568 / 1096
S553
ESTRO 36
_______________________________________________________________________________________________
Results
MR_ST was significantly more accurate than other group
according to DSC of 0.646 ± 0.094 compared to 0.564 ±
0.102, 0.298 ± 0.109, 0.39 ± 0.254 respectively for MR_MV,
CA and BM in optic chiasm. DCS scores in pituitary gland
were following, 0.624 ± 0.055 in MR_ST, 0.582 ± 0.052 in
MR_MV, 0.514 ± 0.140 in CA and 0.28 ± 0.24 in BM
respetively. Brainstem was showed similar DSC score as
0.89 ± 0.021, 0.892 ± 0.017, 0.842 ± 0.038 and 0.73 ± 0.156
respectively for MR_ST, MR_MV, CA and BM.
Conclusion
Most of auto delineated contours was smoothened in
advance. Among 4 groups, DSC of MR based was the
highest. Even though auto contouring is conducted by
different users, it shows certain shape and included
similar region when we use same subject’s data. ABS
software takes more effort and time to use in the first
place. However, MR based ABS would have better auto
contouring accuracy compared with MBS and CT based ABS
in brain cancer. In addition STAPLE has provided better
results for smaller volumes based on my study.
PO-1003 A analysis of safety of whole brain
radiotherapy with Hippocampus avoidance in brain
metastasis
Y. Han
1
, J. Chen
1
, G. Cai
1
, X. Cheng
1
, Y. Kirova
2
, W. Chai
3
1
Shanghai Jiao Tong university-ruijin hospital, radiaton
oncology, Shanghai, China
2
Institute Curie- Paris- France, Department of Radiation
Oncology, Paris, France
3
Shanghai Jiao Tong university-ruijin hospital,
Department of Radiology, Shanghai, China
Purpose or Objective
Purpose
: Whole brain radiotherapy (WBRT) remains
reference treatment in patients with brain metastasis
(BM), especially with multiple lesions. Hippocampus
avoidance in WBRT (HA-WBRT) offers the feasibility of less
impaired cognitive function than conventional WBRT and
better intracranial control than SBRT. Oncological safety
is critical in defining the proper role of HA-WBRT. The
study aims to investigate the frequency of intracranial
substructure involvement based on large series of
radiological data and to optimize the margin definition in
treatment planning.
Material and Methods
Methods
: Consecutive patients with diagnosis of BM from
enhanced MRI between 03/2011 and 07/2016 diagnosed
and treated in RuiJin Hospital were analyzed. Lesions of
each patient were confirmed by a senior radiologist and
the closest distances from tumor to the hippocampal area
were measured and analyzed by radiation oncologist.
Results
Results
: A total of 226 patients (pts) (115 males and 111
females) with 1080 metastatic measurable lesions have
been studied. The distribution of the primary tumours was
as following: 72.6% lung cancers (LC) (n=164), 19.9% breast
cancer (BC) patients (n=45) and 7.5% from other
malignancies (n=17). Seventy-one pts were diagnosed with
BM before or simultaneously with their primary
malignancy. In the case of others 155 pts, the latency of
BM appearance was as following: 14 months in LC pts
(n=100), 59 months in BC pts (n=42). Totally, 758 (70.2%)
lesions were situated beyond the tentorium. The median
diameter of the lesions was 10 mm (1.2mm-162mm). The
situation of the lesions was as following: 322 (29.8%) in the
cerebellum, 268 (24.8%) in the frontal lobe, 168 (15.6%) in
the temporal lobe, 128(11.9%) in the parietal lobe, 131
(12.1%)in the occipital lobe, 45 (4.2%)in the thalamus and
18 (1.6%) in the brainstem. After measuring the closest
between the lesions and the Hippocampus in every case,
the pts with lesions close to this zone (n=45) were
classifed into 3 catogories: 7 (3.1 %) at 5 mm or less, 13
(5.7%) within 10 mm or less and 19 (8.4%) at 20 mm or less
(Fig1). 45 patients received WBRT only and 18 of 45
patients who had complete radiological follow-up after
WBRT in the same hospital were founded progress of BM.
The median follow-up was 11 months. Only one new lesion
was observed in area of Hippocampus (less than 5 mm).
Conclusion
Conclusion
: With complete radiological diagnosis, HA-
WBRT can be delivered with oncological safety. Proper
margin definition of HA in delineation is to be confirmed
with individual technique.
PO-1004 Machine learning methods for automated
OAR segmentation
P. Tegzes
1
, A. Rádics
1
, E. Csernai
1
, L. Ruskó
2
1
General Electric, Healthcare, Budapest, Hungary
2
General Electric, Healthcare, Szeged, Hungary
Purpose or Objective
Manual contouring of organs at risk can take significant
time. The aim of this project is to use machine learning to
develop fully automated algorithms to delineate various
organs in the head and neck region on CT images.
Material and Methods
Machine learning models were built based on 48 CT
sequences of the head and neck region with 5 manually
contoured organs from the Public Domain Database for
Computational Anatomy. Data were randomly separated
to 32 train, 8 cross-validation and 8 test sequences. Three
different machine learning models were combined to
achieve automated segmentation. The first step uses a
support vector machine classifier to separate patient
anatomy from all other objects (a). The second step
applies slice-based deep learning classification to detect
the bounding box around the organ of interest (b). The
final step achieves voxel-level classification based on a
fully connected neural net on the voxel intensities of
suitably selected neighboring voxels (c). Very similar
model architectures were trained for all the different
organs.
Results
The body contour detection has been
previously trained
on another dataset containing full-body images and
achieved an average accuracy of 96.6%. The mean error of
the bounding box edges was 3mm, the corresponding dice
scores ranged from 72% to 94% depending on the organ of
interest. The first results of the voxel level segmentation
gave average dice values of 38% to 77% depending on the
investigated organ, and several opportunities for further
fine-tuning have been identified.