Vanier scholar develops innovations for use of AI in cancer treatment planning
By Andres Mona
Edward Wang, MD/PhD’27 candidate (Photo by Megan Morris/Schulich Medicine & Dentistry Communications)
As academics and scientists around the world seek the best way to navigate the fast-growing world of Artificial Intelligence (AI), a Schulich School of Medicine & Dentistry student has incorporated the technology into his program to help doctors choose the best radiation treatment for patients with lung cancer.
Edward Wang, MD/PhD’27 candidate in medical biophysics, addresses a pressing concern in cancer treatment through his research on metastases in the lungs, which is when tumours or “spots” spread to the lungs from cancer in other parts of the body.
Finding these spots can be extremely difficult. The challenge is to kill the cancer cells without harming the healthy parts of the lungs. Calculating the right radiation dose takes time and is quite complicated, making it hard for doctors to compare treatment options for patients quickly.
Wang’s focus is on using AI to improve and speed up treatment planning for a special type of radiation called stereotactic radiotherapy, which can target multiple spots in the lungs.
“Our tool is used to help medical staff estimate and compare potential treatment plans so that they can compare them, select the optimal one, and then create the right prescription for the patient,” said Wang.
“Our tool is used to help medical staff estimate and compare potential treatment plans so that they can compare them, select the optimal one, and then create the optimal prescription for the patient.”
- Edward Wang
Everyone is interested in AI now
Originally from Vancouver, B.C., Wang, 28, studied chemical and biological engineering during his undergrad at the University of British Columbia (UBC). While working with a surgeon at Vancouver General Hospital, he was inspired to apply to a combined doctorate and medical school program.
“Everyone’s interested in AI now, which was already becoming notable four to five years ago. Back then, I found it super interesting and thought I would get into it as early as possible. For this reason, I chose to study at Western, which has a robust imaging program.”
- Edward Wang
Around this time, Wang was also considering his research path. With a growing passion for biomedical engineering and with the increasing popularity of AI, Wang began a combined program in 2020 at Schulich Medicine & Dentistry.
“Everyone’s interested in AI now, which was already becoming notable four to five years ago,” said Wang. “Back then, I found it super interesting and thought I would get into it as early as possible. For this reason, I chose to study at Schulich Medicine, which has an excellent imaging program.”
Wang was named a 2023-2024 Vanier Scholar for his innovations in AI research. The Vanier scholarship is an award that recognizes individuals for their unique leadership skills and high standards of scholarly achievement.
Wang noted the typical process for planning radiation treatment has been the same for years, but the issue with lung metastases is more complicated.
“The issue with planning multiple lesions (metastases) is that you still use the same process, but because there are more targets to treat, it's a lot easier to overdose those healthy organs with radiation, making this a lot more challenging,” he said. “That means it takes a long time to create a cancer treatment plan.”
To accomplish this, Wang and his supervisors, Sarah Mattonen, PhD, assistant professor, Medical Biophysics and Oncology, and Dr. Pencilla Lang, MD/PhD, radiation oncologist, have developed an AI computer program that can quickly and accurately predict how the radiation will be distributed in the lungs when treating multiple cancer spots.
Currently, Wang and his team are working on making this AI program a part of the regular process doctors use when treating cancer patients at the London Regional Cancer Program.
Additional details of the team’s research can be found in their recent publication in the International Journal of Radiation Oncology – Biology – Physics.
