No abstract

Split-VMAT designed to accommodate an expiratory breath-hold period of 15s is a feasible and efficient use of liver SBRT, because it does not compromise the quality of the plan, when compared with 3D-CRT or continuous-VMAT.

Populating a KBP model with CHO data resulted in a high quality model. Since CHO plans can be generated automatically offline in a process that necessitates little to no user interaction, a CHO-KBP model can quickly adapt to changes in plan evaluation criteria or planning techniques without the need to wait to accrue sufficient numbers of clinical TEO plans. This may facilitate the use of KBP approaches for initial or ongoing quality assurance procedures and plan quality audits.

Purpose Relative biological effectiveness (RBE) uncertainties have been a concern for treatment planning in proton therapy, particularly for treatment sites that are near organs at risk (OARs). In such a clinical situation, the utilization of variable RBE models is preferred over constant RBE model of 1.1. The problem, however, lies in the exact choice of RBE model, especially when current RBE models are plagued with a host of uncertainties. This paper aims to determine the influence of RBE models on treatment planning, specifically to improve the understanding of the influence of the RBE models with regard to the passing and failing of treatment plans. This can be achieved by studying the RBE-weighted dose uncertainties across RBE models for OARs in cases where the target volume overlaps the OARs. Multi-field optimization (MFO) and single-field optimization (SFO) plans were compared in order to recommend which technique was more effective in eliminating the variations between RBE models. Methods Fifteen brain tumor patients were selected based on their profile where their target volume overlaps with both the brain stem and the optic chiasm. In this study, 6 RBE models were analyzed to determine the RBE-weighted dose uncertainties. Both MFO and SFO planning techniques were adopted for the treatment planning of each patient. RBE-weighted dose uncertainties in the OARs are calculated assuming of 3 Gy and 8 Gy. Statistical analysis was used to ascertain the differences in RBE-weighted dose uncertainties between MFO and SFO planning. Additionally, further investigation of the linear energy transfer (LET) distribution was conducted to determine the relationship between LET distribution and RBE-weighted dose uncertainties. Results The results showed no strong indication on which planning technique would be the best for achieving treatment planning constraints. MFO and SFO showed significant differences ( P <.05) in the RBE-weighted dose uncertainties in the OAR. In both clinical target volume (CTV)-brain stem and CTV-chiasm overlap region, 10 of 15 patients showed a lower median RBE-weighted dose uncertainty in MFO planning compared with SFO planning. In the LET analysis, 8 patients (optic chiasm) and 13 patients (brain stem) showed a lower mean LET in MFO planning compared with SFO planning. It was also observed that lesser RBE-weighted dose uncertainties were present with MFO planning compared with SFO planning technique. Conclusions Calculations of the RBE-weighted dose uncertainties based on 6 RBE models and 2 different revealed that MFO planning is a better option as opposed to SFO planning for cases of overlapping brain tumor with OARs in eliminating RBE-weighted dose uncertainties. Incorporation of RBE models failed to dictate the passing or failing of a treatment plan. To eliminate RBE-weighted dose uncertainties in OARs, the MFO planning technique i...

Purpose: Stereotactic body radiotherapy (SBRT) combining transarterial chemoembolization (TACE) with Lipiodol is expected to improve local control. This study aims to evaluate the impact of Lipiodol on dose distribution by comparing the dosimetric performance of the Acuros XB (AXB) algorithm, anisotropic analytical algorithm (AAA), and Monte Carlo (MC) method using a virtual heterogeneous phantom and a treatment plan for liver SBRT after TACE. Methods: The dose distributions calculated using AAA and AXB algorithm, both in Eclipse (ver. 11; Varian Medical Systems, Palo Alto, CA), and EGSnrc‐MC were compared. First, the inhomogeneity correction accuracy of the AXB algorithm and AAA was evaluated by comparing the percent depth dose (PDD) obtained from the algorithms with that from the MC calculations using a virtual inhomogeneity phantom, which included water and Lipiodol. Second, the dose distribution of a liver SBRT patient treatment plan was compared between the calculation algorithms. Results In the virtual phantom, compared with the MC calculations, AAA underestimated the doses just before and in the Lipiodol region by 5.1% and 9.5%, respectively, and overestimated the doses behind the region by 6.0%. Furthermore, compared with the MC calculations, the AXB algorithm underestimated the doses just before and in the Lipiodol region by 4.5% and 10.5%, respectively, and overestimated the doses behind the region by 4.2%. In the SBRT plan, the AAA and AXB algorithm underestimated the maximum doses in the Lipiodol region by 9.0% in comparison with the MC calculations. In clinical cases, the dose enhancement in the Lipiodol region can approximately 10% increases in tumor dose without increase of dose to normal tissue. Conclusion: The MC method demonstrated a larger increase in the dose in the Lipiodol region than the AAA and AXB algorithm. Notably, dose enhancement were observed in the tumor area; this may lead to a clinical benefit.