ESTRO 2022 - Abstract Book

S757

Abstract book

ESTRO 2022

studies.

First, mathematical tumor growth models allow predicting the path of infiltrative tumor cells. Adaptation to GBM, the microscopic spread has been modeled using partial differential equations of the reaction-diffusion type combined with information of anatomical barriers, grey and white matter, and white matter tracts. The frequently used Fisher–Kolmogorov equation describes tumor growth via the two factors “diffusion” and “proliferation”. Early models had to rely on a seed point for initiation rather than the actual GTV and used approximate values for relevant parameters such as the cell density. Recent developments incorporated multimodal imaging such as MRI and 18F-fluoro-ethyl-tyrosine (18F-FET) PET for spatially resolved tumor density estimation and patient-specific Diffusion Tensor Imaging (DTI) for the anatomic definition of anisotropic growth along white matter tracks. Second, the development of imaging-specific artificial intelligence techniques such as deep convolution neural networks, often referred to by the term “Deep Learning”, have opened new possibilities in image processing and analysis. Deep Learning-based elimination of the free water component in peri-tumoral edema has been shown to improve the predictive value of the local recurrences based on fractional anisotropy. In another study, a convolutional neural network could be successfully trained to differentiate glioblastomas from brain metastases based on peri-tumoral free water-corrected fractional anisotropy.Moreover, machine learning algorithms have been deployed for tumor growth modeling to allow training on patient-specific data. Through the integration of such techniques with mechanistic models growth prediction could further be improved. Alternatively, generative adversarial networks or convolutional neural networks were directly trained to predict tumor growth based on longitudinal imaging studies. In conclusion, Deep Learning and tumor growth modeling-based estimation of microscopic invasion of GBM may be used for individualized CTV definitions. In patients with a high risk for more distant progression, larger anisotropic CTVs may be indicated. In patients with a low risk for distant progression, smaller CTVs may be sufficient which could lead to reduced toxicity and improved opportunities for re-irradiations. The improvement of contemporary techniques and their validation in future prospective trials is warranted before clinical application.

SP-0856 Linking CTV margin uncertainties to clinical outcome

E.M.. Vasquez Osorio

United Kingdom Abstract not available

SP-0857 Selection of CTV volume and dose: How to include knowledge from patterns of failure

R. Zukauskaite 1

1 Odense University Hospital, Oncology, Odense M, Denmark

Abstract Text Definition of the optimal result in radiotherapy is elimination of all cancer (stem) cells so no surviving cells are left that eventually can result in disease recurrence. Radiotherapy is a local treatment modality and a deliberate effort during treatment planning is made to encompass macroscopic (GTV) and microscopic (CTV) disease into the final treatment volume to maximise treatment results. In decades before advanced diagnostic imaging modalities and technical possibilities of treatment delivery and planning systems, the uncertainty of real disease expansion was substantial. Thus CTV volumes included often at least 1 cm around GTV and often the whole anatomical compartment. Advanced diagnostics and gained knowledge from analysing surgical specimens of removed tumours made it possible to optimise tumour identification and reduce the GTV to high dose CTV margin more objectively and less by speculations. Despite high biological doses given to eradicate cancer, recurrences occur most often at the primary disease sites. Several attempts to identify the subvolumes of treatment volumes that could be boosted with a higher dose, or patients with positive predictors of disease that could maybe receive de-escalated treatment instead, sparing them from side effects have been attempted. However, recurrences in the GTV are still the most frequent and hence biological radio resistance is still the most important topic in treatment success. I aim to review the changes in treatment margins from GTV to CTV using head and neck cancer patients and their treatment as examples. Besides, I would like to review the knowledge of loco-regional recurrences in head and neck cancer patients and make some highlights in potential ways to improve disease control.

Symposium: Adapting to changes on different time scales

SP-0858 Tracking, trailing and gating: How fast should "real-time" adaptation be?

M. Fast 1

1 University Medical Center Utrecht, Radiotherapy, Utrecht, The Netherlands

Abstract Text Cardiorespiratory motion is an important source of uncertainty during thoracic and abdominal radiotherapy. Real-time motion mitigation techniques have the potential to minimize or eliminate the dosimetric effects of (residual) target motion