Speaker
Description
Medical training at Eberhard Karls University Tübingen, Aberdeen, Royal Infirmary (UK), Radboud University Nijmegen (NL)
MD degree (Staatsexamen)
German doctoral degree; Medizinische Hochschule Hannover
PhD degree; Radboud University Nijmegen (NL)
Assistant Professor at Maastricht University, Department of Radiation Oncology, Faculty of Health, Medicine and Life Sciences, Maastricht (NL)
University Professor of ‚Image-Guided High-Precision Radiotherapy‘ at HZDR, OncoRay – National Center for Radiation Research in Oncology, University Hospital and Faculty of Medicine Carl Gustav Carus Dresden, associated with German Cancer Consortium (DKTK), National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ)
Chair of Department of Radiotherapy and Radiation Oncology of University Hospital Carl Gustav Carus Dresden
Dean of Research, Faculty of Medicine, Technische Universität Dresden, Germany
Dean of the Faculty of Medicine, Technische Universität Dresden, Germany
Summary
Artificial intelligence (AI) is rapidly changing the way we live and the way we work. Ranging from personalised treatment advices during tumor boards to AI-supervised reading of mammography or CT images, AI is also entering the world of oncology and of radiation therapy in particular.
For radiation therapy, the use of AI-based applications, e.g. for contouring of normal tissues and the target volumes, or for predicting the therapeutic outcome, is being intensively researched and discussed. Moreover, AI is rapidly transforming biology-guided radiotherapy for example, and shifting the focus of cancer treatment from pure anatomy to cellular biology and metabolic activity. Biologically-guided radiotherapy incorporates (1) the tumor characteristics, such as hypoxia, proliferation, stem cell number, into the definition of the biological target volume and (2) functional imaging methods, such as positron emission tomography (PET) or functional magnetic resonance imaging (MRI) to depict the tumor biology. The concept of biologically-guided radiotherapy may finally come into clinical action, since AI algorithms can integrate real-time image data (MR-Linacs, CBCT-Linacs) to track tumors and adapt radiation therapy based on individual biological characteristics.
During the presentation, the current status and pending challenges will be addressed, alongside ethical and technical questions.