939 / 1096
S923
ESTRO 36
_______________________________________________________________________________________________
Fig 2.
ADC in prostate and dose painted prostate for
C
im
scenario.
Conclusion
Gleason driven dose painting for prostate cancer using
ADC-MRI is feasible to reduce the average dose. The
reduction in dose is strongly dependent on the minimum
dose assigned to voxels with
G
<6.
EP-1690 Validating the robustness of PET features in a
phantom in a multicenter setting
T. Konert
1
, M. La Fontaine
2
, S. Van Kranen
2
, W. Vogel
1
, J.
Van de Kamer
2
, J.J. Sonke
2
1
Netherlands Cancer Institute Antoni van Leeuwenhoek
Hospital, Nuclear Medicine, Amsterdam, The
Netherlands
2
Netherlands Cancer Institute Antoni van Leeuwenhoek
Hospital, Radiation Oncology, Amsterdam, The
Netherlands
Purpose or Objective
PET features may have prognostic or predictive value and
could therefore assist treatment decisions. However, PET
features are sensitive to differences in data collection,
reconstruction settings, and image analysis. It is
insufficiently known which features are least affected by
these differences, especially in a multicenter setting.
Therefore, this study investigates the robustness of PET
features in a phantom after repeated measurements
(repeatability), due to varying scanner type
(reproducibility) and their dependence on binning method
and SUV activity.
Material and Methods
PET scans from a NEMA image quality phantom were used
for assessment of PET feature robustness. Scans were
acquired on a Philips, a Siemens and a GE scanner from
three medical centers (see figure 1 and table 1 for more
details). Per sphere, a VOI was created by applying a
threshold of 40% of the SUV
max
. Per VOI, 10 first order
statistics and 10 textural features, often reported in
literature, were extracted. Two common implementations
of image pre-processing, before feature extraction, were
compared: using a fixed bin size (SUV = 1) versus a number
of fixed bins (64 bins). To examine the feature
repeatability, measurements were repeated two or three
times on the same scanner. The reproducibility was
assessed in images by comparing all scanners. The degree
of variation was calculated per VOI with the coefficient of
repeatability (1.96 x SD/mean), normalized to a
percentage (CR
%
). Features were seen as robust with a CR
< 30%, matching the level of uncertainty found in response
of PERCIST criteria. Wilcoxon signed rank tests were used
to estimate the significance of differences due to binning
method and p-values ≤ 0.05 were considered significant.
Results
For an overview of the results, see Table 1. The CR
%
of
SUV
max
in all scans depended on sphere volume, and
ranged from 1.1% (largest sphere) to 15.2% (smallest
sphere). In the repeatability study, 9 out of 10 PET
features were robust with 64 bins in more than one
scanner, and significantly higher (p < 0.05) when
compared to using a fixed bin size, where 7 out of 10 PET
features were robust. Reproducibility was achieved in 3
out of 10 PET features when 64 bins were used. PET
features were not reproducible when using a fixed bin
size.
Dissimilarity (CR
%
: 6.3-24.9), homogeneity 1 (CR
%
:
16.9-22.5), and inertia (CR
%
: 10.2-22.5) were robust to
binning method, scanner type, and SUV activity.
Coarseness, contrast, busyness, energy, correlation were
not robust (CR
%
> 30%).
Conclusion
This study indicates that not all PET features are robust in
a multicenter setting. Care has to be taken in feature
selection and binning method, especially if harmonization
of methods across centers is not accomplished.
Dissimilarity, homogeneity 1, and inertia seem robust and
promising PET features for use in a multicenter setting.
Use of fixed bin size should be avoided.
EP-1691 Multi-modal voxel-based correlation between
DCE-CT/MRI and DWI in metastatic brain cancer
C. Coolens
1,2,3
, W. Foltz
1,4
, N. Sinno
1
, C. Wang
1
, B.
Driscoll
1
, C. Chung
2,5
1
Princess Margaret Cancer Centre and University Health
Network, Radiation Medicine Program, Toronto, Canada
2
University Health Network, TECHNA Institute, Toronto,
Canada
3
University of Toronto, Radiation Oncology and IBBME,
Toronto, Canada
4
University of Toronto, Radiation Oncology, Toronto,
Canada
5
MD Anderson Cancer Center, Radiation Oncology,
Houston, USA
Purpose or Objective
Quantitative model-based measures of dynamic contrast
enhanced (DCE) and Diffusion Weighted (DW) MRI
parameters have shown variable findings to-date that may
reflect variability in the MR acquisition and analysis. This
work investigates the use of a voxel-based, multi-modality
GPU architecture to include various complimentary solute
transport processes into a common framework and