Dr. Kailyn Stenhouse
Positions
Medical Physics Resident
Faculty of Science, Radiation Oncology Physics
Medical Physics Resident
Arthur J.E. Child Comprehensive Cancer Centre
Member
Arnie Charbonneau Cancer Institute
Contact information
Preferred method of communication
For future partnerships or other inquiries please email me at kailyn.stenhouse@albertahealthservices.ca
Background
Educational Background
PhD Radiation Oncology Physics, University of Calgary, 2024,
BSc Physics and Astronomy, University of Calgary, 2017,
Biography
Dr. Kailyn Stenhouse received her HBSc in Physics in 2017 from the University of Calgary, where she completed an undergraduate research project in atmospheric physics investigating the stable isotope composition of rainwater across southern Alberta. She went on to complete her PhD in Radiation Oncology Physics at the same institution in 2024, with a thesis titled "Applications of Machine Learning to the High-Dose-Rate Cervical Brachytherapy Workflow: Applicator Prediction and Late Toxicity Modelling."
During her doctoral studies, her research focused on combining advanced computational techniques including artificial intelligence and Bayesian methods with clinical data to improve gynecologic cancer care. She developed predictive models for both treatment technique selection and long-term toxicity outcomes, aiming to move the field toward more patient-specific, data-driven decision-making. By integrating these tools into the clinical workflow, her work aspires to make radiation therapy not only more efficient but also better tailored to individual patient needs.
After completing her PhD, Dr. Stenhouse began her clinical residency at the Tom Baker Cancer Centre in 2024, later relocating to the Arthur Child Comprehensive Cancer Centre. Her clinical interests include brachytherapy, automation, and the implementation of AI-driven tools in radiation oncology.
Research
Areas of Research
- Brachytherapy
- Outcome Prediction
- Artificial Intelligence and Machine Learning
Dr. Stenhouse's primary research interest is the application of machine learning, artificial intelligence, and Bayesian techniques to gynecologic brachytherapy. These techniques are applied to treatment technique and outcome prediction, more specifically using data-driven models to guide interventional, patient-specific care. In the future, this research could be used to support adaptive planning strategies and enable more individualized treatments, with the aim of improving long-term clinical outcomes for patients.
Publications
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