The influence of beam model differences in the comparison of dose calculation algorithms for lung cancer treatment planning

In this study, we show that beam model differences play an important role in the comparison of dose calculated with various algorithms for lung cancer treatment planning. These differences may impact the accurate correlation of dose with clinical outcome. To accomplish this, we modified the beam model penumbral parameters in an equivalent path length (EPL) algorithm and subsequently compared the EPL doses with those generated with Monte Carlo (MC). A single AP beam was used for beam fitting. Two different beam models were generated for EPL calculations: (1) initial beam model (init_fit) and (2) optimized beam model (best-fit), with parameters optimized to produce the best agreement with MC calculated profiles at several depths in a water phantom. For the 6 MV, AP beam, EPL(init_fit) calculations were on average within 2%/2 mm (1.4 mm max.) agreement with MC; the agreement for EPL(best-fit) was 2%/0.5 mm (1.0 mm max.). For the 15 MV, AP beam, average agreements with MC were 5%/2 mm (7.4%/2.6 mm max.) for EPL(init_fit) and 2%/ 1.0 mm (1.3 mm max.) for EPL(best-fit). Treatment planning was performed using a realistic lung phantom using 6 and 15 MV photons. In all homogeneous phantom plans, EPL(best-fit) calculations were in better agreement with MC. In the heterogeneous 6 MV plan, differences between EPL(best-fit and init_fit) and MC were significant for the tumour. The EPL(init_fit), unlike the EPL(best-fit) calculation, showed large differences in the lung relative to MC. For the 15 MV heterogeneous plan, clinically important differences were found between EPL(best-fit or init_fit) and MC for tumour and lung, suggesting that the algorithmic difference in inhomogeneous tissues was most influential in this case. Finally, an example is presented for a 6 MV conformal clinical treatment plan. In both homogeneous and heterogeneous cases, differences between EPL(best-fit) and MC for lung tissues were smaller compared to those between EPL(init_fit) and MC. Although the extent to which beam model differences impact the dose comparisons will be dependent upon beam parameters (orientation, field size and energy), and the size and location of the tumour, this study shows that failing to correctly account for beam model differences will lead to biased comparisons between dose algorithms. This may ultimately hinder our ability to accurately correlate dose with clinical outcome.