ESTRO 2020 Abstract book

S381 ESTRO 2020

The presentation will address the questions: What is mentoring? And is there a difference between mentoring, supervision, sparring and sponsoring? What is important in order to achieve a good and rewarding relationship in a mentor-mentee relation? What are the specific roles of the mentor and the mentee? How do mentors and mentees work together to make sure that both learn from the experience?

reduced doses to organs at risk, which may lower radiation-induced side effects. In this presentation, literature will be critically reviewed on how automated treatment planning may contribute to the prevention of radiation-induced toxicity. The limitations of automated treatment planning will be discussed as well. Furthermore, we will address how automated treatment planning can be used as a decision tool to select the optimal treatment modality for an individual patient. This approach will be compared with decision tools that are not based on a full optimization of a treatment plan. Finally, in-silico studies that make use of automated treatment planning for example to determine the impact of treatment accuracy on treatment planning, the dose to organs at risk, and patient selection will be discussed. SP-0619 Insight in sub-organ radiosensitivity profiles may improve prediction models of toxicity G. Defraene 1 1 KU Leuven - University of Leuven, Department of Oncology - Experimental Radiation Oncology, Leuven, Belgium Abstract text Accurately predicting radiation-induced toxicity outcomes is a crucial step towards a precision medicine approach tailoring radiotherapy treatments to the individual patient. An informed clinical decision could then be based on the weighting of the risks from different normal tissue complication probability (NTCP) models. In practice, this could result in sparing the relevant organs at risk or referring the patient to the most effective treatment modality. In head and neck cancer, several NTCP models have been published, predicting the risk of relevant endpoints affecting the patient’s quality of life, e.g. xerostomia and dysphagia. These models combine dosimetric predictors with clinical and treatment-related risk factors. While these mostly logisitic regression-based NTCP models contain actionable dosimetric predictors, they often have suboptimal accuracy measures, with AUCs typically around 0.7. The limited discrimination performance currently leads to false positive and false negative predictions, and thus to suboptimal optimization objectives and/or patient selection. It could be hypothesized that the reason for the dosimetric suboptimality of these classical NTCP models is that DVH points or summarizing metrics as the mean doses of organs at risk (e.g. the parotid glands) were used as input to the modeling studies. As DVHs condense 3D planning dose maps into a 1D curve, clearly regional information is lost. This regional dependence hypothesis was underpinned by new evidence from small animal studies showing that suborgan radiosensitivity exists in the parotid gland. More specifically, the subregion of the major salivary gland containing stem cells was most radiosensitive. This finding was corroborated by human data, where imaging studies quantifying radiation-induced damage showed similarly significant regional differences. Parotid functioning on MR images and salivary flow rate on SPECT imaging showed a dose-related regional dependence, and were associated to xerostomia endpoints. This 3D dose-response knowledge could pave the way to improve the NTCP model accuracy. A voxel-wise statistical modeling strategy using the 3D dose map and taking into account regional information, was previously described. This technique could highlight the dose level of importance for the endpoint in association with a subregion of the parotid gland. Another method that could

Symposium: Preventing radiation-induced toxicity in head and neck cancer patients

SP-0617 Clinical approaches to preventing toxicity in head and neck cancer patients J. Kazmierska 1 1 Greater Poland Cancer Centre, Radiotherapy Dept, Poznan, Poland Abstract text Radiotherapy is one of the essential methods for treatment of head and neck cancer and can be combined with surgery, chemotherapy or immunotherapy. All therapeutic methods, including radiotherapy, can lead to acute and late side effects affecting quality of life of patients and survivors. Thus, minimizing and preventing toxicity is as important as the treatment itself. Both early and late sequelae of radiotherapy like mucositis, dermatitis and fibrosis depend on individual combination of many factors including patient and disease related features. Moreover, modern combination of radiotherapy with cytotoxic or targeted cancer therapies and immunotherapy not only escalate well known side effects but also result in new and different toxicity profiles. Thus, collecting of toxicity data including PROMs is essential to develop optimal ways to mitigate side effects or even predict them. New tools allowing such predictions based on combination of clinical factors with radiomics, genetic and molecular profile of tumour and healthy tissue are under testing and seem to offer promise in improving the quality of life of patients and survivors with head and neck cancer. SP-0618 Automated treatment planning for prevention of radiation-induced toxicity M. Hoogeman 1 1 Erasmus MC, Radiation Oncology, Rotterdam, The Netherlands Abstract text The definite treatment of head and neck cancer is associated with a considerable risk of moderate and sever acute and late side effects induced by radiation. These side effects deteriorate the patient’s quality of life. Modern radiation therapy techniques such as intensity- modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) combined with image-guidance were introduced to reduce these adverse effects. The introduction of IMRT and VMAT, however, added considerable complexity to the treatment planning process. The planner has to tweak the optimization parameters in order to properly balance the often conflicting constraints and objectives of the target volumes and organs at risk. This complexity results in considerable variation in the quality of the treatment plans. Automated treatment planning has been advocated as a means for reducing this variation and improving the quality of the treatment plans. The latter could result in