884 / 1096
S868
ESTRO 36
_______________________________________________________________________________________________
Conclusion
Varying the RBE depending on end-point may strongly
influence results when estimating carcinogenic risks from
C-ion therapy and should be included in modelling risk of
radiation-induced SC from C-ion therapy.
EP-1608 Deriving HPV status from standard CT imaging:
a radiomic approach with independent validation
R. Leijenaar
1
, M. Nesteruk
2
, G. Feliciani
1
, F. Hoebers
1
, J.
Van Timmeren
1
, W. Van Elmpt
1
, S. Walsh
1
, A. Jochems
1
,
S. Huang
3
, B. Chan
3
, J. Waldron
3
, B. O'Sullivan
3
, D.
Rietveld
4
, C. Leemans
5
, O. Riesterer
2
, K. Ikenberg
6
, P.
Lambin
1
1
MAASTRO Clinic, Department of Radiation Oncology-
GROW- School for Oncology and Developmental Biology-
Maastricht University Medical Centre, Maastricht, The
Netherlands
2
University Hospital Zurich and University of Zurich,
Department of Radiation Oncology, Zurich, Switzerland
3
Princess Margaret Cancer Center, Department of
Radiation Oncology- University of Toronto, Toronto,
Canada
4
VU University Medical Center, Department of Radiation
Oncology, Amsterdam, The Netherlands
5
VU University Medical Center, Department of
Otolaryngology/Head and Neck Surgery, Amsterdam, The
Netherlands
6
University Hospital Zurich and University of Zurich,
Department of Pathology and Molecular Pathology,
Zurich, Switzerland
Purpose or Objective
Oropharyngeal squamous cell carcinoma (OPSCC) is one of
the fastest growing disease sites of head and neck cancers.
HPV positive cancers have been shown to have better
tumor control with radiotherapy and increased survival,
which makes them interesting for de-escalation protocols.
HPV is routinely tested using in situ hybridization for viral
DNA, or immunohistochemistry for p16. However, an
established, non-invasive, imaging biomarker of HPV
status currently does not exist. Radiomics–the high-
throughput extraction of large amounts of quantitative
features from medical images–has already been shown to
be of prognostic value for head and neck cancer. In this
study we evaluate the use of a Radiomic approach to
identify the HPV status of OPSCC patients.
Material and Methods
Three independent cohorts, with a total of 793 OPSCC
patients were collected: C1 (N=543), C2 (N=159) and C3
(N=100). HPV status was determined by p16 and available
for 686 patients. Patients underwent pre-treatment CT
imaging and the tumor volume was manually delineated
for treatment planning purposes. Images were visually
assessed for the presence of CT artifacts (e.g. streak
artifacts due to dental fillings) within the GTV, in which
case they were excluded from further analysis. In total,
1378 Radiomic features were extracted, comprising: a)
first-order statistics, b) shape, and c) (multiscale) texture
(Laplacian of Gaussian and Wavelet). The model was
learned on the C1 cohort and validated on the remaining
cohorts. The Radiomic feature space was first reduced by
selecting cluster medoids after hierarchical cluster
analysis using correlation (ρ>0.9) as a distance measure.
Multivariable logistic regression was performed using least
absolute shrinkage and selection operator (LASSO) model
selection (200 times 10-fold cross-validated). The area
under the receiver operator curve was used to assess out-
of-sample model performance in predicting HPV status.
Results
Out of the patients with known HPV scoring, we identified
337 (49%) patients without visible CT artifacts: C1
(N=206), C2 (N=88), C3 (N=43), of which 132, 20, and 18
were HPV positive, respectively. The modeling process
resulted in a multivariable prediction model, with an AUC
of 0.85. External validation in the C2 and C3 cohorts
showed an AUC of 0.6 and 0.72, respectively. The receiver
operator curves for training and validation are shown in
Figure
1.