ESTRO 2020 Abstract Book

S422 ESTRO 2020

SP-0767 Clinical indications - Rationale and strategies M. Intven 1 1 UMC Utrecht, Radiation Oncology Department, Utrecht, The Netherlands Abstract text The clinical benefits of online MR-guided radiotherapy are twofold: imaging with better soft tissue contrast than with cone-beam CT and the possibility of daily online treatment plan adaptation. Clinical indications for MR-guided radiotherapy are indications which benefit from one or both of these opportunities. In this lecture typical clinical examples of such treatment sites are shown, like rectal cancer, pancreatic cancer and oligometastases. Besides, also the workflow on a 1.5T MR-linac is discussed. SP-0768 Motion-based treatment delivery L. Boldrini 1 1 Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Radiation Oncology, Rome, Italy Abstract text The talk will focus on motion management solutions offered in clinical Magnetic Resonance guided Radiotherapy. Target motion will be described in the different anatomical sites highlighting site specific mitigation strategies and the available clinical evidence. More specifically, patients selection and positioning, breathing control solutions and visual feedback approaches will be presented in the different clinical scenarios. SP-0769 Online MR-guided radiotherapy - Adaptation by size or function S. Bhide 1 1 the Institute Of Cancer Research And The Royal Marsden Nhs Foundation Trust, Head And Neck And Gi Oncology, Sutton, United Kingdom Abstract text HPV+ve oropharyngeal Cancer (OPC) is associated with markedly improved prognosis compared to non-HPV- associated OPC. The favorable prognosis, along with the long-term survival is the basis for treatment dose- escalation. While tumor responsiveness to radiotherapy in OPC has been shown to be associated with permanent tumor control outcomes, intra-treatment tumor shrinkage is observed as early as by fraction 10. The principle of adaptive radiotherapy planning relies on adjusting the treatment plan based on observed changes over the course of therapy. The increasing use of magnetic resonance imaging (MRI) for head and neck radiotherapy planning has the advantage of improved soft-tissue visualization, allowing for more confident assessment of anatomical tumor changes during treatment. MR-Linac (MRL) holds promise to facilitate such adaptive MR-guided radiotherapy (MRgRT) workflows by mean of daily on-line MRI during radiation treatment. Daily, MR guided RT adaptation of radiation dose based on change in shape in patients with OPC will be discussed in this presentation. Patients with complete response (CR) following neo- adjuvant chemo-radiotherapy (CRT) for rectal cancer adenocarcinoma are offered organ preservation. Response rates following (CRT) are dose-dependent, with dose escalation of >60 Gy increasing CR rates. MRI has potential to act as a biomarker, identifying good and poorly responding tumours to select patients for dose adaptation in order to improve treatment outcomes. Studies suggest

that functional imaging such as diffusion-weighted images (DWI) can predict pathological complete response early during CRT. MRL has the capability of obtaining daily functional imaging, which can potentially be used to identify treatment response early during CRT. This can potentially facilitate tailoring RT dose based on response. Functional image guided RT for rectal cancers and other tumours will be discussed.

Debate: This house believes that radiomics will improve predictive models in RT

SP-0770 For the motion: A. Dekker 1

1 Department of Radiation Oncology MAASTRO- GROW School for Oncology and Developmental Biology- Maastricht University Medical Centre+, MAASTRO, Maastricht, The Netherlands Abstract text For almost every cancer patient, images are being used for diagnosis, treatment planning, QA, treatment verification and follow-up. But most of the times, these images are only used by humans for the specific task at hand. Radiomics is all about automated, high-throughput quantitative image analysis. The adagium of Radiomcs is "Images are more than pictures, they are data". Some might say, what is new? For them Radiomics is simply an extension of something we do everyday in radiation oncology. But with more data and computing power now available and better algorithms to "mine" images, we are now able to see much more and different things in images with Radiomics than humans can. Radiomics has already led to outcome prediction models which are better than those based solely on clinical variables and certainly better than humans. With the move towards Deep Radiomics - based on deep learning - we can expect to see many routine and often mundane tasks performed by care professionals being taken over by Radiomics leading to higher quality care at reduced cost and more stimulating work for our profession. Perhaps more importantly, Radiomics will be crucial for patients and cancer outcomes as it will power adaptive radiotherapy and decision making in combined modality treatments, especially with immunotherapy. Abstract text In the last six/seven years great attention and enthusiasm raised around radiomics, with a considerable amount of literature proposing to use radiomic features to identify tumour phenotypes on medical images, to describe tumour heterogeneity and possibly use these features as predictors of clinical outcomes. Many studies are trying to identify “image biomarkers” with predictive/prognostic significance. Various studies obtained some initial promising results on the associations between radiomic features and oncological outcomes. Despite the clinical potential of radiomics, most times enthusiasm was not coupled to clever use of radiomics. Heterogeneity of acquisition protocols and image reconstruction methods impairs the robustness of radiomic studies. Furthermore, the SP-0771 Against the motion A. McWilliam University of Manchester, UK Abstract not available