Challenges for TPS
Chunhua Men
Elekta Software,
Treatment Planning System
BIRS Workshop
Banff, Canada
3/12/2011
Outline
• Overview
• Challenges
• Conclusions
Radiation Therapy Treatment Planning System
Good treatment planning system (TPS) for radiation
therapy allows dosimetrists, physicists, and physicians to
create, select, and verify the treatment plans for their
patients efficiently with high quality
Radiation Therapy Techniques
Delivery techniques:
1) external beam
2) brachytherapy
Linear accelerator x-ray (external beam):
1) Conventional linear accelerator - categorized by the
modulation techniques:
i. 3D CRT
ii. Conformal arc
iii. IMRT (step-and-shoot, dMLC)
iv. VMAT
2) Neurosurgery (Gamma Knife, CyberKnife)
3) TomoTherapy
Roles of TPS in Radiation Therapy
Tumor and OAR contouring
Forward planning (3D CRT, conformal arc)
1) Beam setup (isocenter, beam angles, etc.)
2) Dose calculation
Inverse planning (IMRT, VMAT)
1) Beam setup (isocenter, beam modality, beam
angles/arcs, etc.)
2) Prescriptions
3) Dose calculation and plan optimization
 Plan evaluation, QA, export, etc.
Why Challenges Exist?
 The increasing complexity of treatment techniques:
more and more components are involved in treatment
o 3D CRT → conformal arc → IMRT → VMAT → …
MLC moving, gantry moving, couch moving, next?
o photon, electron → proton→ carbon→ ion→ …
 Higher and higher request on plan quality
o Static → Dynamic: robust /4D planning and adaptive RT: be
able to handle motion (inter-fractional and intra-fractional)
More efficient treatment
o reduced MU / treatment time
More efficient planning
o on-line / real-time planning
Big Picture: Challenge 1
Can you offer a strong, integrated single product which
includes all state of the art technologies and supports
technological innovation, enable changing practice
trends, with very high efficiency?
 Implement different components from different
vendors?
 Supporting all modalities?
 Hybrid treatment planning?
 Quickly implement new emerging techniques?
Big Picture: Challenge 2
 Can you let me know what is the best achievable plan?
 The fact: current TPSs ask user to specify prescriptions
(tumor prescribed dose, DVH constraints, etc.) and/or
optimization parameters (weighting factors, penalty
thresholds, etc.)
o
o
o
Pre-setting weighting factors is a weighted sum method (a branch of
multi-criteria optimization)
Same prescriptions/parameters may lead to very different plans using
different TPSs; slightly different prescriptions/parameters may lead to
very different plans
Different physicians may choose different plans as the “best” ones
 The possible solution
o
o
More sophisticated multi-criteria optimization / decision making
methods
The optimization of a given system vs. the design of the optimal
system
Big Picture: Challenge 2 - Two Facts (from Wikipedia)
 “Instead of being a unique solution to the problem, the solution to a
multi-objective problem is a possibly infinite set of Pareto points”
o Question: How to identify finite set of solutions provided to user?
o Pre-calculating plans and interactive planning are timeconsuming and may hit-and-miss
o User needs a fast algorithm!
 “There are many MCDA / MCDM methods in use today. They all
claim that they can accurately solve this type of problem. However,
often different methods may yield different results for exactly the
same problem”
Big Picture: Challenge 3
Goal: Implement biologic information into optimization and
predict biological outcome at course and fraction level
 Background: radiobiological response of tumor and
normal tissue is dependent on many factors: cell
sensitivity to radiation, cell cycle, hypoxia, ...
 Currently: static CT (only electron density information)
 Ideally: integrate more biological information into planning
(patient level, organ level, voxel level?)
Cell Response
Cell sensitivity
Cell cycle
M: Most
sensitive
S: least
sensitive
Monaco (from Elekta) has
implemented cell sensitivity, but
just a single value at this moment
Hypoxia and Re-oxygenation
Big Picture: Challenge 3 –cont.
Difficulty #1: Acquiring biological information before
planning and during treatment: MRI, PET, and ?
Difficulty #2: Accurate modeling of the biological response
- huge mount of data needed
Difficulty #3: Uncertainty or variation of biological
outcome during treatment
Small Picture: Optimization Technique Related Challenges
 Can you further improve IMRT treatment quality?
o Beam orientation optimization (BOO) has been investigated by
many researchers, but none works well (in terms of
effectiveness and efficiency)
• The angles are chosen based on user’s experience
o In most TPSs, IMRT treatment plans are developed using a twostage process (fluence map optimization problem is followed by
a leaf-sequencing stage). The treatment quality gets worse a lot
in Stage 2.
• Direct aperture optimization has been investigated for many
years, but only simulated annealing algorithm has been
used in commercial TPS which is a heuristic-based method
and time-consuming
• Can you improve VMAT treatment quality?
Small Picture: Non-optimization Technique Related Challenges
 The accuracy of input data for 4D/robust optimation
 Adaptive RT (workflow, efficiency, CBCT image quality,
dose accumulation, etc. )
 Dose calculation: pencil beam – fast but inaccurate;
Monte Carlo – accurate but slow
 Auto-contouring
 And more…
Non-technical Challenge
 How to minimize the gap between the research
outcomes and clinical applications?
o Fact 1: research outcomes lead to clinical applications
o Fact 2: clinical applications are far behind research
outcomes
Conclusions
 There are many technical and non-technical
challenges for TPS
 To overcome the challenges
1.
2.
3.
4.
5.
Much more research work is needed
More researchers from various research areas
need to be involved in: imaging, data processing,
modeling / optimization, and more...
More radiobiologists, radiologists, radiation
oncologists and medical physicists input is
required
More vendors need to be involved
Overall, more collaborations are needed