A Literature Review of Proton Beam Therapy for Prostate Cancer in Japan

Role of Proton Beam Therapy in Current Day Radiation Oncology Practice

Proton beam therapy (PBT), because of its unique physics of no–exit dose deposition in the tissue, is an exciting prospect. The phenomenon of Bragg peak allows protons to deposit their almost entire energy towards the end of the path of the proton and stops any further dose delivery. Braggs peak equips PBT with superior dosimetric advantage over photons or electrons because PBT doesn’t traverse the target/body but is stopped sharply at an energy dependent depth in the target/body. It also has no exit dose. Because of no exit dose and normal tissue sparing, PBT is hailed for its potential to bring superior outcomes. Pediatric malignancies is the most common malignancy where PBT have found utmost application. Nowadays, PBT is also being used in the treatment of other malignancies such as carcinoma prostate, carcinoma breast, head and neck malignancies, and gastrointestinal (GI) malignancies. Despite advantages of PBT, there is not only a high cost of setting up of PBT centers but also a lack of definitive phase-III data. Therefore, we review the role of PBT in current day practice of oncology to bring out the nuances that must guide the practice to choose suitable patients for PBT.

Accelerated robust optimization algorithm for proton therapy treatment planning

PURPOSE

Robust optimization is a computational expensive process resulting in long plan computation times. This issue is especially critical for moving targets as these cases need a large number of uncertainty scenarios to robustly optimize their treatment plans. In this study, we propose a novel worst-case robust optimization algorithm, called dynamic minimax, that accelerates the conventional minimax optimization. Dynamic minimax optimization aims at speeding up the plan optimization process by decreasing the number of evaluated scenarios in the optimization.

METHODS

For a given pool of scenarios (e.g., 63 = 7 setup  × 3 range  × 3 breathing phases), the proposed dynamic minimax algorithm only considers a reduced number of candidate-worst scenarios, selected from the full 63 scenario set. These scenarios are updated throughout the optimization by randomly sampling new scenarios according to a hidden variable P, called the "probability acceptance function," which associates with each scenario the probability of it being selected as the worst case. By doing so, the algorithm favors scenarios that are mostly "active," that is, frequently evaluated as the worst case. Additionally, unconsidered scenarios have the possibility to be re-considered, later on in the optimization, depending on the convergence towards a particular solution. The proposed algorithm was implemented in the open-source robust optimizer MIROpt and tested for six four-dimensional (4D) IMPT lung tumor patients with various tumor sizes and motions. Treatment plans were evaluated by performing comprehensive robustness tests (simulating range errors, systematic setup errors, and breathing motion) using the open-source Monte Carlo dose engine MCsquare.

RESULTS

The dynamic minimax algorithm achieved an optimization time gain of 84%, on average. The dynamic minimax optimization results in a significantly noisier optimization process due to the fact that more scenarios are accessed in the optimization. However, the increased noise level does not harm the final quality of the plan. In fact, the plan quality is similar between dynamic and conventional minimax optimization with regard to target coverage and normal tissue sparing: on average, the difference in worst-case D95 is 0.2 Gy and the difference in mean lung dose and mean heart dose is 0.4 and 0.1 Gy, respectively (evaluated in the nominal scenario).

CONCLUSIONS

The proposed worst-case 4D robust optimization algorithm achieves a significant optimization time gain of 84%, without compromising target coverage or normal tissue sparing.