468 / 1096
S453
ESTRO 36
_______________________________________________________________________________________________
Conclusion
This study demonstrates the ability of creating high-
quality MRL treatment plans for rectum cancer. Given the
differences in machine characteristics, some plan quality
differences were found between MRL treatment plans and
current clinical practice. These results support a well-
prepared clinical introduction of the MRL.
PO-0839 Personalized VMAT optimization for
pancreatic SBRT
I. Mihaylov
1
, L. Portelance
1
1
University of Miami, Radiation Oncology, Miami, USA
Purpose or Objective
Inverse IMRT planning is a very labor intensive, trial-and-
error process, aiming to find a middle ground between the
conflicting objectives of adequate tumor coverage and
sparing nearby healthy tissues. Even if a plan is clinically
acceptable, that plan is unlikely to be the best solution,
where the healthy tissue is spared as much as possible. To
a large extent the optimization process is user and
treatment planning system specific, where more
experienced users generate better quality radiotherapy
plans. This work introduces a fully automated inverse
optimization approach and its application to pancreatic
SBRT.
Material and Methods
Ten cases, treated breath-hold, were retrospectively
studied. The outlined anatomical structures consisted of a
PTV, and OARs including duodenum, stomach, bowel,
spinal cord, liver, and kidneys. In each case the
prescription was set to 35 Gy (to 95% of the PTV) in 5
fractions. The treatment plans were created by
experienced dosimetrists, following national and
international clinical protocols. Those treatment plans
were generated for VMAT delivery. For each case an
additional plan was generated with the newly proposed
automated inverse optimization. This optimization is
based on unattended step-wise reduction of DVHs, where
several DVH objectives were specified for each OAR. The
automated plans utilized the same number of arcs, with
the same parameters as the treatment plans. The
treatment and the automated plans (Treatment and Auto
hereafter) were compared on commonly used clinical
dosimetric parameters. Those parameters included D
PTV
95%
(dose to 95% of the PTV), D
Duodenum
1%
, D
Bowel
1%
, D
Stomach
1%
,
D
Cord
1%
, D
Liver
mean
, D
rt_kidney
mean
, and D
lt_kidney
mean
. The doses to
1% of the volumes of duodenum, bowel, stomach, and
spinal cord were used as surrogates for maximum doses.
The prescriptions for the Auto plans matched the
prescriptions of the Treatment plans.
Results
The first row in the table below summarizes the average
values of the tallied quantities (over the ten patients) as
derived from the treatment plans. The second row
outlines the average differences (in per-cent) between the
dosimetric endpoints as well as the range of the
differences between the Treatment and the Auto-
optimized plans. The negative differences indicate that
the Auto plans result in lower absolute doses and vice-
versa. The figure outlines the normalized (with respect to
the Treatment plans) tallied quantities on patient-by-
patient basis. In 8 out of the 40 maximum doses the
Treatment plans demonstrated lower absolute doses. For
none of the 30 tallied average (or mean) doses the
Treatment plans were better than the Auto plans. The
average differences over the patient cohort range from -
7% to +36%.
Conclusion
Unattended inverse optimization holds great potential for
further personalization and tailoring of radiotherapy to
particular patient anatomies. It utilizes minimum user
time and it can be used at the very minimum as a good
starting point for personalized precision radiotherapy.
PO-0840 Hypofractionated intensity modulated
radiotherapy in patients with immediate breast
reconstruction
D.P. Rojas
1
, R. Ricotti
2
, M.C. Leonardi
2
, A. Viola
1
, S.
Dicuonzo
1
, D. Ciardo
2
, R. Cambria
3
, R. Luraschi
3
, F.
Cattani
3
, C. Fodor
2
, A. Morra
2
, V. Dell'Acqua
2
, V.
Galimberti
4
, R. Orecchia
5
, B.A. Jereczek-Fossa
1
1
European Institute of Oncology - University of Milan,
Department of Radiation Oncology - Department of
Oncology and Hemato-oncology, MIlan, Italy
2
European Institute of Oncology, Department of
Radiation Oncology, MIlan, Italy
3
European Institute of Oncology, Department of Medical
Physics, MIlan, Italy
4
European Institute of Oncology, Department of Surgery,
MIlan, Italy
5
European Institute of Oncology - University of Milan,
Department of Medical Imaging and Radiation Sciences -