ESTRO 36 Abstract Book

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S32 ESTRO 36 2017
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This study is based on 4D-CBCT and 4D-CT scans of 19 non-
small-cell lung cancer (NSCLC) patients subjected to
curatively intended radiotherapy initiated between March
2012 and August 2014. Lung ventilation was measured as
voxel-wise Jacobian determinants (JD) computed by
deformable image registration between expiration phases
and inspiration phases. All image registrations were
carried out by the freeware tool elastix .
4D-CT scans acquired before treatment were chosen as
gold standard for ventilation. The clinical 4D-CBCT
projection images of the first treatment fraction were
improved by the procedure described in [PMB, 15, 5781,
2016], which corrected projections for image lag, detector
scatter, body scatter, and beam hardening. Clinical and
improved projection images were binned and FDK-
reconstructed by software in the RTK-package
). All CBCT reconstructions were rigidly
resampled into CT-space. Before deformable image
registration a 1x1x1cm wide median filter was applied on
all images. For each patient the clinical 4D-CBCT JDs and
the improved 4D-CBCT JDs were voxel-wise compared to
4D-CT JDs by Spearman correlation and the resulting
correlation coefficients were analysed by a paired t-test.
Results
The clinical projection images were improved successfully
and both versions of projection images were reconstructed
to 4D-CBCT. Deformable registrations were carried out on
clinical 4D-CBCT, improved 4D-CBCT, and 4D-CT. The
sample mean for correlations between clinical 4D-CBCT
and 4D-CT JDs was 0.297±0.154 while the sample mean for
clinical 4D-CBCT and 4D-CT JDs was 0.362±0.106 (±1 SD).
A paired t-test of the Fisher transform correlation values
showed the difference to be statistically significant
(p=0.047).
Conclusion
Ventilation computed from clinical 4D-CBCT and improved
4D-CBCT were compared to 4D-CT ventilation. The
improved 4D-CBCT ventilation correlated better with
4DCT ventilation, suggesting that the improved 4D-CBCT
are superior to clinical 4D-CBCT for measuring lung
ventilation. Further improvements on measuring lung
ventilation from 4D-CBCT may possibly be achieved by
adding iterative reconstruction.
OC-0067 4DCT imaging to assess radiomics feature
stability: an investigation for thoracic cancer
R.T.H.M. Larue 1, L. Van De Voorde1, J.E. Van Timmeren1,
R.T.H. Leijenaar1, M. Berbée1, M.N. Sosef2, W.M.J.
Schreurs3, W. Van Empt1, P. Lambin1
1 Maastricht University Medical Centre - GROW-School for
Oncology and Developmental Biology, Department of
Radiation Oncology - MAASTRO, Maastricht, The
Netherlands
2 Zuyderland Medical Centre, Department of Surgery,
Heerlen, The Netherlands
3Zuyderland Medical Centre, Department of Nuclear
Medicine, Heerlen, The Netherlands
Purpose or Objective
For several tumour sites it was shown that quantitative
radiomics features, derived from CT images, unravel
valuable prognostic information. However, the large
number of available features increases the risk of
overfitting. Typically, test-retest scans allow to reduce
this number by selecting a robust feature subset.
However, these test-retest scans are not available for all
tumour sites. Hence we hypothesized that different
phases of respiratory-correlated 4D CT-scans (4DCT) can
be used as alternative to test-retest imaging to select
robust features. To test this hypothesis, we assessed the
repeatability of 542 radiomics features in a test-retest and
two 4DCT datasets of lung and oesophageal cancer
patients.
Material and Methods
The publically available RIDER dataset, consisting of test-
retest CT-scans of 27 non-small cell lung cancer (NSCLC)
patients, and 4DCT-scans of 22 NSCLC (4D-Lung) and 20
oesophageal cancer patients (4D-OES) were analysed. The
4DCT-scans contained 8 phases of the breathing cycle. The
gross tumour volume (GTV) of the primary tumours were
manually delineated. In total, 70 radiomics features
describing the tumour shape, intensity and texture, and
472 wavelet-filtered features were calculated within the
GTVs. A concordance correlation coefficient (CCC) ≥ 0.85
was used to identify robust features, either between the
test-retest scans or over all phase pairs in the 4DCT scans.
Results
Unfiltered features in general showed a higher robustness
than wavelet-filtered features. In total 34/70 (49%)
unfiltered features and 122/472 (26%) wavelet features
were stable in both the test-retest dataset and the 4D-
lung dataset. The four features selected previously to be
prognostic in lung and head-and-neck cancer (Aerts et al
2014), had a minimum CCC > 0.95 in both datasets. In the
4D-OES dataset 205/542 (38%) features showed a high
robustness, of which 42 unfiltered and 99 wavelet-filtered
features were also stable in the 4D-lung dataset. Due to
the fact that the image acquisition settings and hardware
were exactly the same in the 4D-lung and 4D-OES scans,
this partial disconcordance suggests that the remaining
stable features might be tumour site specific.
Figure 1. Venn chart visualizing the overlap of stable
features with CCC>0.85 in the RIDER, 4D-lung and 4D-OES
dataset.