Radiooncoclogy

The success of radiotherapy depends on precision. The integration of magnetic resonance imaging (MRI) with a linear accelerator (linac) in a single hybrid device (MRI-linac) for MRI-guided adaptive online radiotherapy is one of the most important technological advances in radiotherapy. During each treatment session, an MRI is performed, the contours of the tumor and the organs at risk are adjusted and the radiation plans are re-optimized. Anatomical changes (tumor growth or shrinkage, edema of healthy tissue) can be detected during radiotherapy, allowing an accurate assessment of treatment-related side effects. Adaptive online MRI not only improves geometric high-precision treatment, but also captures functional and quantitative MRI data, enabling sequential monitoring of quantitative imaging biomarkers (QIB) during radiotherapy. This may enable early response prognosis and individualized dose prescription based on treatment response and biological prognostic factors. The aim of this research focus is to analyze the online integration of anatomical and functional imaging in adaptive radiotherapy.

As part of innovative research projects, the Section for Biomedical Physics is carrying out various aspects of the use and further development of machine learning (ML) and artificial intelligence (AI) in the field of medical physics and radiation oncology. In cooperation with the Cluster of Excellence Machine Learning and the MiDAs Lab of the Department of Radiology, for example, ML methods for the automatic annotation of multi-modal image data are developed and validated for subsequent use in radiation therapy planning. Other research projects deal with the rapid, AI-based calculation of radiation dose in tissue or with the automation of radiation planning by using deep neural networks.

Publications

Modern imaging techniques such as positron emission tomography (PET) or magnetic resonance imaging (MRI) not only allow the body's anatomy to be displayed, but also enable the recording and quantification of physiological processes in the human body using functional imaging. The information obtained from this is used in the Section for Biomedical Physics to individualize radiotherapy for patients. PET and MRI imaging are used to better estimate tumor size and to predict patient response to therapy. Another goal is to use functional imaging techniques to identify patient-specific, radioresistant tumor areas that are treated with an increased radiation dose in clinical studies.

The Department of Radiation Oncology conducts studies that combine maximum geometric precision through technological advancements and biological precision. Rectal cancer is one example of this. As part of DFG-funded projects, a new treatment plan for dose-escalated radiotherapy of rectal cancer was established, which leads to complete tumor regression in many patients with early tumor stages. Functional imaging also plays a central role here, acquiring longitudinal data during treatment and evaluating them quantitatively. In the previous CAO/ARO/AIO-16 study on organ preservation in rectal cancer, RNA sequencing was used to identify potential target genes for radiosensitization, which are currently being investigated in more detail as part of research projects.