Target dose conversion modeling from pencil beam (PB) to Monte Carlo (MC) for lung SBRT

Abstract

BackgroundA challenge preventing routine clinical implementation of Monte Carlo (MC)-based lung SBRT is the difficulty of reinterpreting historical outcome data calculated with inaccurate dose algorithms, because the target dose was found to decrease to varying degrees when recalculated with MC. The large variability was previously found to be affected by factors such as tumour size, location, and lung density, usually through sub-group comparisons. We hereby conducted a pilot study to systematically and quantitatively analyze these patient factors and explore accurate target dose conversion models, so that large-scale historical outcome data can be correlated with more accurate MC dose without recalculation.MethodsTwenty-one patients that underwent SBRT for early-stage lung cancer were replanned with 6MV 360° dynamic conformal arcs using pencil-beam (PB) and recalculated with MC. The percent D95 difference (PB-MC) was calculated for the PTV and GTV. Using single linear regression, this difference was correlated with the following quantitative patient indices: maximum tumour diameter (MaxD); PTV and GTV volumes; minimum distance from tumour to soft tissue (dmin); and mean density and standard deviation of the PTV, GTV, PTV margin, lung, and 2 mm, 15 mm, 50 mm shells outside the PTV. Multiple linear regression and artificial neural network (ANN) were employed to model multiple factors and improve dose conversion accuracy.ResultsSingle linear regression with PTV D95 deficiency identified the strongest correlation on mean-density (location) indices, weaker on lung density, and the weakest on size indices, with the following R2 values in decreasing orders: shell2mm (0.71), PTV (0.68), PTV margin (0.65), shell15mm (0.62), shell50mm (0.49), lung (0.40), dmin (0.22), GTV (0.19), MaxD (0.17), PTV volume (0.15), and GTV volume (0.08). A multiple linear regression model yielded the significance factor of 3.0E-7 using two independent features: mean density of shell2mm (P = 1.6E-7) and PTV volume (P = 0.006). A 4-feature ANN model slightly improved the modeling accuracy.ConclusionQuantifiable density features were proposed, replacing simple central/peripheral location designation, which showed strong correlations with PB-to-MC target dose conversion magnitude, followed by lung density and target size. Density in the immediate outer and inner areas of the PTV showed the strongest correlations. A multiple linear regression model with one such feature and PTV volume established a high significance factor, improving dose conversion accuracy.

Does intensity modulation increase target dose calculation errors of conventional algorithms for lung SBRT?

“…The dose errors observed in this study for Type‐A and Type‐B algorithms are in agreement with numerous previous studies 7, 9, 14, 15. Both algorithms overestimated the target peripheral or “cold spot” indices such as D95% and Dmin, and the magnitude of the overestimation varied widely from case to case.…”

“…However, other concerns remain regarding the suitability of intensity‐modulated techniques for lung SBRT treatments, associated with smaller field apertures, and specifically whether they would lead to significantly higher dose errors than conformal techniques when calculated with conventional dose algorithms. This concern was based on previous findings that field size impacts dose errors (wherein smaller target sizes were associated with higher dose errors than larger target sizes) 5, 7, 14, 20. However, this intuitive concern has not been substantiated; the dose error influence of intensity modulation, which effectively generates smaller field apertures, remains to be elucidated.…”

“…9 Dose calculation has been widely compared among the above algorithms for lung SBRT, with interalgorithm differences mostly observed for the target dose. 5,7,9,10,14 It is known that as compared with Type-C algorithms, Type-A and Type-B algorithms tend to overestimate the target dose for lung SBRT, and the magnitude of the dose error varies widely from case to case, up to over 30% for Type A and over 15% for Type B. 7,9,15 These results include both forward plans using conventional conformal techniques as well as inverse plans using modern intensity-modulated techniques.…”

“…This concern was based on previous findings that field size impacts dose errors (wherein smaller target sizes were associated with higher dose errors than larger target sizes). 5,7,14,20 However, this intuitive concern has not been substantiated; the dose error influence of intensity modulation, which effectively generates smaller field apertures, remains to be elucidated.…”

Organs at risk in lung SBRT

“…In this context many groups studied these dosimetric differences, mainly focusing on the target [1]. Regarding organs at risk (OAR), Zheng and colleagues found, on 21 lung SBRT patients, the differences between Monte Carlo (the gold standard) and Pencil Beam (Type ''a" algorithm) calculations for the OARs and normal tissues to be relatively small, within 3% for all structures [2]. Schuring and Hurkmans [3] reported, on 26 patients prescribed at 60 Gy, significant (but small in absolute value) differences (P < 0.001) between algorithms on MLD (4.0 Gy, 4.5 Gy, and 4.4 Gy for, respectively, types ''a" and ''b", and reference values).…”