Metastasis is a complex multi-step process that accounts for nearly 90% of cancer-related deaths. The ability to predict the participation of circulating tumor cells in metastasis and the location of secondary tumor sites has significant impacts on saving lives. Furthermore, in metastatic patients with no prior diagnosis records, a tool that can predict the primary cancer site based on the current metastatic state is of paramount importance for guiding therapies. Despite its vital importance, there exists no predictive method that could guide clinical decisions and therapies.

We aim to develop a patient-specific imaging-based predictive framework to model the fluid dynamics within the patient’s circulatory system and to simulate the separation, transportation and arrest of cancer cells. This framework will be developed to have the following capabilities: 1) to predict most probable secondary-cancer sites to make clinical diagnosis and staging more efficient and precise; 2) to predict the primary cancer site in metastatic patients with no prior records of the primary cancer for targeted treatments.

Computational Metastasis

University of Waterloo

Lab Leader

Industry Partner

Nima Maftoon


Faculty of Engineering Department of Systems Design Engineering

University of Waterloo

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Lab Team

Johnson Darko

Grand River Hospital

Elazer Edelman

Harvard & MIT

Julio Garcia Flores

University of Calgary 

Shruti Nambiar

University of Waterloo

Ernest Osei
Grand River Hospital

Jose de la Torre Hernandez
Hospital Universitario

Postdocs and Graduate Trainees

Sina Anvari

Masters Candidate, University of Waterloo

Arash Ebrahimian

PhD Candidate, University of Waterloo

Hossein Mohammadi

PhD Candidate, University of Waterloo

Pouyan Keshavarz

PhD Candidate, University of Waterloo

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