ESTRO 36 Abstract Book

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S905 ESTRO 36 2017
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individual clinical risk factors (PSA >20 ng/ml, or Gleason
score of 8–10, or clinical stage T3a) are not significantly
associated with distant metastasis (P-value is 0.493,
0.087, 0.109, respectively). In the multivariate forward
analysis, imaging characteristics of main tumor side wall
invasion by anatomical T2 MRI is the only significant risk
factor predicting distant metastasis (odds ratio 42.25,
confidence interval 5.1-346.5, P-value <0.001). The PET
regional tumor texture features can further divide
patients into with or without distant metastasis by using
high intensity long run emphasis value > -0.40 and low
intensity run emphasis value <0.26 (Figure 1). The
Sensitivity and specificity of the multivariate tree model
was 80% and 80%, respectively.
Conclusion
By providing excellent anatomical, functional, and
metabolic information, integrated PET/MR enhances the
staging of metastatic disease in high risk Pca. Imaging
characteristics including pelvic side wall invasion and
tumor metabolic heterogeneity may have crucial role in
patient management.
EP-1682 Comparison of SUV based on different ROIs and
VOIs definitions: a multi-center 4D phantom study
M. Lambrecht1, K. Ortega Marin1, M. La Fontaine2, J.J.
Sonke2, R. Boellaard3, M. Verheij2, C.W. Hurkmans1
1 Catharina Ziekenhuis, Physics/Radiotherapy, Eindhoven,
The Netherlands
2 Netherlands cancer institute, Radiotherapy,
Amsterdam, The Netherlands
3 University medical center- university of Groningen,
Nuclear medicine, Groningen, The Netherlands
Purpose or Objective
In the context of the EORTC LungTech trial, a QA
procedure including a PET/CT credentialing has been
developed. This procedure will ultimately allow us to pool
data from 23 institutions with the overall goal of
investigating the impact of tumour motion on
quantification. As no standardised procedure exists under
respiratory conditions, we investigated the variability of
14 SUV metrics to assess their robustness over respiratory
noise.
Material and Methods
The customized CIRS-008A phantom was scanned at 13
institutions. This phantom consists of a 18 cm long body,
a rod attached to a motion actuator, and a sphere of
either 1.5 or 2.5cm diameters. Body, rods and spheres
were filled with homogeneous 18FDG solutions
representative of activity concentrations in mediastinum,
lung and tumour for a 70kg patient. Three respiratory
patterns with peak-to-peak amplitudes and periods of
15mm/3sec, 15mm/6sec and 25mm/4sec were tested.
Prior to scanning in respiratory condition, a 3D static
PET/CT was acquired as reference. During motion, images
were acquired using 3D or 4D gated PET(average image)
according to institutional settings. 14 SUV(mean) metrics
were obtained per acquisition varying VOI/ ROI shape and
location. Three ROIs and three VOIs with respective radii
of 0.5, 0.6 and 0.8cm were investigated. These ROIs/VOIs
were first centred on the maximum activity voxel; a
second analysis was made changing the location from the
voxel to the region (ROI5voxels) or the volume
(VOI7voxels) with the maximum value. Two additional
VOIs were defined as 3D isocontours respectively at 70%
and 50% of the maximum voxel value. The SUV metrics
were normalized by the corresponding 3D static SUV.
Converting to recovery coefficients (RC) allowed us to pool
data from all institutions, while maintaining focus solely
on motion. For each RC from each motion setting we
calculated the mean over institutions, we then looked at
the standard deviation (Sd) and spread of each averaged
RC over each motion setting.
Results
For the institutions visited we found that RCVOI70% and
RCVOI50%, yielded over the 14 metrics the lowest
variability to motion with Sd of 0.04 and 0.03 respectively.
The RCs based on ROIs/VOIs centered on a single voxel
were less impacted by motion (Sd: 0.08) compared to
region RCs (Sd: 0.14). The averaged Sd over the RCs based
on VOIs and ROIs was 0.12 and 0.11 respectively.
Conclusion
Quantification over breathing types depends on ROI/VOI
definition. Variables based on SUV max thresholds were
found the most robust against respiratory noise.
EP-1683 Fractals in Radiomics: implementation of new
features based on fractal analysis
D. Cusumano1, N. Dinapoli2, R. Gatta2, C. Masciocchi2, J.
Lenkowicz2, G. Chilorio2, L. Azario1, J. Van Soest3, A.
Dekker3, P. Lambin3, M. De Spirito4, V. Valentini5
1Fondazione Policlinico Universitario A.Gemelli, Unità
Complessa di Fisica Sanitaria, Roma, Italy
2Fondazione Policlinico Universitario A.Gemelli,
Divisione di Radioterapia Oncologica- Gemelli ART,
Roma, Italy
3Maastricht University Medical Center, Department of
Radiation Oncology, Maastricht, The Netherlands
4Università Cattolica del Sacro Cuore, Istituto di Fisica,
Roma, Italy
5Università Cattolica del Sacro Cuore, Department of
Radiotherapy - Gemelli ART, Roma, Italy
Purpose or Objective
A fractal object is characterized by a repeating pattern
that it displays at different size scales: this property,
known as self-similarity, is typical of many structures in
nature or inside human body (a snow flake and the neural
networks are just some examples).
The fractal self-similarity can be measured by Fractal
Dimension (FD), a parameter able to quantify the
geometric complexity of the object under analysis.
Aim of this study is to introduce in Radiomics new features
based on fractal analysis, in order to obtain new indicators
able to detect tumor spatial heterogeneity. These fractal
features have been used to develop a predictive model
able to calculate the probability of pathological complete
response (pCR) after neoadjuvant chemo-radiotherapy for
patients affected by locally advanced rectal cancer
(LARC). Material and Methods
An home-made R software was developed to calculate the
FD of the Gross Tumor Volume (GTV) of 173 patients
affected by LARC. The software, validated by comparing