I am a post doc researcher focusing on the application of artificial intelligence in radiotherapy, with particular focus on deep learning-based auto-segmentation in breast cancer. My work combines clinical radiotherapy, AI and quality assurance, with a focus on developing and evaluting methods that can support more consistent and efficient radiotherapy across the nation.
My research focuses on variation in delineations in breast cancer radiotherapy, and its impact on dose and treatment plans. I also work with deep learning-based auto-segmentation and dose prediction and the implementation of this in the clinic.
My research also focuses on the usage of big datasets, standardisation of these and what we can get from analysing clinical data.
I am also involved in AIM@cancer.
PhD in medical physics, Aarhus University, 2024. PhD project: DBCG RT Nation Automation - National consistency in delineations in breast cancer patients.
MSc in Physics, Aarhus University, with a focus on medical physics.
I collaborate with clinical staff and researhc groups within radiotherapy, medical physics and artificial intelligence. My work includes national Danish Breast Cancer Group (DBCG) collagorations as well as collaborations with research groups abroad, including the Netherlands Cancer Institute. i am also involved in AIM@cancer and international collaborations on improving the documentation model development and how to standardize data.
My primary area of work is the initiation and conduct of a national prospective randomized study investigating AI-assisted versus manual contouring in radiotherapy. In addition, i contribute to quality assurance analysis of finished clinical radiotherapy trials, such as the DBCG IMN2 study.
