Dosimetric errors during treatment of centrally located lung tumors with stereotactic body radiation therapy: Monte Carlo evaluation of tissue inhomogeneity corrections

Evaluation of dose calculations with inhomogeneity correction in intensity-modulated radiation therapy for esophagus cancer

BACKGROUND

Differences often exist in the dose calculation accuracy caused by using different dose calculation algorithms in non-uniform tissues.

OBJECTIVE

To evaluate the accuracy of dose calculation with inhomogeneity correction in intensity-modulated radiation therapy (IMRT) by comparing dose calculated in Monaco with measurements in lung-chest phantom for esophagus cancer treatments.

METHODS

Finite size pencil beam (FSPB) and X-ray voxel Monte Carlo (XVMC) were used respectively for IMRT dose recalculations. Ten IMRT plans were recalculated and measured in the chest-lung phantom. The dose measurements using the Gafchromic ® (EBT3) dosimetry films were validated with open fields in the interfaces of materials with various physical densities. The accuracy of dose calculations was then evaluated by both point dose comparison and Gamma analysis against the film measurements.

RESULTS

For regular open fields, the discrepancies of the point doses were less than 3.0% and 2.0% between measurement and calculations by FSPB and XVMC, respectively. For 6 MV IMRT plans, the average passing rates based on 3% /3 mm Gamma criteria were 82.8±1.0% and 96.4±0.7% for FSPB and XVMC, respectively.

CONCLUSIONS

The XVMC algorithms more accurate in IMRT dose calculations with inhomogeneity correction for esophagus cancer.

Prediction of GTV median dose differences eases Monte Carlo re-prescription in lung SBRT

BACKGROUND AND PURPOSE

The use of Monte Carlo (MC) dose calculation algorithm for lung patients treated with stereotactic body radiotherapy (SBRT) can be challenging. Prescription in low density media and time-consuming optimization conducted CyberKnife centers to propose an equivalent path length (EPL)-to-MC re-prescription method based on GTV median dose. Unknown at the time of planning, GTV D50% practical application remains difficult. The current study aims at creating a re-prescription predictive model in order to limit conflicting dose value during EPL optimization.

MATERIAL AND METHODS

129 patients planned with EPL algorithm were recalculated with MC. Relative GTV_D50% discrepancies were assessed and influencing parameters identified using wrapper feature selection. Based on best descriptive parameters, predictive nomogram was built from multivariate linear regression. EPL-to-MC OARs near max-dose discrepancies were reported.

RESULTS

The differences in GTV_D50% (median 10%, SD: 9%) between MC and EPL were significantly (p < .001) impacted by the lesion's surface-to-volume ratio and the average relative electronic density of the GTV and the GTV's 15 mm shell. Built upon those parameters, a nomogram (R2 = 0.79, SE = 4%) predicting the GTV_D50% discrepancies was created. Furthermore EPL-to-MC OAR dose tolerance limit showed a strong linear correlation with coefficient range [0.84-0.99].

CONCLUSION

Good prediction on the required re-prescription can be achieved prior planning using our nomogram. Based on strong linear correlation between EPL and MC for OARs near max-dose, further restriction on dose constraints during the EPL optimization can be warranted. This a priori knowledge eases the re-prescription process in limiting conflicting dose value.

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

BACKGROUND

A 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.

METHODS

Twenty-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.

RESULTS

Single 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 R(2) 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.

CONCLUSION

Quantifiable 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.

Dose differences in intensity-modulated radiotherapy plans calculated with pencil beam and Monte Carlo for lung SBRT

For patients with medically inoperable early‐stage non‐small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy, early treatment plans were based on a simpler dose calculation algorithm, the pencil beam (PB) calculation. Because these patients had the longest treatment follow‐up, identifying dose differences between the PB calculated dose and Monte Carlo calculated dose is clinically important for understanding of treatment outcomes. Previous studies found significant dose differences between the PB dose calculation and more accurate dose calculation algorithms, such as convolution‐based or Monte Carlo (MC), mostly for three‐dimensional conformal radiotherapy (3D CRT) plans. The aim of this study is to investigate whether these observed dose differences also exist for intensity‐modulated radiotherapy (IMRT) plans for both centrally and peripherally located tumors. Seventy patients (35 central and 35 peripheral) were retrospectively selected for this study. The clinical IMRT plans that were initially calculated with the PB algorithm were recalculated with the MC algorithm. Among these paired plans, dosimetric parameters were compared for the targets and critical organs. When compared to MC calculation, PB calculation overestimated doses to the planning target volumes (PTVs) of central and peripheral tumors with different magnitudes. The doses to 95% of the central and peripheral PTVs were overestimated by 9.7%±5.6% and 12.0%±7.3%, respectively. This dose overestimation did not affect doses to the critical organs, such as the spinal cord and lung. In conclusion, for NSCLC treated with IMRT, dose differences between the PB and MC calculations were different from that of 3D CRT. No significant dose differences in critical organs were observed between the two calculations.

PACS number: 87.53.Ly