Scientist profiles S-Z

Administrative Assistant: Stephanie Kulczyski
Phone: 416-480-6100 ext. 61026
Email: stephanie.kulczyski@sunnybrook.ca

Education:

• B.Sc., 2003, anatomy and cell biology, McGill University, Canada
• PhD, 2018, radiomics (radiotherapy and oncology), Sheffield Hallam University, United Kingdom
• Post-doctoral fellow, 2018, radiation oncology, University of Toronto, Canada

Appointments and Affiliations:

• Senior scientist, Biological Sciences Platform, Odette Cancer Program, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre
• Staff radiotherapist, department of radiation oncology, Sunnybrook Health Sciences Centre
• Associate professor, department of radiation oncology, Faculty of Medicine, University of Toronto, Canada
• Adjunct professor, department of physics, Faculty of Science, Toronto Metropolitan University, Canada
• Canadian National Breast Cancer Consortium
• International Immuno-Oncology Working Group

Research Foci:

• Radiomics
• Ablative radiotherapy
• Biomarkers for chemotherapy resistance in breast cancer
• Digital pathology
• High-risk breast cancer
• Personalized oncology

Research Summary:

Dr. Tran is a clinician and scientist at Sunnybrook Health Sciences Centre. The focus of Dr. Tran’s research is to identify predictive (i.e. pre-treatment) and early-response biomarkers for tumour response to chemotherapy and radiotherapy using artificial intelligence. His team is exploring quantitative imaging biomarkers from digital pathology to design computer-aided theragnostic (CAT) systems. The primary endpoint of Dr. Tran’s research will be improved patient outcomes by using biomarker measurements for adaptive and response-guided cancer treatments.

Selected Publications: 

See current publications list at PubMed.

1. Gandhi S, Brackstone M, Hong NJL, Grenier D, Donovan E, Lu FI, Skarpathiotakis M, Lee J, Boileau JF, Perera F, Simmons C, Joy AA, Tran WT; Canadian National Neoadjuvant Breast Cancer Consortium. A Canadian national guideline on the neoadjuvant treatment of invasive breast cancer, including patient assessment, systemic therapy, and local management principles. Breast Cancer Res Treat. 2022 Feb 28. doi: 10.1007/s10549-022-06522-6. Epub ahead of print. PMID: 35224713.
2. Lagree A, Shiner A, Alera MA, Fleshner L, Law E, Law B, Lu FI, Dodington D, Gandhi S, Slodkowska E, Shenfield A, Jerzak KJ, Sadeghi-Naini A, Tran WT. Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Curr Oncol [Online Ahead of Print]. 2021 Oct 27;28(6):4298–316. Available from: https://www.mdpi.com/1718-7729/28/6/366.
3. Lagree A, Mohebpour M, Meti N, Saednia K, Lu FI, Slodkowska E, Gandhi S, Rakovitch E, Shenfield A, Sadeghi-Naini A, Tran WT. A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Sci Rep. 2021 Apr 13;11(1):8025. doi: 10.1038/s41598-021-87496-1. PMID: 33850222; PMCID: PMC8044238.
4. Meti N, Saednia K, Lagree A, Tabbarah S, Mohebpour M, Kiss A, Lu F, Slodkowska E, Gandhi S, Jerzak KJ, Fleshner L, Law E, Sadeghi-Naini A, & Tran WT. Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathologic Features. JCO Clinical Cancer Informatics. 2020 Nov.
5. Saednia K, Tabbarah S, Lagree A, Wu T, Klein J, Garcia E, Hall M, Chow E, Rakovitch E, Childs C, Sadeghi-Naini A, Tran WT. Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity from Breast Radiotherapy Using Supervised Machine Learning. Int J Radiat Oncol Biol Phys. 2020 Jan 23. pii: S0360-3016(19)34550-X. doi: 10.1016/j.ijrobp.2019.12.032. [Epub ahead of print] PubMed PMID: 31982495.
6. Tran WT, Suraweera H, Quaioit K, Cardenas D, Leong KX, Karam I, Poon I, Jang D, Sannachi L, Gangeh M, Tabbarah S, Lagree A, Sadeghi-Naini A, Czarnota GJ. Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer. Future Science OA. 2019 Nov. doi: 10.2144/fsoa-2019-0048
7. Tran WT, Sadeghi-Naini A, Lu FI, Gandhi S, Meti N, Brackstone M, Rakovitch E, Curpen B. Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence. Can Assoc Radiol J. 2020 Aug 31:846537120949974. doi: 10.1177/0846537120949974. Epub ahead of print. PMID: 32865001.
8. Tran William T., Gangeh Mehrdad, Sannachi Lakshmanan, Chin Lee, Watkins Elyse, Bruni Sylvio, Rastegar Rashin Fallah, Curpen Belinda, Trudeau Maureen, Gandhi Sonal, Yaffe Martin, Slodkowska Elzbieta, Childs Charmaine, Sadeghi-Naini Ali & Czarnota Gregory J. Predicting Breast Cancer Response to Neoadjuvant Chemotherapy Using Pre-treatment Diffuse Optical Spectroscopic-Texture Analysis. British J. Cancer 2017 [Epub ahead of print, In press].
9. Tran WT, Childs C, Chin L, Slodkowska E, Sannachi L, Tadayyon H, Watkins E, Wong SL, Curpen B, El Kaffas A, Al-Mahrouki A, Sadeghi-Naini A, Czarnota GJ. Multiparametric monitoring of chemotherapy treatment response in locally advanced breast cancer using quantitative ultrasound and diffuse optical spectroscopy. Oncotarget. 2016 Apr 12;7(15):19762-80. doi: 10.18632/oncotarget.7844. PubMed PMID: 26942698; PubMed Central PMCID: PMC4991417.
10. Tran WT, Iradji S, Sofroni E, Giles A, Eddy D, Czarnota GJ. Microbubble and ultrasound radioenhancement of bladder cancer. Br J Cancer. 2012 Jul 24;107(3):469-76. doi: 10.1038/bjc.2012.279. Epub 2012 Jul 12. PubMed PMID: 22790798; PubMed Central PMCID: PMC3405216.

Related News and Stories

• 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)