Physica Medica: European Journal of Medical Physics - Volume 32 - Supplement 1 - February 2016

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A.103
THE ORGAN EQUIVALENT DOSE TO QUANTIFY SECONDARY CANCER
INDUCTION IN BREAST AFTER VMAT TREATMENTS
G. Guidi N. Maffei F. Itta E. D’angelo B. Meduri P. Ceroni
G.M. Mistretta A. Ciarmatori G. Gottard P. Giacobazzi G. Baldazzi
T. Cost a Az. Ospedaliero-Universitaria di Modena, Modena, Italy; b Università
di Bologna, Bologna, Italy
Introduction: During volumetric arc therapy (VMAT) therapy, the dose de-
livered involves significant area and organs with low dose. In order to study
the Radiation Therapy (RT) cancer induction probability, the risk for con-
tralateral breast and lung secondary tumor was evaluated, estimating the
organ equivalent dose (OED).
Materials and Methods: The dose distribution of 30 breast cancer pa-
tients treated with VMAT techniques were analyzed in this retrospective
study. The cohort was divided for treatment side (right/left) and the OED
for each organ was calculated from the dose volume histogram (DVH). The
bell shaped model formula was applied in a MATLAB® toolbox to esti-
mate secondary cancer induction in breast and lung tissues. Contralateral
and ipsilateral statistic outcomes were assessed using SPSS®.
Results: A mean ODE of 2.09 ± 0.32, 1.94 ± 0.32 and 2.55 ± 0.61 was ob-
tained for contralateral lung, ipsilateral lung and contralateral breast
respectively. For contralateral organs, an ANOVA analysis (sign. > 0.30) con-
firmed that these results were independent to the side of treatment, with
an intra-group variability of [2.1 ÷ 3.1 Gy] and [2.2 ÷ 3.8 Gy] for the right
and left side. For ipsilateral organs, in agreement with other scientific works
in literature, the OED showed a less variability (±0.16 Gy).
Conclusions: The ODE value is a simple and useful estimation of radia-
tion secondary cancer induction of VMAT treatments based on a DVH
analysis. With a cross correlation between contralateral and ipsilateral struc-
tures, an independence from the side of treatment was detected, highlighting
a lower intra-organ variability in case of ipsilateral comparison. To in-
crease the robustness of the study, the bell shaped model used could be
related to the time patterns of cancer induction. Moreover, absolute risk
should be investigated in a larger patients’ cohort or in national data mining.
A.104
A MACHINE LEARNING TOOL FOR RE-PLANNING AND ADAPTIVE RT: A
MULTICENTER COHORT INVESTIGATION
G. Guidi N. Maffei B. Meduri A. Ciarmatori G.M. Mistretta
S. Maggi M. Cardinali V.E. Morabit F. Rosica S. Malar A. Savini
G. Orlandi C. D’Ugo F. Bunkheila M. Bono S. Lappi C. Blasi
P. Giacobazzi G. Baldazzi T. Cost a Az. Ospedaliero-Universitaria di
Modena, Modena, Italy; b Università di Bologna, Bologna, Italy;
c Az.Ospedaliero-Universitaria Ospedale Riuniti di Ancona, Ancona, Italy; d AUSL4
Teramo, Teramo, Italy; e Az.Osp.Ospedali Riuniti Marche Nord di Pesaro, Pesaro,
Italy
Introduction: A multicenter research was carried out to validate predic-
tive strategies: to determinate patients eligible for re-planning due to
predictable anatomical variations. Advantage and challenges of IGRT, de-
formable image registration and different set-up protocols were evaluated
in a multicenter retrospective study using planning data.
Materials and Methods: 76 head and neck (H&N) patients were consid-
ered with more than 2200 daily studies analyzed: 1200 MVCT from Center-A
(A), 600 CBCT from Center-B (B), 240 CBCT from Center-C (C) and 240 CBCT
from Center-D (D). To obtain a predictive time of warping during the 6 weeks
of therapy, the study focused on volume and dose variations of parotid glands
(PG). RayStation hybrid algorithm, supported by IronPython scripts and GPU
computing, was used to perform a daily deformable image registration, struc-
tures automatic re-contouring and dose accumulation. A home-made
machine-learning classifier tool was developed in MATLAB. Support Vector
Machines (SVM) and cluster analysis were used for a weekly time-series
evaluation and patients’ selection.
