ESTRO 36 Abstract Book

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S947 ESTRO 36
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Conclusion
Our results suggested that Body6x1 might provide better
SNR and image uniformity for T2-weighted abdominal MR-
simulation scan than other two settings.
EP-1725 Predicting radiation-induced pneumonitis in
NSCLC: a radiobiological and texture analysis study
W. Nailon1, W. Lu2, D. Montgomery1, L. Carruthers1, J.
Murchiston3, A.W. Yong3, G. Ritchie3, T. Evans4, F.
Little4, S.C. Erridge4, A. Price4, D.B. McLaren4, S.
Campbell4
1 Edinburgh Cancer Centre Western General Hospital,
Department of Oncology Physics, Edinburgh, United
Kingdom
2 School of Engineering University of Edinburgh, Institute
of Digital Communications, Edinburgh, United Kingdom
3 Royal Infirmary of Edinburgh, Department of Radiology,
Edinburgh, United Kingdom
4 Edinburgh Cancer Centre Western General Hospital,
Department of Clinical Oncology, Edinburgh, United
Kingdom
Purpose or Objective
In patients with inoperable non-small cell lung cancer
(NSCLC) reliably estimating susceptibility to radiation-
induced pneumonitis is challenging. Typically dose-volume
histogram (DVH) parameters, normal tissue complication
probability (NTCP) and changes in lung density are used,
however, there is still considerable uncertainty in
predicting individual patient susceptibility. The aim of this
work was to investigate the presence of patient-specific
density patterns that predict the likelihood of pneumonitis
based on image analysis of radiotherapy planning CT
images. The predictive image analysis measures were
compared to NTCP models that predict the risk of
pneumonitis.
Material and Methods
A cohort of 9 NSCLC patients made up of 4 patients (ID 71
to 74) that developed pneumonitis after radiotherapy and
5 patients that remained asymptomatic after radiotherapy
(ID 10 to 14) was selected. Radiotherapy planning CT
images were acquired at 3 mm slice thickness with a pixel
resolution of 1 mm. 6 patients were treated with 55 Gy in
20 fractions and 3 patients with 60 Gy in 30 fractions.
Treatment plans were produced on Eclipse using a pencil
beam convolution dose calculation algorithm. 7
radiobiological models were used to calculate NTCP on the
whole, right and left lungs. Image texture analysis was
used to calculate 99 unique features on 32x32 and 20x20
pixel 2 subimages within the whole lung volume. Redundant
texture features were removed and a neural network (NN)
trained to classify the results.
Results
The predicted NTCP values are shown in Figure 1 for the
analysis of the whole lung volume (normal lung tissue
excluding the GTV). Similar results were obtained for the
right and left lungs. Although model 5, symptomatic or
radiographic pneumonitis <=6 months, showed high NTCP
values this was not specific to patients with confirmed
pneumonitis. Similar values were obtained in patients
showing no signs of pneumonitis. The image texture
analysis results identified the risk of pneumonitis most
notably in the right lung (87.49%).
Figure 1: NTCP results on the 9 patients (ID 10-14
asymptomatic, 71-74 symptomatic).
Table 1 : Texture analysis classification on the whole lung
and right and left lung volumes.
Conclusion
These preliminary results show that it is possible to predict
radiation-induced pneumonitis, both prior to treatment
and independently of dosimetric evaluation, using image
texture analysis of the radiotherapy planning CT images.
However further validation on a larger patient cohort is
required.
EP-1726 Efficacy of vendor supplied distortion
correction algorithms for a variety of MRI scanners
E.P. Pappas1, I. Seimenis 2, D. Dellios2, A. Moutsatsos1, E.
Georgiou1, P. Karaiskos1
1National and Kapodistrian University of Athens, Medical
Physics Laboratory - Medical School, Athens, Greece
2Democritus University of Thrace, Medical Physics
Laboratory - Medical School, Alexandroupolis, Greece
Purpose or Objective
Although inherently distorted, Magnetic Resonance Images
(MRIs) are being increasingly used in stereotactic
radiosurgery (SRS) treatment planning in order to take
advantage of the superior soft tissue contrast they exhibit.
MR scanner manufacturers have equipped their units with
distortion correction algorithms to mainly compensate for
gradient nonlinearity induced spatial inaccuracies. The
purpose of this study is to assess the accuracy of these
algorithms by comparing distortion maps deduced with
and without the optional distortion correction schemes
enabled for a variety of MRI scanners.
Material and Methods
A custom acrylic-based phantom was designed and
constructed in-house. Its external dimensions were limited
to approximately 17x16x16 cm3 in order to accurately fit
in a typical head coil while extending to the edges of the
available space. On eleven axial planes, a total of 1978
holes were drilled, the centers of which serve as control
points (CPs) for distortion detection. Center-to-center CP
distance is 10 mm on x and y axis and 14 mm on z axis,
resulting in adequately high CP density. The phantom was
filled with copper sulfate solution and MR scanned at 1.5T
(SIEMENS Avanto, Philips Achieva) and 3.0T (SIEMENS
Skyra) using the corresponding standard clinical MR
protocol for SRS treatment planning. All scans were