ESTRO 36 Abstract Book

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S907 ESTRO 36 2017
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The optimal window for assessing the responsiveness to
treatment based on αeff calculations derived from
repeated FDG PET scans in NSCLC patients appears to be
the second week of the treatment but validation on a
larger cohort of patients is warranted.
[1] Toma-Dasu I, Uhrdin J, Lazzeroni M, Carvalho S, van
Elmpt W, Lambin P, Dasu A. Evaluating tumor response of
non-small cell lung cancer patients with 18F-
fludeoxyglucose positron emission tomography: potential
for treatment individualization. Int J Radiat Oncol Biol
Phys. 2015 1;91(2):376-84.
EP-1685 CT-Radiomics outperforms FMISO-PET/CT for
the prediction of local control in head-and-neck cancer
J.A. Socarras Fernandez1, D. Mönnich 1, F. Lippert 1, D.
Welz 2, C. Pfannenberg3, C. La Fougere4, G. Reischl5, D.
Zips 2, D. Thorwarth1
1 University Hospital Tübingen, Radiation Oncology -
Section for Biomedical Physics, Tübingen, Germany
2 University Hospital Tübingen, Radiation Oncology,
Tübingen, Germany
3 University Hospital Tübingen, Diagnostic and
Interventional Radiology, Tübingen, Germany
4 University Hospital Tübingen, Radiology - Section of
Nuclear Medicine, Tübingen, Germany
5 University Hospital Tübingen, Radiology - Section of
Radiopharmacy, Tübingen, Germany
Purpose or Objective
FMISO-PET has proven to capture probabilities of hypoxia
in tumors, which may predict risks of local recurrence
across patients. On the other hand, Radiomics
hypothesizes that heterogeneity of tumors can be
extracted from medical images. In this study, we
investigate the performance of CT-radiomics features and
FMISO PET/CT for prediction of local recurrence in head
and neck cancer (HNC) patients.
Material and Methods
A cohort of 22 HNC patients who underwent FMISO PET/CT
before primary Radiotherapy (RT) treatment was used.
Planning CT scans as well as FMISO PET/CT were acquired
prior to RT, FMISO PET data was analysed using maximum
tumour-to-muscle ratios (TMR max) 4h post injection. 92
Robust radiomics features including intensity-based as
well as texture features were extracted from the planning
CT images in the gross tumour volume (GTV). Six highly
significant radiomics features were selected from a simple
filter method based on cumulative distribution function
(CDF) in a univariate fashion in addition to a logistic
regression classification model (LoG) to build a predictive
model. Area under the curve of the receiver operating
characteristic curve (AUC-ROC) was computed for TMR max
and the model including the six selected radiomics
Features. Finally, a combined model using FMISO TMR max
and two radiomics features (one from texture and one
from intesity) were constructed.
Results
Each of the selected six radiomics features (1 texture and
5 first order statistics), which were normalized to be
comparable, showed higher predictive power compared to
FMISO TMRmax at the moment of predicting outcomes
univariately. AUC-ROC curves demonstrated that a model
created out of only six dominant CT-radiomics features
can discriminate groups better with respect to local
control in HNC using the logistic regression models (AUC =
0.904) than FMISO TMRmax (AUC = 0.800). Nevertheless, a
combination of FMISO-PET TMR max values and only two CT-
radiomics features (Small Zone Emphasis texture and
Minimum Grey Level first-order statistics) can reach an
AUC of 0.886 in our classification model.
Conclusion
CT radiomics proved to have better prognostic power with
respect to local control in HNC than FMISO-PET TMRmax.
Nonetheless, a combination of TMR max and the two most
significant features of CT radiomics reaches high
prognostic power with fewer features to assess.
Consequently, analysing tumour heterogeneity using CT
radiomics features may have the power to determine
substitute measures of tumour hypoxia and might
therefore be used as a basis for personalized RT
adaptations in the future.
EP-1686 Diffusion weighted imaging for treatment
response prediction in advanced rectal cancer
H.D. Nissen 1
1Nissen Henrik D., Department of Oncology - Section for
Radiotherapy, Vejle, Denmark
Purpose or Objective
The standard treatment of locally advanced distal rectal
cancer is chemoradiotherapy (CRT) followed by surgery.
Based on pathologic examination of the surgery specimen,
a significant number of patients are found to be without
remaining tumor at the time of surgery. This has led to an
increasing interest in whether, for a select group of
patients, surgery can be replaced by a wait-and-see
strategy. Several recent studies [1, 2] have shown that this
is possible without compromising survival and with
significantly reduced comorbidities. A significant
challenge in this strategy is selecting the patients who are
candidates for this strategy.
We wish to examine whether diffusion weighted MRI (DWI)
can be used as an early biomarker for tumor response to
CRT.
Material and Methods
Here we present data from 25 patients treated for distal
T3 or T4 rectal tumors. Patients were treated with long
course CRT, including a brachytherapy boost to the tumor,
followed by surgery. Patients were DWI scanned before
start of CRT and again after 2 weeks of CRT. The DWI
sequence included 11 b-values from 0 to 1100. Regions of
interest (ROI) were drawn using an algorithm to locate