Session Item

Over the course of a conventional radiotherapy treatment, additional magnetic resonance images or positron emission tomography (PET) images require auxiliary time and effort, often across different clinical departments, like radiology or nuclear medicine. Therefore, an adaptation of a treatment plan during therapy is challenging and may only be realized unfrequently or at specific pre-defined timepoints. Hybrid MR-Linacs, combining a linear accelerator with magnetic resonance imaging, address this problem by providing the possibility of daily magnetic resonance imaging (MRI) during fractionated radiotherapy. Hence, the position of the target structures as well as organs at risk can be visualized on a daily basis and the positioning of the patient as well as the daily treatment plan can be adapted online. By doing so, margins around the target volumes and doses to organs at risk can be minimized.

While anatomical imaging at the start or during a treatment fraction may improve the accuracy of the radiation delivery, little information about the individual response to therapy is gained. Therefore, various recent studies evaluated quantitative imaging biomarkers (QIBs) for their potential with regard to correlating QIB to radiotherapy outcome and identifying predictive or prognostic QIBs representing the tumor response to radiotherapy. Usually, these QIBs are derived from functional MRI or PET, which in the case of MRI can also be acquired daily on the MR-Linac. Possible biomarkers are the apparent diffusion coefficient (ADC) from diffusion weighted MRI, the transfer constant (Ktrans) from dynamic contrast enhanced imaging or the standard uptake value from PET. But the integration of QIBs in the standard clinical workflow in the context of therapy adaptation based on a QIB has yet to be done. Apart from validating the acquisition of a QIB and its prognostic value, the technical integration of the images into the clinical workflow is quite difficult, especially in online adaptive radiotherapy on MR-Linacs.

This talk will discuss different strategies on how to implement online as well as offline adapt-to-response workflows, firstly. Different QIB have shown predictive or prognostic potential, including changes of tumor volume. Furthermore, the necessary steps for the translation of a biomarker into a clinical workflow will be presented, including determining the QIB, technical validation of the acquisition and correlation of the QIB to outcome parameters. The longitudinal assessment of diffusion-weighted MRI as a response marker for head and neck cancer radiotherapy will be described and results on diffusion-weighted MRI in head and neck cancer assessed on a 1.5 T MR-Linac including repeatability and reproducibility will be presented. Finally, an example for a response adaptive workflow out of a phase I trial on diffusion-weighted MRI based response adaptive MR-guided radiotherapy will be shown. Here, a radioresistant subvolume inside the GTV, defined by a band of ADC values, will be dose escalated once weekly using a hybrid MR-Linac.