In our research team (https://www.radioonkologie.mri.tum.de/de/praeklinische-forschung/ag-ki-radiomics), we aim to develop an artificial intelligence (AI)-based system for individualized toxicity prediction in lung cancer patients based on CT imaging and spatial radiotherapy dose maps.

Toxicity caused by radiotherapy of lung cancer is a relevant and severe issue during cancer treatment and differs by individual radiotherapy dose and patient characteristics. By being able to better predict lung toxicity, radiotherapy treatments plans could be better individualized. In this thesis, we will identify common intra-pulmonal spatial high-risk regions based on voxel-based dose analysis using deformable image registration and permutation-based methods. Based on CT data we will develop a Machine Learning Model to predict individual high-risk regions using spatially resolved radiomic feature maps. Overall, we aim to integrate the AI models into a clinical decision support system that will enable individualized risk-stratification, outcome prediction and identify individual high-risk regions.

Our offer:

- Medical doctorate project in an interdisciplinary research team

- Active professional supervision

- Regular team and individual meetings

- Option for Post-doctoral research

- Working remotely and on-site

- Flexible working hours

What do we expect:

- Enthusiasm and interest in research and application of Machine Learning and Artificial Intelligence in cancer treatment

- Interest in technical and physical background is preferable

- Interest and skills in computer-based working

- Basic knowledge/experience in programming (python, R, …) is highly appreciated

- Basic knowledge in Medical Physics in Radiotherapy

- Good teamwork skills

If you are interested in this medical doctoral thesis, please contact:

Dr. rer. nat. Dr. med. Kim Melanie Kraus

Email: kim.kraus@tum.de