Clinical Validation of a Ray-Casting Analytical Dose Engine for Spot Scanning Proton Delivery Systems

Abstract

Purpose: To describe and validate the dose calculation algorithm of an independent second-dose check software for spot scanning proton delivery systems with full width at half maximum between 5 and 14 mm and with a negligible spray component. Methods: The analytical dose engine of our independent second-dose check software employs an altered pencil beam algorithm with 3 lateral Gaussian components. It was commissioned using Geant4 and validated by comparison to point dose measurements at several depths within spread-out Bragg peaks of varying ranges, modulations, and field sizes. Water equivalent distance was used to compensate for inhomogeneous geometry. Twelve patients representing different disease sites were selected for validation. Dose calculation results in water were compared to a fast Monte Carlo code and ionization chamber array measurements using dose planes and dose profiles as well as 2-dimensional–3-dimensional and 3-dimensional–3-dimensional γ-index analysis. Results in patient geometry were compared to Monte Carlo simulation using dose–volume histogram indices, 3-dimensional–3-dimensional γ-index analysis, and inpatient dose profiles. Results: Dose engine model parameters were tuned to achieve 1.5% agreement with measured point doses. The in-water γ-index passing rates for the 12 patients using 3%/2 mm criteria were 99.5% ± 0.5% compared to Monte Carlo. The average inpatient γ-index analysis passing rate compared to Monte Carlo was 95.8% ± 2.9%. The average difference in mean dose to the clinical target volume between the dose engine and Monte Carlo was −0.4% ± 1.0%. For a typical plan, dose calculation time was 2 minutes on an inexpensive workstation. Conclusions: Following our commissioning process, the analytical dose engine was validated for all treatment sites except for the lung or for calculating dose–volume histogram indices involving point doses or critical structures immediately distal to target volumes. Monte Carlo simulations are recommended for these scenarios.

Technical Note: Treatment planning system (TPS) approximations matter — comparing intensity‐modulated proton therapy (IMPT) plan quality and robustness between a commercial and an in‐house developed TPS for nonsmall cell lung cancer (NSCLC)

“…Specifically, we note that an optimal plan derived based on Solo dose calculation engine was not necessarily an optimal plan if it was evaluated using the final dose distribution as calculated by CTPS dose calculation engine. The slight difference between these two dose engines was enough to degrade the quality of Solo plans evaluated using the final dose distributions by CTPS recalculations . Interestingly, we found that the Solo plans were still superior to CTPS plans after plan quality degradation due to recalculation, which confirmed that Solo indeed performed better than CTPS.…”

Beam angle comparison for distal esophageal carcinoma patients treated with intensity‐modulated proton therapy

“…High linear energy transfer (LET) may appear in the distal fall-off regions of a proton beam and may cause unexpected AEs to nearby OARs. At our institution, every plan will receive a second dose and LET calculation [66][67][68] after the plan is generated by our TPS (Eclipse TM ).…”

“…High linear energy transfer (LET) may appear in the distal fall‐off regions of a proton beam and may cause unexpected AEs to nearby OARs. At our institution, every plan will receive a second dose and LET calculation 66–68 after the plan is generated by our TPS (Eclipse TM ). The physicists and the attending physician will then check for overlap of high dose (at least 50% of the prescription dose) and high LET (at least 6 keV/µm) in nearby OARs during the LET‐guided plan evaluation.…”

Intensity‐modulated proton therapy (IMPT) interplay effect evaluation of asymmetric breathing with simultaneous uncertainty considerations in patients with non‐small cell lung cancer

“…A large number of time-consuming computations were needed for this study. In order to speed up the calculation, we migrated our in-house developed treatment planning system (TPS) to a Graphic Processing Unit (GPU)-based computing platform, including the following three components: (a) a modified ray-casting-based dose and linear energy transfer (LET) calculation engine, 59,64 with the enhanced capability to account for inhomogeneity more accurately 65 ; (b) voxel- wise worst-case-based 3D, [10][11][12][13][14][15][16][17]20,[25][26][27][28]66,67,68 4D, 19 and LETguided 26,69 robust optimization; and (c) DVH-band method 20,61,63 to quantify plan robustness. The TPS was highly parallelized using Compute Unified Device Architecture (CUDA).…”