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Challenges for TPS

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.

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Challenges for TPS

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  1. Challenges for TPS Chunhua Men Elekta Software, Treatment Planning System BIRS Workshop Banff, Canada 3/12/2011

  2. Outline • Overview • Challenges • Conclusions

  3. Radiation Therapy Treatment Planning System • treatment planning system (TPS) for radiation therapy allows dosimetrists, physicists, and physicians to create, select, and verify the treatment plans for their patients Good efficiently withhigh quality

  4. Radiation Therapy Techniques • Delivery techniques: • external beam • brachytherapy • Linear accelerator x-ray (external beam): • Conventional linear accelerator - categorized by the modulation techniques: • 3D CRT • Conformal arc • IMRT (step-and-shoot, dMLC) • VMAT • Neurosurgery (Gamma Knife, CyberKnife) • TomoTherapy

  5. Roles of TPS in Radiation Therapy • Tumor and OAR contouring • Forward planning (3D CRT, conformal arc) • Beam setup (isocenter, beam angles, etc.) • Dose calculation • Inverse planning (IMRT, VMAT) • Beam setup (isocenter, beam modality, beam angles/arcs, etc.) • Prescriptions • Dose calculation and plan optimization • Plan evaluation, QA, export, etc.

  6. Why Challenges Exist? • The increasing complexity of treatment techniques: more and more components are involved in treatment • 3D CRT → conformal arc → IMRT → VMAT → … MLC moving, gantry moving, couch moving, next? • photon, electron → proton→ carbon→ ion→ … • Higher and higher request on plan quality • Static → Dynamic: robust /4D planning and adaptive RT: be able to handle motion (inter-fractional and intra-fractional) • More efficient treatment • reduced MU / treatment time • More efficient planning • on-line / real-time planning

  7. 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?

  8. 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.) • 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 • More sophisticated multi-criteria optimization / decision making methods • The optimization of a given system vs. the design of the optimal system

  9. 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” • Question: How to identify finite set of solutions provided to user? • Pre-calculating plans and interactive planning are time-consuming and may hit-and-miss • 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”

  10. 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?)

  11. 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

  12. Hypoxia and Re-oxygenation

  13. 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

  14. Small Picture: Optimization Technique Related Challenges • Can you further improve IMRT treatment quality? • 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 • In most TPSs, IMRT treatment plans are developed using a two-stage 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?

  15. 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…

  16. Non-technical Challenge • How to minimize the gap between the research outcomes and clinical applications? • Fact 1: research outcomes lead to clinical applications • Fact 2: clinical applications are far behind research outcomes

  17. Conclusions • There are many technical and non-technical challenges for TPS • To overcome the challenges • 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

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