Speaker
Description
Intensity modulated proton therapy (IMPT) is an advance form of proton therapy, in which tumor is irradiated by proton beamlet spot-by-spot and layer-by-layer through controlling the trajectory and energy of a focus beam of protons.1,2 While proton radiobiological effects depend primarily on their physical dose distribution, studies have showed that linear energy transfer (LET) plays an important role too.3 LET itself can be used as indicator for radiobiological outcome at the microscopic level, which would justify the use of purely LET-based objectives in treatment plan optimization.4
We developed a method using a hybrid approach to calculate proton linear energy transfer (LET) for intensity modulated proton therapy (IMPT) based on the data pre-computed by Geant4 Monte Carlo (MC) simulations. The hybrid method was incorporated into our in-house developed treatment planning system (TPS), as an extension to calculate LET in voxelized patient geometries. First we commissioned the Geant4 MC code to model three proton treatment nozzles installed in our hospital without range shifter (VAC machine), with range shifter at 42.5 cm away from iso-center (RS machine), and with range shifter at 30 cm away from iso-center (ERS machine). The code was used to generate pencil beam type of LET kernels for all 97 proton energies used clinically. Second, the LET kernels were incorporated into the in-house developed TPS using the ray-casting algorithm. The inhomogeneities were taken into account using water-equivalence-thickness (WET). Since the LET kernels were pre-calculated, the calculation of LET distribution in patient geometries takes much less time. It is found that the LET distribution calculated by the in-house developed TPS agrees well with the MC calculation. The calculated LET distributions were used to evaluate potential clinical benefit and toxicity for various tumor sites including lung, head and neck, esophageal, and brain etc. The LET calculation code has also been used in the IMPT treatment planning, allowing for radiobiological optimization by including LET-weighted constraints in the inverse treatment planning process.
