Uncertainties in the dosimetric heterogeneity correction and its potential effect on local control in lung SBRT.

Objective. Dose calculation in lung stereotactic body radiation therapy (SBRT) is challenging due to the low density of the lungs and small volumes. Here we assess uncertainties associated with tissue heterogeneities using different dose calculation algorithms and quantify potential associations with local failure for lung SBRT.Approach. 164 lung SBRT plans were used. The original plans were prepared using Pencil Beam Convolution (PBC, n = 8) or Anisotropic Analytical Algorithm (AAA, n = 156). Each plan was recalculated with AcurosXB (AXB) leaving all plan parameters unchanged. A subset (n = 89) was calculated with Monte Carlo to verify accuracy. Differences were calculated for the planning target volume (PTV) and internal target volume (ITV) Dmean[Gy], D99%[Gy], D95%[Gy], D1%[Gy], and V100%[%]. Dose metrics were converted to biologically effective doses (BED) usingα/β= 10Gy. Regression analysis was performed for AAA plans investigating the effects of various parameters on the extent of the dosimetric differences. Associations between the magnitude of the differences for all plans and outcome were investigated using sub-distribution hazards analysis.Main results. For AAA cases, higher energies increased the magnitude of the difference (ΔDmean of -3.6%, -5.9%, and -9.1% for 6X, 10X, and 15X, respectively), as did lung volume (ΔD99% of -1.6% per 500cc). Regarding outcome, significant hazard ratios (HR) were observed for the change in the PTV and ITV D1% BEDs upon univariate analysis (p = 0.042, 0.023, respectively). When adjusting for PTV volume and prescription, the HRs for the change in the ITV D1% BED remained significant (p = 0.039, 0.037, respectively).Significance. Large differences in dosimetric indices for lung SBRT can occur when transitioning to advanced algorithms. The majority of the differences were not associated with local failure, although differences in PTV and ITV D1% BEDs were associated upon univariate analysis. This shows uncertainty in near maximal tumor dose to potentially be predictive of treatment outcome.

Duke Scholars

Author Yunfeng Cui Radiation Oncology

Author Donna Niedzwiecki Biostatistics & Bioinformatics, Division of Biostatistics

Author Fang-Fang Yin Radiation Oncology

Author Christopher Ryan Kelsey Radiation Oncology

Author Justus D Adamson Radiation Oncology