Close mobile menu

Ali Sadeghi-Naini
Associate Professor
Electrical Engineering and Computer Science Department

Member, IC@L
Scientist (Cross-Appointed), Radiation Oncology / Physical Sciences, Sunnybrook Health Sciences Centre
Assistant Professor (Cross-Appointed), Medical Biophysics, University of Toronto
Adjunct Professor, Electrical and Computer Engineering / Biomedical Engineering, Western University

Website | Email

2021 – 2022 Research Highlights

Quantitative Imaging and Smart Biomarkers for Personalized Cancer Therapeutics

The focus of our research program is to develop quantitative imaging and smart biomarker technologies to make personalized medicine an option for cancer patients. Specifically, we develop integrated imaging and computational frameworks to derive smart biomarkers for cancer diagnosis and characterization and prediction of tumour response to anti-cancer therapies. We adapt different imaging modalities including, ultrasound, optical and photoacoustic imaging, elastography and MRI, in addition to digital histopathology images to quantify tumour micro-environment and to model changes in various characteristics of tumour in response to treatment. We develop special machine learning techniques that utilize our imaging biomarkers to diagnose and characterize cancer and to predict cancer response to therapy before or early after the treatment initiation.

External Collaborators:

  • Dr. Greg Stanisz, Sunnybrook Research Institute
  • Dr. Gregory Czarnota, Sunnybrook Health Sciences Centre
  • Dr. Hany Soliman, Sunnybrook Health Sciences Centre
  • Dr. Arjun Sahgal, Sunnybrook Health Sciences Centre
  • Dr. William Tran, Sunnybrook Health Sciences Centre
  • Dr. Alex Shenfield, Sheffield Hallam University (UK)

Research Highlights

  1. K. Saednia, A. Jalalifar, S. Ebrahimi, A. Sadeghi-Naini. An attention-guided deep neural network for annotation of abnormalities in chest x-ray images: visualization of network decision basis. 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). July 2020; Montreal, QC, Canada. pp. 1258-1261.
  2. W. T. Tran, A. Sadeghi-Naini, F. I. Lu, S. Gandhi, N. Meti, M. Brackstone, E. Rakovitch, B. Curpen. Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence. The Canadian Association of Radiologists Journal. 2021; 72 (1): 98-108.
  3. H. Moghadas-Dastjerdi, H. R. Sha-E-Tallat, L. Sannachi, A. Sadeghi-Naini, G. J. Czarnota. A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning. Scientific Reports. 2020; 10:10936.
  4. P. Jafari, S. Dempsey, D. A. Hoover, E. Karami, S. Gaede, A. Sadeghi-Naini, T. Y. Lee, A. Samani. In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy. Computers in Biology and Medicine. 2021; 130: 104231.
  5. W. W. Lam, W. Oakden, E. Karami, M. M. Koletar, L. Murray, S. K. Liu, A. Sadeghi-Naini, G. J. Stanisz. An automated segmentation pipeline for intratumoural regions in animal xenografts using machine learning and saturation transfer MRI. Scientific Reports. 2020; 10: 8063.
  6. K. Saednia, S. Tabbarah, A. Lagree, T. Wu, J. Klein, E. Garcia, M. Hall, E. Chow, E. Rakovitch, C. Childs, A. Sadeghi-Naini, W. T. Tran. Quantitative thermal imaging biomarkers to detect acute skin toxicity from breast radiotherapy using supervised machine learning. International Journal of Radiation Oncology, Biology, Physics. 2020; 106(5): 1071-1083.
  7. Deep learning of ultrasound RF data for tissue characterization. Invention Disclosure, Protected. Filing Date: 2020 March. Disclosure#: IY2020-005, ON, Canada. Inventors: S. Ebrahimi, A. Sadeghi-Naini.