The focus of Dr. Sadeghi-Naini’s research is on developing AI-assisted image-guided technologies to improve personalized cancer therapeutics. In particular, he is interested in developing integrated imaging and computational frameworks to detect and characterize cancer, to facilitate cancer-targeting interventions, and to predict/evaluate cancer response to treatment.

  • M.Sc., 2006, Artificial Intelligence, Tehran Polytechnic University, Iran
  • PhD, 2011, Biomedical Engineering, Western University, Canada
  • Postdoctoral Fellowship, 2015, Medical Biophysics and Radiation Oncology, University of Toronto, Canada
  • Cross-appointed Scientist, Physical Sciences, Odette Cancer Research Program, Sunnybrook Research Institute
  • Cross-appointed Scientist, Department of Radiation Oncology, Sunnybrook Health Sciences Centre
  • Associate Professor, Department of Electrical Engineering and Computer Science, York University

Research Foci

  • Image-Guided Personalized Cancer Therapeutics
  • Modelling Intra-Tumour Heterogeneity
  • Quantitative Imaging and Biomarkers AI in Precision Medicine
  • Smart Digital Pathology

Publications


Affiliated Labs & Programs

Selected Publications

  1. Jalalifar SA, Soliman H, Sahgal A, Sadeghi-Naini A. Automatic Assessment of Stereotactic Radiation Therapy Outcome in Brain Metastasis Using Longitudinal Segmentation on Serial MRI. IEEE J Biomed Health Inform. 2023;27(6):2681-2692.

  2. Saednia K, Tran WT, Sadeghi-Naini A. A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies. Med Phys. 2023;50(12):7852-7864.

  3. Kheirkhah N, Kornecki A, Czarnota GJ, Samani A, Sadeghi-Naini A. Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Phys Med. 2023;112:102619.

  4. Jalalifar SA, Soliman H, Sahgal A, Sadeghi-Naini A. Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features. Med Phys. 2022;49(11):7167-7178.

  5. Taleghamar H, Moghadas-Dastjerdi H, Czarnota GJ, Sadeghi-Naini A. Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment. Sci Rep. 2021;11(1):14865.