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.
Education
- 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
Appointments & Affiliations
- 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
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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.
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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.
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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.
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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.
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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.