Scientist profiles S-Z

Administrative Assistant: Lorelie Lacson
Phone: 416-480-4619
Email: lorelie.lacson@sunnybrook.ca

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 and Affiliations:

• Scientist, Physical Sciences, Odette Cancer Research Program, Sunnybrook Research Institute
• Scientist, department of radiation oncology, Sunnybrook Health Sciences Centre
• Assistant professor, department of medical biophysics, U of T

Research Foci:

• Image-guided personalized cancer therapeutics
• Quantitative multimodal cancer imaging
• Modelling intra-tumour heterogeneity
• Computer-aided minimally-invasive cancer interventions

Research Summary:

The focus of Dr. Sadeghi-Naini's research is on developing computer-aided 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 evaluate response to treatment.

In this context, he is investigating novel methods of multimodal cancer imaging to explore different stages during cancer development and decay from various structural and functional perspectives.

Specifically, he is developing integrated frameworks to adapt complementary aspects of quantitative ultrasound imaging, optical spectroscopy, elastography, computed tomography and MRI to characterize a tumour in terms of its micro-structure, physiology, perfusion, metabolism and biomechanical properties. Further, he is investigating alterations in such tumour characteristics to develop sensitive biomarkers of cancer response to treatment.

Dr. Sadeghi-Naini is also transforming multimodal imaging within computational frameworks to facilitate the planning and navigation of interventional procedures such as biopsy, brachytherapy and radiation therapy.

A particular interest is in developing ad hoc models for quantification of spatial heterogeneity in cancer imaging. Alterations within a tumour during its formation or degeneration are frequently inhomogeneous. Therefore, quantifying intra-tumour heterogeneity can provide further insights into tumour characteristics or rapidly flag a change in tumour state within its life cycle. To quantify intra-tumour heterogeneity noninvasively, Dr. Sadeghi-Naini is developing novel image-processing techniques to model and analyze the texture within tumour images. He is adapting machine learning techniques to determine how to correspond these textural features to specific tumour characteristics, or to a change that indicates tumour response to treatment.

Selected Publications:

See current publications list at PubMed.

1. Sadeghi-Naini A, Vorauer E, Chin L, Falou O, Tran WT, Wright FC, Gandhi S, Yaffe MJ, Czarnota GJ. Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images. Med Phys. 2015;42(11):6130–6146.
2. Sadeghi-Naini A, Sofroni E, Papanicolau N, Falou O, Sugar L, Morton G, Yaffe M, Nam R, Sadeghian A, Kolios MC, Chung HT, Czarnota GJ. Quantitative ultrasound spectroscopic imaging for characterization of disease extent in prostate cancer patients. Transl Oncol. 2015;8(1):25–34.
3. Sadeghi-Naini A, Papanicolau N, Falou O, Zubovits J, Dent R, Verma S, Trudeau ME, Boileau JF, Spayne J, Iradji S, Sofroni E, Lee J, Lemon-Wong S, Yaffe MJ, Kolios MC, Czarnota GJ. Quantitative ultrasound evaluation of tumour cell death response in locally advanced breast cancer patients receiving chemotherapy. Clin Cancer Res. 2013;19(8):2163–2174.
4. Sadeghi-Naini A, Falou O, Tadayyon H, Al-Mahrouki A, Tran WT, Papanicolau N, Kolios MC, Czarnota GJ. Conventional-frequency ultrasonic biomarkers of cancer treatment response in vivo. Transl Oncol. 2013;6(3):234–243.
5. Sadeghi-Naini A, Patel RV, Samani A. Measurement of lung hyperelastic properties using inverse finite element approach. IEEE Trans Biomed Eng. 2011; 58(10):2852–2859.

Related News and Stories:

• Seeing the bigger picture: Researchers explore use of MRI and machine learning to customize treatment of brain tumours (Oct. 15, 2018)
• Will ultrasound be used one day to diagnose breast cancer?: Inexpensive imaging technique shows high potential to provide early, precise and noninvasive "acoustic" biopsy (SRI Magazine, 2018)
• A measured approach: researchers test use of quantitative ultrasound-based tool to detect cancer (Dec. 13, 2017)
• Echoes of success: Ultrasound-based imaging technique aims to personalize cancer care (Oct. 3, 2017)
• Back to basics: Federal agency invests in SRI scientists (June 30, 2016)
• Individualizing breast cancer therapy: New technologies can deliver highly specific information about a therapy’s effectiveness in days, not months; and help women with DCIS decide on the best course of action (SRI Magazine, 2016)
• CV: Dr. Ali Sadeghi-Naini (Oct. 21, 2015)

Related Links:

• U of T profile, department of medical biophysics