ESTRO 36 Abstract Book

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S866 ESTRO 36
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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.
EP-1605 Deep learning of radiomics features for
survival prediction in NSCLC and Head and Neck
carcinoma
A. Jochems1, F. Hoebers1, D. De Ruysscher1, R.
Leijenaar1 , S. Walsh1, B. O'Sullivan2, J. Bussink 3, R.
Monshouwer3, R. Leemans4, P. Lambin1
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 quality1. 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 model1.
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).
Figure 1: ROC curves of the model validation.
Conclusion
The combination of deep learning and radiomics features
has similar performance to conventional radiomics
modelling strategies. However, feature selection is no
longer a required component as all features can be
included in the network. This is a major advantage as
feature selection is a computationally intractable task for
which only heuristic solutions exist.
References
1 Aerts, H. et al, Nat. Commun. 2014, 5, 4006.
EP-1606 Calculating ion-induced cell death and
chromosome damage by the BIANCA biophysical model
M.P. Carante1,2, F. Ballarini 1,2
1Istituto Nazionale di Fisica Nucleare INFN, Section of
Pavia, Pavia, Italy
2University of Pavia, Physics Department, Pavia, Italy
Purpose or Objective
To calculate probabilities of cell death and chromosome
aberrations following cell irradiation with ion beams of
different energy.
Material and Methods
A biophysical model called BIANCA (BIophysical ANalysis of
Cell death and chromosome Aberrations) [Carante M.P.
and Ballarini F. Front. Oncol. 6:76 2016] was refined and
applied to simulate cell death and chromosome
aberrations by therapeutic protons and heavier ions. The
model, which assumes a pivotal role for DNA cluster
damage, is based on the following assumptions: i) a DNA
“Cluster Lesion” (CL) produces two independent
chromosome fragments; ii) chromosome fragment un-
rejoining, or distance-dependent mis-rejoining , gives rise
to chromosome aberrations; iii) certain aberrations
(dicentrics, rings and large deletions) lead to cell death.
The CL yield is an adjustable parameter, as well as the
probability that a chromosome fragment remains un-
rejoined even if possible partners for rejoining are
present. The model, implemented as a MC code providing
simulated dose-response curves comparable with
experimental data, was applied to different beams,
including beams available at the CNAO hadrontherapy
centre in Pavia, Italy, and at the CATANA facility in
Catania, Italy.
Results
The model allowed reproduction of experimental survival
curves for cell lines characterized by different
radiosensitivity, supporting the model assumptions.
Furthermore, cell death and chromosome aberrations