Results: At the end of the treatment (~30 fractions), the whole cohort is
affected by a PG mean reduction of 23.7 ± 8.8%: 25.1 ± 9.2% (A), 23.8 ± 6.6%
(B), 21.2 ± 10.3% (C) and 24.4 ± 9.8% (D). Using machine learning ap-
proach, the shrinkage of 86.3% of cases can be predicted during the first 3
weeks of therapy: 89.6% (A), 92.7% (B), 76.0% (C) and 87.0% (D). The number
of patients that would benefit from a review of the initial plan reached 53.5%
from the 4th week, with an inter-centers variability of 19.7%.
Conclusions: A SVM and cluster decision making tool was developed and
trained in order to overcome Adaptive RT logistic challenges in a busy clin-
ical routine. The time for re-planning and the specific patients that would
benefit from a new plan were quantified in this multicenter study to in-
crease the personalization of the patients’ treatment during all sessions.
A.105
NEW ERA FOR QA AND VMAT: REAL-TIME MONITOR SYSTEM IN CLINICAL
PRACTICE
G. Guidi * N. Maffei G.M. Mistretta P. Ceroni A. Ciarmatori
L. Morini A. Bernabei P. Giacobazzi G. Baldazzi T. Costi
a Az.Ospedaliero-Universitaria di Modena – Modena – Italia, Modena, Italy;
b Università di Bologna – Bologna – Italia, Bologna, Italy
Introduction: During radiation therapy (RT) treatment, a real-time moni-
toring of the delivery could increase patients’ safety. An independent monitor
system was tested to prove the feasibility of real-time monitoring of cal-
ibration errors, malfunctions in multi-leaf collimator (MLC) or inappropriate
setup parameter during VMAT plan.
Material and Methods: 6 months of measures were carried out mount-
ing the iQM® system below the gantry of an Elekta Synergy accelerator.
The repeatability of the detector was tested in >70 quality assurance (QA)
sessions. A dummy plan (17 segments 4 × 4 cm2 and 1 segment 10 × 10 cm2
with constant 50 MU per segments) and a complex Head and Neck (H&N)
VMAT plan (1 arc with 140 control points, low gantry speed, high MU and
low dose rate) were used. Sensitivity was tested by introducing specific do-
simetric errors of MU (1÷20%) in the H&N plan. Correlation with gantry
and collimator angles was evaluated.
Results: Delivering the dummy plan in standard condition (gantry and col-
limator angles = 0°), a counts mean variability of 0.7 ± 1.0% was detected
in comparison with the commissioning day. No statistical difference (ANOVA
sign. ~1) was detected for all segments in rotational conditions (gantry angle:
0°–90°–180°–270° and collimator angle: 0°–45°–135°–225°–315°). Con-
trariwise, unexpected counts were observed in the H&N plan with low angle
gantry (120° ÷ 240°), showing a mean dose discrepancy of 2.8 ± 1.0% from
the original plan. The ad hoc MU introduced errors were detected within
a range of [0.1–0.4%] with a linear trend (R2 = 0.99).
Conclusion: The repeatability of measures highlights the robustness of the
system. Uncorrected rotation of the gantry or increased MU delivered in
comparison with the treatment plan was detected. Following the output
for each beam segment with the differential and cumulative approach, the
detector enables a real-time check during VMAT treatment finalized to pa-
tients’ safety and to evaluate the daily condition of the machine during QA.
A.106
NAL PROTOCOL IMPLEMENTATION AND REDUCTION OF SYSTEMATIC
ERRORS IN PATIENT SETUP DURING RADIATION THERAPY
M. Haller S. Hofer P. Ferrari M. Maffei a Azienda Sanitaria dell’Alto
Adige – Servizio Aziendale di Fisica Sanitaria, Bolzano, Italy; b Azienda Sanitaria
dell’Alto Adige – Servizio di Radioterapia Oncologica del Comprensorio Sanitario
di Bolzano, Bolzano, Italy
Introduction: This study aimed to assess a patient setup correction pro-
tocol during radiation therapy in order to minimize the systematic error
and consequently the required number of Cone Beam (CB) CT. We imple-
ment a NAL (No Action Level) protocol observing trends of errors in patient
setup in the three different orientations and for three different pathology
treatments, prostate, breast and rectum.
Material and Methods: In the study we analyse data of 33 patients and
calculate the mean setup errors over the first 5 fractions, and apply them
in the setup for subsequent treatments. The performance of the protocol
was evaluated observing the trends by means of 3 CB CT’s every week, and
calculating the mean and stdev of setup errors for every patient, every pa-
thology and every treatment fraction.
e31 Abstracts/Physica Medica 32 (2016) e1–e70