182 / 1096
S169
ESTRO 36
_______________________________________________________________________________________________
Time Points
Mean(SD) of change in GTV
expressed as percentage
Baseline and different
fractions
Fractions 0 and 5
-11.5%(7.8)
Fractions 0 and 10
-52.0%(4.6)
Fractions 0 and 15
-57.0%(6.0)
Fractions 0 and 20
-64.0%(9.0)
Fractions 0 and last
fraction
-66.0%(.0)
Baseline
different
fractions
Fractions 0 and 5
-11.5%(7.8)
Fractions 5 and 10
-48.0%(3.9)
Fractions 10 and 15 -14.5%(5.5)
Fractions 15 and 20 -16.0%(12.2)
Fractions 20 and the
last fraction
-6.0%(5.9)
Conclusion
Real-time MRI-guided radiation provides previously
unavailable data on tumor response during neoadjuvant
chemoradiation. In this study, the most significant
volumetric change in the GTV was observed earlier than
expected, between fractions 5 and 10. Correlation of early
volumetric response changes with clinical and or
pathological outcomes may prove highly valuable. Daily
MRI during radiation provides a unique opportunity to
tailor individual treatment based on early response to
chemoradiation, and suggests that functional imaging
correlates are likely best undertaken early during
chemoradiation. Additional patients are being recruited
into this study to correlate imaging response with clinical
and pathological outcomes.
PV-0323 Development of a prognostic model
incorporating PET texture analysis in oesophageal
cancer patients
K. Foley
1
, R. Hills
1
, B. Berthon
2
, C. Marshall
2
, W. Lewis
3
,
T. Crosby
4
, E. Spezi
5
, A. Roberts
6
1
Cardiff University, Division of Cancer & Genetics,
Cardiff, United Kingdom
2
Cardiff University, Wales Research & Diagnostic PET
Imaging Centre, Cardiff, United Kingdom
3
University Hospital of Wales, Upper GI Surgery, Cardiff,
United Kingdom
4
Velindre Cancer Centre, Oncology, Cardiff, United
Kingdom
5
Cardiff University, School of Engineering, Cardiff,
United Kingdom
6
University Hospital of Wales, Clinical Radiology,
Cardiff, United Kingdom
Purpose or Objective
Texture analysis provides additional quantitative data
extracted from radiological staging investigations. This
exploratory study investigates the prognostic significance
of PET texture variables when incorporated into a model
predicting overall survival (OS) in patients with
oesophageal cancer (OC).
Material and Methods
This retrospective cohort study includes consecutive OC
patients staged with PET/CT between October 2010 and
December 2014. PET-defined tumour variables and
texture metrics were obtained using ATLAAS, a machine
learning algorithm for optimised automatic segmentation.
A Cox regression model including age, radiological stage,
treatment and 12 novel texture variables was developed
and a prognostic score stratifying patients into quartiles
was calculated. Primary outcome was OS and a p-value
<0.1 was considered statistically significant.
Results
Three hundred and forty-three consecutive patients
[median age=67 (range=24-83), adenocarcinoma=258]
were included. Median survival was 17 months (95% CI
14.685–19.315). Multivariate analysis demonstrated 7
variables that were significantly and independently
associated with OS; age [HR=1.024 (95% CI 1.010-1.038),
p<0.001], radiological stage [HR=1.492 (1.221-1.823),
p<0.001], treatment [HR=2.855 (2.038–3.998), p<0.001],
standard deviation [HR=0.898 (0.815–0.989), p=0.029],
log(coarseness) [HR=1.774 (0.918–3.43), p=0.088],
dissimilarity [HR=1.136 (1.007–1.281), p=0.038] and zone
percentage [HR=0.938 (0.897–0.980), p=0.005].
A
prognostic score derived from the model equation showed
significant differences in OS between quartiles (X
2
=90.13,
df=3, p<0.001).
Conclusion
This study demonstrates the additional benefit of PET
texture analysis in OC staging, over and above the current
TNM system. Our prognostic model requires further
validation, but highlights the potential of texture analysis
to predict survival in OC.
PV-0324 FDG-PET based pelvic bone marrow dose
predicts for blood cell nadirs in CT-RT for anal cancer
P. Franco
1
, F. Arcadipane
1
, R. Ragona
1
, A. Lesca
2
, E.
Gallio
3
, M. Mistrangelo
4
, P. Cassoni
5
, M. Baccega
2
, P.
Racca
6
, R. Faletti
7
, N. Rondi
8
, M. Morino
4
, U. Ricardi
1
1
University of Turin A.O.U. Citta' della Salute e della
Scienza, Department of Oncology- Radiation Oncology,
Torino, Italy
2
A.O.U. Citta' della Salute e della Scienza- Turin,
Department of Surgical Sciences - Nuclear Medicine,
Torino, Italy
3
A.O.U. Citta' della Salute e della Scienza- Turin,
Department of Medical Imaging - Medical Physics,
Torino, Italy
4
University of Turin A.O.U. Citta' della Salute e della
Scienza, Department of Surgical Sciences, Torino, Italy
5
University of Turin A.O.U. Citta' della Salute e della