ESTRO 36 Abstract Book

S854 ESTRO 36 2017 _______________________________________________________________________________________________

parameters. We approach the problem on a mechanistic level, linking nanoscale energy deposition to cellular repair. Material and Methods We present a stochastic model to predict ion induced DNA damage and subsequent repair. DNA damage patterns are predicted using nanodosimetric principles applied to track structure simulations within the Monte Carlo based Geant4-DNA toolkit. A section of detailed DNA geometry is irradiated to study specific DNA double strand break structures; building up a library of break models for a given radiation quality. These patterns are then fed into a modified Geant4-DNA simulation where the DNA double strand break ends are explicitly modelled within a simplified cell nucleus. Double strand break ends then progress along the predefined Non-Homologous End Joining repair pathway according to stochastic, time constant based state changes. This allows the prediction of differences in DNA repair for a range of radiation qualities. Results We show that break complexity and repair kinetics are dependent on the particle LET and particle type, with more complex breaks becoming more probable for higher LET (fig 1.). Our simulations predict a greater number of residual DSBs after 24h when higher LET particles are used (fig 2.), which is in good agreement with the literature. We also observe a difference in break complexity for protons and alpha particles at the same LET due to differences in radiation track structure.

EP-1605 Deep learning of radiomics features for survival prediction in NSCLC and Head and Neck carcinoma A. Jochems 1 , F. Hoebers 1 , D. De Ruysscher 1 , R. Leijenaar 1 , S. Walsh 1 , B. O'Sullivan 2 , J. Bussink 3 , R. Monshouwer 3 , R. Leemans 4 , P. Lambin 1 1 MAASTRO Clinic, Radiotherapy, Maastricht, The Netherlands 2 Princess Margaret Cancer Centre, Cancer Clinical Research Unit, Toronto, Canada 3 Radboud University Medical Center Nijmegen, Radiation Oncology, Nijmegen, The Netherlands 4 VU University Medical Center, Department of Otolaryngology/Head and Neck Surgery, Amsterdam, The Netherlands Purpose or Objective In order to facilitate personalized medicine in cancer care, predictive models are of vital importance. Radiomics, the high-throughput extraction of large amounts of image features from radiographic images, facilitates predictive model development by providing non-invasive biomarkers. Previous work indicates that radiomics features have high predictive quality 1 . However, these studies used conventional models and the added value of deep learning combined with radiomics features is unexplored. Furthermore, conventional modelling strategies require a selection of features to establish a signature whereas deep learning algorithms do not. In this work we learn a deep learning model on radiomics features and compare it to a previously published cox regression model 1 . Material and Methods 4 independent Lung and Head & Neck (H&N) cancer cohorts (1418 total patients) were used in this study. Radiomic features were extracted from the pre-treatment computed tomography images. The model was learned on the Institute 1 lung cohort (N=422) and validated on the other datasets. The outcome is two-year survival following treatment. A 3 layer deep learning network was used to make predictions. Results Validation on Institute 2 dataset (N=154) yields an AUC of 0.71 (95% CI: 0.63-0.8) for the deep learning network and 0.66 on the conventional model (95% CI: 0.56-0.75). The difference is not significant (P=0.11). Validation on Institute 3 dataset (N=95) yields an AUC of 0.64 (95% CI: 0.53-0.79) for the deep learning network and 0.75 on the conventional model (95% CI: 0.64-0.86). The difference is not significant (P=0.19). Validation on Institute 4 dataset (N=136) yields an AUC of 0.71 (95% CI: 0.59-0.8) for the deep learning network and 0.74 on the conventional model (95% CI: 0.64-0.83). The difference is not significant (P = 0.24). Validation on Institute 5 dataset (N=540) yields an AUC of 0.58 (95% CI: 0.52-0.63) for the deep learning network and 0.65 on the conventional model (95% CI: 0.59- 0.70). The difference is not significant (P = 0.10).

Conclusion Monte Carlo track structure simulation coupled to a mechanistic DNA damage repair simulation is a useful tool for modelling biologically relevant endpoints to cellular radiation injury. We have modelled DSB damage and repair with respect to several beam delivery parameters. The complexity of the biological response caused by different ions of the same LET was found to differ due to the radiation track structure. We suggest that this is as a direct consequence of the complexity of the breaks caused, as similar trends are observed for both repair and break induction. This is of relevance for potential application to LET based treatment plans.