Evaluation of dose prediction errors and optimization convergence errors of deliverable-based head-and-neck IMRT plans computed with a superposition/convolution dose algorithm

Abstract

The purpose of this study is to evaluate dose prediction errors ͑DPEs͒ and optimization convergence errors ͑OCEs͒ resulting from use of a superposition/convolution dose calculation algorithm in deliverable intensity-modulated radiation therapy ͑IMRT͒ optimization for head-and-neck ͑HN͒ patients. Thirteen HN IMRT patient plans were retrospectively reoptimized. The IMRT optimization was performed in three sequential steps: ͑1͒ fast optimization in which an initial nondeliverable IMRT solution was achieved and then converted to multileaf collimator ͑MLC͒ leaf sequences; ͑2͒ mixed deliverable optimization that used a Monte Carlo ͑MC͒ algorithm to account for the incident photon fluence modulation by the MLC, whereas a superposition/convolution ͑SC͒ dose calculation algorithm was utilized for the patient dose calculations; and ͑3͒ MC deliverable-based optimization in which both fluence and patient dose calculations were performed with a MC algorithm. DPEs of the mixed method were quantified by evaluating the differences between the mixed optimization SC dose result and a MC dose recalculation of the mixed optimization solution. OCEs of the mixed method were quantified by evaluating the differences between the MC recalculation of the mixed optimization solution and the final MC optimization solution. The results were analyzed through dose volume indices derived from the cumulative dose-volume histograms for selected anatomic structures. Statistical equivalence tests were used to determine the significance of the DPEs and the OCEs. Furthermore, a correlation analysis between DPEs and OCEs was performed. The evaluated DPEs were within Ϯ2.8% while the OCEs were within 5.5%, indicating that OCEs can be clinically significant even when DPEs are clinically insignificant. The full MC-dose-based optimization reduced normal tissue dose by as much as 8.5% compared with the mixed-method optimization results. The DPEs and the OCEs in the targets had correlation coefficients greater than 0.71, and there was no correlation for the organs at risk. Because full MC-based optimization results in lower normal tissue doses, this method proves advantageous for HN IMRT optimization.

Dosimetrischer Vergleich eines erweiterten Collapsed-Cone-Algorithmus mit einem neuen kommerziell verfügbaren Monte-Carlo-Algorithmus anhand klinisch relevanter Fälle

“…The DVH analysis revealed some clinically relevant deviations of more than 5%. This is in agreement with literature comparing research MC implementations vs. CC [14,18,21,26,29,28].…”

“…From obtained dose values, the percentage dose prediction error (%DPE) was calculated [7,26]. Assuming that the MC algorithm is more accurate, %DPE ecc was defined as (3) where x is the given dose-volume index (maximum, minimum, or mean dose).…”

The effect of gantry spacing resolution on plan quality in a single modulated arc optimization

“…The compared DIs were normalized to the reference DIs. This normalization eases the comparison, especially as in the studied case, where different patients have different prescription doses ( 16 ) and different OAR DIs, respectively. As a result, if a normalized DI has a value of one, it means that the compared DIs are equal; where as, if the value of the index is greater than one, it means that the compared DI is greater than the reference DI and smaller otherwise.…”

“…The metric used in the comparison was based on DIs. ( 16 ) Briefly, DIs are points on the DVHs corresponding to certain fractional anatomical volume coverage (e.g., PTV ${\text{DI}}_{95\%}$ is the point on the PTV DVH corresponding the 95% of the PTV volume). In the prostate cases, the evaluated DIs were PTV ${\mathrm{normalD}}_{95\%}$, rectum and bladder ${\mathrm{normalD}}_{15\%}$, ${\mathrm{normalD}}_{25\%}$, and ${\mathrm{normalD}}_{40\%}$.…”

Lung Dose for Minimally Moving Thoracic Lesions Treated With Respiration Gating

“…For each patient, an intensity-modulated radiotherapy (IMRT) deliverable (39)(40)(41)(42), optimized at the MidVen phase, was performed by Pinnacle's collapsed cone convolution dose calculation algorithm, which has been shown to be in agreement with Monte Carlo dose calculations in heterogeneous media (43)(44)(45). Treatment plans consisted of 5 to 11 noncoplanar 6-MV and/or 18-MV beams.…”