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radiomics, i.e. the change of radiomic features over time,
has not yet been extensively explored. Cone-beam CT
(CBCT) imaging can be performed daily for lung cancer
patients and is therefore a potential candidate for delta
radiomics, which may allow further treatment
individualization. In this study we explored delta
radiomics using CBCT imaging by investigating the number
of features changing at a specific time point during
treatment. Moreover, we investigated the differences
between patients having an overall survival of less or more
than 2 years.
Material and Methods
A total of 40 stage II-IV NSCLC patients, receiving
curatively intended radiotherapy for a period of at least
six weeks, were included in the study. The CBCT images
used in this study were 1) CBCT prior to the first fraction
of treatment (CBCTfx1), 2) CBCT prior the second fraction
of treatment (CBCTfx2), 3) CBCT one week after the start
of treatment (CBCTweek2), 4) CBCT three weeks after the
start of treatment (CBCTweek4) and 5) CBCT five weeks
after the start of treatment (CBCTweek6). For 38 patients
CBCTfx1 and CBCTfx2 were available, whereas for 33
patients all weekly CBCTs were available. All patients had
a minimal follow-up of 2 years. Per time point, a total of
1046 radiomic features were derived from the primary
tumor volume. The images prior to the first and second
fraction were used to calculate the variability in imaging
features using the coefficient of repeatability (COR),
defined as 1.96*SD. The weekly images were used to
investigate the number of features changing more than
the COR with respect to baseline (CBCTfx1).
Results
Figure 1 represents the total number of features that
changed more than the COR, ranging from 0 to 999
features. The median number of features that changed for
the group with overall survival <2 years was 279, whereas
this was 500 for the group with overall survival >2 years
(Mann-Whitney U test, p = 0.06). For 8 out of 10 patients
that survived >2 years, more features (31.7%) changed one
week after CBCTfx1 than for 13 out of 23 patients that did
not survive two years.
Conclusion
This study shows that a large proportion of the radiomic
features derived from cone-beam CT images change
significantly during the course of treatment, meaning that
an interval of about two weeks is feasible for a radiomics
study using CBCT imaging. The larger number of features
that changed in the group with overall survival >2 years
could reflect an early response of the tumor to the
treatment. In future research, the prognostic value of
changing radiomic features (delta radiomics) should be
explored in a larger cohort.
EP-1601 Do higher CT pixel values outside the GTV
predict for poorer lung cancer survival?
M. Van Herk
1
, J. Kennedy
2
, E. Vasquez Osorio
1
, C. Faivre-
Finn
1
, A. McWilliam
1
1
University of Manchester, Division of Molecular and
Clinical Cancer Sciences- Faculty of Biology- Medicine
and Health, Manchester, United Kingdom
2
The Christie NHS Foundation Trust, Department of
Infomatics, Mancehster, United Kingdom
Purpose or Objective
Radiomics aims to extract features from medical images
that are prognostic for outcome and may help optimize
treatment. As far as the tumour is concerned, most work
has focused on pixel values
inside
the gross tumour volume
(GTV). The aim of this work is to develop a generic
methodology to also sample pixels
outside
a tumour
volume, assuming that these may carry information about
microscopic tumour spread and therefore might predict
outcome.
Material and Methods
We analysed data from a cohort of 1101 non-small cell lung
cancer patients treated with IMRT to 55 Gy in 20 fractions.
To evaluate the CT pixel values at various distances inside
and outside the GTV, we calculated a signed distance
transform of the GTV, which was subsequently used to
efficiently collect cross-histograms of the CT density
versus distance from the GTV edge. Based on these cross-
histograms various pixel statistics were determined as
function of the GTV distance, here we report only on the
mean pixel value, giving a curve of mean CT value versus
distance. The mean of these curves was calculated for
patients that were alive (652) and dead (449) at 12 months
after start of therapy, censored for follow-up. Significance
of the difference was tested by permuting the dead/alive
labels 1000 times to create mock differences and counting
how often the true difference exceeded the mock
difference. Significant regions were defined and the mean
pixel value from those regions used as variable in a cox
proportional hazard model, splitting the patients on the
median of the mean region density, while correcting for
age and tumour size. As the outside of the tumour can also
include chest wall and mediastinum, we repeated the
analysis only analysing pixels inside the lungs.
Results
There was a significant different average pixel value in the
region 0-1 cm outside the GTV for dead and alive patients
(fig. 1) that translated to a hazard ratio (HR) of 1.4, p<10
-
5
(corrected for tumour size and age), survival curves split
at median density value (fig. 2A). However when only
pixels inside the lungs were analysed, the HR reduced to
1.1, p=0.15; i.e. no longer significant (fig. 2B). This finding
indicates that the mean pixel values represent mediastinal
attachment rather than microscopic disease. Not
correcting for tumour size, both signals incorrectly predict
outcome significantly (e.g. fig. 2C for lung pixels only).