Applications of Automated Treatment Planning in Adaptive Radiotherapy
Presenter:
Aly Khalifa, PhD Student
University of Toronto
Abstract:
Technological advancements in radiotherapy have significantly enhanced treatment precision and patient safety, but they have also introduced a greater burden of manual, time-intensive tasks for clinicians to manage these innovations effectively. Machine Learning (ML) offers a promising solution to streamline these processes and optimize treatment outcomes.
The work of Aly and his colleagues explores applications of automated treatment planning in adaptive radiotherapy procedures. ML is used to predict an ideal radiation dose distribution based on the position of the tumour during treatment to reduce unnecessary radiation exposure to healthy tissues. In comparison to existing clinical methods, the ML automated technique requires no human intervention during the planning process. This removes the reliance on human skill to drive the treatment process.
The work of Aly and his colleagues demonstrates that ML improves the quality of treatment by reducing the radiation dose delivered to healthy tissues, compared to current clinical adaptation techniques. These findings suggest that automated ML-based approaches could substantially improve clinical workflows and patient outcomes in adaptive radiotherapy.