., Gospodarowicz, M., & Jha, P. (2018). Disease Control Priorities: cancer package principles and overview.
  • ܱԳٱ‐A, S., Bhakta, N., Vasquez, R. F., Gupta, S., & Horton, S. E. (2018). The cost and Dz‐eڴڱ𳦳پԱ of childhood cancer treatment in El Salvador, Central America: A report from the Childhood Cancer 2030 Network.
  • Fuentes-Alabi, S., Vasquez, R. F., Bhakta, N., Rodriguez-Galindo, C., Frazier, A. L., Atun, R., ... & Horton, S. (2017). Cost and Cost-Effectiveness of Childhood Cancer Treatment in El Salvador: A Collaborative Budget Model.
  • Burke, M. V., Atkins, A., Akens, M., Willett, T. L., & Whyne, C. M. (2016). Osteolytic and mixed cancer metastasis modulates collagen and mineral parameters within rat vertebral bone matrix.

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