Background: While intensity modulated proton therapy (IMPT) plans allow for increased normal tissue sparing in comparison to photon radiotherapy, IMPT is also more sensitive to treatment uncertainties, including geometrical shifts in patient anatomy and range errors. Additionally, for thoracic treatment sites, e.g. the oesophagus, interference between breathing motion and IMPT treatment delivery, ‘the interplay effect’, can significantly degrade the quality of the dose distribution. To ensure IMPT plans are robust against treatment uncertainties, the dose distribution should ideally be evaluated in a high number of error scenarios. In this way, probabilistic robustness evaluation (PRE) enables the derivation of the probability distribution of relevant dose distribution parameters. Previous research has shown that polynomial chaos expansion (PCE) methods can efficiently perform PRE for head-and-neck and neurological IMPT plans, accounting for geometrical shifts and range errors. The goal of this thesis was to extend the PCE methodology for PRE to include the interplay effect, in addition to geometrical shifts and range errors, for evaluation of clinical IMPT plans of oesophageal cancer patients.
Methods: To include the interplay effect in the PCE methodology, we simulated 4D dynamic dose distributions with 1 set of PCE models per breathing signal. Sinusoidal breathing signals with a period between 3 s and 7 s were assumed. For 1 patient, we studied the effect of PCE parameters – Monte Carlo (MC) noise level, grid order (GO), polynomial order (PO), and PCE coefficient calculation methods – on PCE model accuracy. PCE model parameters and PRE parameters were selected with cost-accuracy analysis and validated for 2 patients. To evaluate the performance of the clinical protocol that was used to create the IMPT plans, PRE was performed including geometrical shifts, range errors, and different breathing signals for 20 clinical IMPT plans of oesophageal cancer patients treated at Holland PTC. We evaluated the probability of adequate CTV dose and scenario distributions of OAR (heart) dose and normal tissue complication probability (NTCP). Finally, we analysed the effect of several factors on PRE outcomes for 1 patient, including presence/absence of fractionation and repainting, extreme breathing signals, and presence/absence of geometrical shifts and range errors.
Results: Lowering the MC noise level improved PCE model accuracy. Selection of the GO3E1PO4 MC1.0% regression model resulted in accurate models for acceptable computational costs. Cost-accuracy analysis showed that 10 000 fully fractionated scenarios and 5 included breathing signals were statistically adequately for PRE, resulting in a total computation time of < 1 day per patient. For all patients, we found a probability of adequate clinical target volume dose (CTV D99.8% > 0.95 Dprescribed) larger than the criterium of 90%, with a population mean 99.56% (minimum – maximum: 98.75% – 99.96%). If fractionation and/or repainting are not considered, ≥ 20 breathing signals could be required for statistically adequate PRE.
Conclusion: We established and validated a pipeline for extending the PCE methodology for PRE to account for breathing motion. Our selected PCE models were as accurate as previously reported models. However, the number of included breathing signals required for statistically adequate PRE could vary depending on the IMPT treatment planning protocol. Our results show that the protocol used to create the included IMPT plans leads to safe, but conservative treatment plans. A lower prescription dose level could be used to significantly reduce the heart dose and NTCP, while still ensuring adequate dose to the CTV.