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In terms of robustness evaluation, PTV-based MFO showed
reduced robustness against both anatomical changes and
uncertainties, i.e. wider DVH bands and a disagreement
between planned and summed dose, whereas the robust
MFO is less influenced. Both SFO approaches resulted in
robust plans on the CTVs (Figure 1).
Conclusion
The PTV-based MFO approach showed less robustness
against uncertainties in setup and range, as well as for
anatomical changes during the treatment course. Both
SFO plans are robust in terms of CTV coverage; however,
they present higher doses to the ipsilateral parotid gland.
Robust MFO approach presents the lowest doses to the
ipsilateral parotid and more robustness against
uncertainties.
The dose to more organs at risk and the difference in
normal tissue complication probabilities for the 4 planning
approaches will be presented as well.
PO-0820 Full automation of radiation therapy
treatment planning
L. Court
1
, R. McCarroll
1
, K. Kisling
1
, L. Zhang
1
, J. Yang
1
,
H. Simonds
2
, M. Du Toit
2
, M. Mejia
3
, A. Jhingran
4
, P.
Balter
1
, B. Beadle
4
1
MD Anderson Cancer Center, Department of Radiation
Physics, Houston, USA
2
Stellenbosch University, Radiation Oncology,
Stellenbosch, South Africa
3
University of Santo Tomas, Department of Radiation
Oncology, Manila, Philippines
4
MD Anderson Cancer Center, Department of Radiation
Oncology, Houston, USA
Purpose or Objective
To fully automate radiotherapy planning for cervical
cancer (4-field box treatments) and head/neck cancer
(VMAT/IMRT).
Material and Methods
We are using a combination of in-house software, Eclipse
Treatment Planning System, and Mobius 3D to create and
validate radiotherapy plans. Most planning tasks have
been automated using a primary algorithm for the
treatment plan, and a secondary independent algorithm
to verify the primary algorithm.
The first step is to automatically determine the external
body surface and isocenter (based on radiopaque markers
in a 3-point setup) using two independent techniques.
For H/N cases, the radiation oncologist manually
delineates the GTV. Normal tissues (parotids, cord,
brainstem, lung, eyes, mandible, cochlea, brain), cervical
neck nodes (levels II-IV, IB-V or IA-V) and retropharyngeal
nodes are automatically delineated using an in-house
multi-atlas segmentation tool. The RapidPlan tool
(Eclipse) is used to create a VMAT plan.
For 4-field box cervical cancer treatments, the field
apertures (jaw and MLC positions) are automatically
calculated based on bony anatomy using two techniques:
The primary technique uses atlas-based segmentation of
bony anatomy, and then calculates apertures based on the
projection of these bones to each beam’s-eye-view. The
secondary technique deformably registers atlas DRRs to
the patient’s DRR for each beam, then uses the
deformation matrix to deform atlas blocks (MLC positions)
to the patient’s DRR. Relative beam weighting is
determined based on a least-squares fit, minimizing
heterogeneity in the treatment volume.
Final dose distributions are automatically sent to Mobius
for secondary dose calculation.
Results
Primary and secondary techniques for identifying the body
surface agreed within 1.0mm/0.99 (mean distance to
agreement/average DICE coefficient). Primary and
secondary techniques for determining isocenter agreed
within 3mm. H/N normal tissue and lymph node
segmentation was evaluated by a radiation oncologist (128
patients), and found to be acceptable for all structures,
except for esophagus and cochlea and in situations where
the head position was non-standard. The figure below
shows a fully automated plan including contours and
optimized doses.