ESTRO 2020 Abstract book

S909 ESTRO 2020

There were no significant differences in pulmonary toxicities between the two arms. For RP, target size and infection were significantly linked to the risk of RP. Patients presenting dyspnea at start and increase of cough during RT showed higher incidence of RP as well. No multivariate RP model could be established for RP, most likely because of low sample size. PO-1582 Dose rate effects in robotic radiosurgery treatments E. Zoros 1 , A. Moutsatsos 2 , L. Lekas 2 , P. Pantelakos 2 , E. Pantelis 1 1 Medical School of National and Kapodistrian University of Athens, Medical Physics Laboratory, Athens, Greece ; 2 Iatropolis Clinic, CyberKnife and TomoTherapy department, Athens, Greece Purpose or Objective Treatment time in robotic radiosurgery depends on the size and shape of the target, the number and size of collimators, as well as the nominal dose-rate of the system. In this study, the influence of dose-rate on the biologically effective dose (BED) was investigated for a patient cohort suffered from vestibular schwannomas. Material and Methods For the purpose of this study, a group of 24 patients treated for vestibular schwannomas with a G4 CybeKnife (Accuray Inc., CA, USA) model was selected. Target volumes varied from 0.27 to 5.66 cc, with a mean value of 2.71 cc. Collimator sizes of 5, 7.5, 10 and 12.5 mm were used. On average 146 beams were used ranging from 90 to 202. All cases were treated with a prescription dose of 14 Gy in a single fraction planned using the MultiPlan v.4.6 (Acurray Inc) treatment planning system. An in-house ray tracing dose calculation algorithm based on the measured beam data was developed to calculate the dose-rate in a voxel-level from each beam of each case using 800 and 1000 MU/min linear accelerator (linac) nominal output values. Second quartile dose rate distributions were created in each case indicating the dose-rate that corresponds to 50% of the total dose at each voxel. For each patient, BED distributions were calculated in a region covering the irradiated target based on the linear- quadratic model with and without taking into account the effects of dose-rate, the time between beams and the overall treatment time. Slow and fast components of repair were incorporated into the BED formulation that takes into account the aforementioned time-depended terms. Results The target average second quartile dose-rates of the studied cases were found equal to 2.49 ± 0.34 and 3.11 ± 0.43 Gy/min for the 800 and 1000 MU/min output values, respectively. Averaged BED values of 97 ± 3 Gy 2.47 covering 95% of the target volume were found without taking account of slow and fast repairs, while dropped down to 78 ± 3 Gy 2.47 for the 800 MU/min nominal dose-rate when time-dependent parameters were considered. A dependence of 2.5% was found between the 800 and 1000 MU/min linac output rates. Conclusion A BED loss of approximately 20% was found in robotic radiosurgery treatments when dose rate effects were considered. This BED loss was found to improve by 2.5% when the 800 MU/min linac was replaced with the 1000 MU/min one.

PO-1583 Non-invasive radiomic imaging prediction of tumour hypoxia: biomarker for FLASH irradiation? S. Sanduleanu 1 , T. Tamanupadhaya@gmail.com 2 , R. Klaassen 3 , H. Woodruff 4 , M. Hatt 5 , J. Kaanders 6 , O. Vrieze 7 , H. Laarhoven 8 , R. Subramiam 9 , S.H. Huang 10 , S. Bratman 11 , L. Dubois 12 , R. Miclea 13 , D. Di Perri 14 , X. Geets 15 , M. Crispin-Ortuzar 16 , A. Aptea 17 , J. Hun Oh 18 , N. Lee 18 , J. Humm 18 , H. Schoder 18 , D. Ruysscher 19 , F. Hoebers 19 , P. Lambin 1 1 Maastricht University, Precision Medicine, Maastricht, The Netherlands ; 2 University of California San Francisco UCSF Medical Center, Radiation Oncology, San Francisco, USA ; 3 AMC Medical Center, Medical Oncology, Amsterdam, Belgium ; 4 Maastricht University, Precision Medicine, Maastricht, Belgium ; 5 French Institute of Health and Medical Research, Unit of Continuous Optimization of Therapeutic Actions through the Integration of Multimodal Information, Brest, France ; 6 Radboud hospital, Radiation Oncology, Nijmegen, Belgium ; 7 Antoni van Leeuwenhoek Medical Centre, Radiation Oncology, Amsterdam, Belgium ; 8 AMC Academic Medical Centre, Medical Oncology, Amsterdam, Belgium ; 9 University of Texas Southwestern Medical Centre, Department of Radiology and Radiologic Sciences, Dallas, USA ; 10 Princess Margaret Cancer Center, Radiation Oncology, Toronto, Canada ; 11 Princess Margaret Cancer Centre, Radiation Oncology, Toronto, Canada ; 12 Maastricht University, Department of Precision Medicine- The M-LAB, Maastricht, Belgium ; 13 Maastricht University Medical Centre+, Radiology, Maastricht, Belgium ; 14 Cliniques universitaires Saint- Luc, Radiation Oncology, Ziekenhuis in Sint-Lambrechts- Woluwe, Belgium ; 15 Cliniques Universitaires St-Luc, Radiation Oncology, Brussels, Belgium ; 16 University of Cambridge, Cancer Research UK Cambridge, Cambridge, United Kingdom ; 17 Memorial Sloan Kettering Cancer Center, The Joseph Deasy Lab, New York, USA ; 18 Memorial Sloan Kettering Cancer Center, Radiation Oncology, New York, USA ; 19 Maastro Clinic, Radiation Oncology, Maastricht, Belgium Purpose or Objective Tumor hypoxia increases resistance to radiotherapy and systemic therapy. The FLASH effect, demonstrating a remarkable sparing of normal tissue after irradiation at ultra-high dose rate (>40Gy s -1 ), is hypothesized to deplete oxygen too quickly for diffusion to maintain an adequate level of oxygenation, and consequently the normal tissue will respond as a hypoxic tissue. When a hypoxic (radioresistant) tumour is surrounded by an oxic tissue (radiosensitive), ultra-high dose rate will increase the radioresistance of the normal tissue with small impact on the already hypoxic tumour tissue. Our aim was to develop and validate an agnostic and site-specific CT and FDG-PET- based radiomics hypoxia classification signature as a biomarker for future (pre)-clinical FLASH trials. Material and Methods A total of 808 patients from 8 registered prospective clinical trials were included in the generation and validation of the CT and FDG-PET hypoxia classification signature. The primary gross tumor volumes (GTV) were manually defined on CT. In order to dichotomize between hypoxic/well-oxygenated tumors threshold values of 10%, 20% and 30% were used for the [ 18 F]-HX4-derived hypoxic fractions (HF, defined as the ratio between the high uptake hypoxic regions with tumor-to-background ratio>1.4 to total GTV). A random forest (RF)-based machine-learning classifier was trained to classify patients as hypoxia-positive/ negative based on radiomic features. Results An agnostic CT model reached AUC’s of 0.79±0.16, 0.76±0.18 and 0.72±0.14 respectively in external validation by combining 5 CT-derived radiomic features to classify hypoxia (HF cutoff 20%). An agnostic FDG-PET model reached an AUC of 0.74±0.23 in external validation