A consortium of TU Delft, Erasmus MC and
LUMC is in the process of building a proton therapy clinic and start patient treatments
in 2017 (www.HollandPTC.nl). Proton
therapy, especially in its most advanced
application of intensity-modulated proton
therapy, allows very high conformality of the
delivered radiation dose to the tumor, with
significantly better sparing of healthy tissue
and organs in the patient compared to existing radiotherapy techniques. Already the three partners are involved in many areas of research of proton therapy such as dose calculation, in-vivo dosimetry, uncertainty quantification, treatment planning, etc. At present we have various positions available for MSc students in modeling respiratory motion, stochastic optimization and robust planning. All projects are cooperations between TU Delft and Erasmus MC, and students will spend half of their time at each institute.
Proton radiotherapy is a form of radiotherapy where protons are used to irradiate the patient. The advantage of these particles over conventional radiotherapy where photons are used, is that the dose distribution is such that healthy tissue is less affected by the treatment. The physics phenomenon behind this is that the protons have essentially a finite range after which no dose is deposited. By varying the energy of the particles, the complete tumor area can be covered. This high accuracy however comes with great sensitivity. If the composition or density of the tissue is different than expected, then the protons will stop at another location, possibly irradiating healthy tissue. In case of breathing motion this becomes even more complicated as the anatomy changes with the breathing cycle. Previously we have developed techniques for the propagation of uncertainties in patient position and proton range on the dose distribution. The purpose of this project is to model the breathing cycle including the deformation of the anatomy and to extend the previously developed techniques to include effects arising from the inherent motion of breathing.
Treatment planning in radiotherapy comprises the optimization process of achieving the best possible plan for a specific patient where a prescribed set of criteria concerning tumor dose, tumor coverage and various dose-constraints are combined and need to be satisfied. Such optimization procedures use the patient geometry (CT-scan) and material properties as input and output the best plan. It is especially important in proton therapy planning to take uncertainties in patient positioning or proton range into account from the start. Otherwise plans may result that are not robust to such uncertainties and patients may receive deteriorated dose distributions with negative consequences (side effects from e.g. overdosage of the glands). In practice such robust plans are made by considering a set of patient displacements and proton range shifts for which a treatment plan is made where the worst case of these scenarios still meets the desired criteria (worst case optimization). This is however not an optimal approach. In the present project a probabilistic view will be used as starting point where stochastic optimization procedures will be used instead of just the few discrete scenarios. In effect one can then get an optimal treatment plan in statistical sense. We will build on a proof of principle of the method delivered a few months ago. This project is more mathematically oriented.
Intensity Modulated Proton Therapy (IMPT) uses proton pencil beams whose intensities are individually optimized, which potentially results in an improved sparing of healthy tissues surrounding the tumor compared to conventional Intensity-Modulated Radiotherapy with photons. However, IMPT is highly susceptible to inaccuracies in the patient setup, internal organ motion, and from uncertainties in the anticipated proton range, known as range errors. These inaccuracies can be taken into account by constructing a robust treatment plan using ’minimax’ optimization. Minimax optimization includes the dose for a limited number of error scenarios and optimizes the worst case value of the objective function. Recently, we derived a so-called robustness recipe for head and neck cancer treatments that yields the error scenarios that need to be included to guarantee adequate CTV coverage for a high fraction patient treatments. The purpose of this project is to extent this robustness recipe to other treatment sites and treatment groups, to investigate its inter-patient variability, and to incorporate internal organ motion.
For more information you can contact Danny Lathouwers (firstname.lastname@example.org) or Mischa Hoogeman (M.email@example.com)