Proton therapy plans are very sensitive to anatomical changes such as density changes along the pencil-beam paths and changes in organ shape and location. Previously, we developed a restoration method which compensates for density changes along the pencil-beam paths but which is unable to adapt for anatomical changes. This study's purpose is to develop and evaluate an automated method for adaptation of IMPT plans in near real-time to the anatomy of the day. We developed an automated treatment plan adaptation method using (1) a restoration of spot positions (Bragg peaks) by adapting the energies to the new water equivalent path lengths; and (2) a spot addition to fully cover the target of the day, followed by a fast reference point method optimization of the spot weights resulting in a Pareto optimal plan for the daily anatomy. The method was developed and evaluated using 8-10 repeat CT scans of 11 prostate cancer patients, prescribing 55 Gy(RBE) (seminal vesicles and lymph nodes) with a boost to 74 Gy(RBE) (prostate). Applying the automated adaptation method resulted in a clinically acceptable target coverage (V 95% 98% and V 107% 2%) for 96% of the scans after a single iteration of adding 2500 spots. The other scans obtained target coverages with V 95% 98% and 2 < V 107% 5%. When using two spot-addition iterations, all scans obtained clinically acceptable results. Compared to the restoration method the adaptation lowered the mean dose to rectum and bladder with median values of 6.2 Gy(RBE) and 4.7 Gy(RBE) respectively. The largest improvements were obtained for V 45Gy(RBE) for both rectum and bladder, with median differences of 10.3%-point and 10.8%-point respectively, and maximum differences up to 22%-point. The two adaptation steps took on average 7.3 s and 1.7 min respectively. No user interaction was needed, making this fast and fully automated method a first step towards online adaptive proton therapy.

, , ,,
Physics in Medicine and Biology

Jagt, T., Breedveld, S., Van Haveren, R., Heijmen, B., & Hoogeman, M. (2018). An automated planning strategy for near real-time adaptive proton therapy in prostate cancer. Physics in Medicine and Biology, 63(13). doi:10.1088/1361-6560/aacaa7