Abstract

PurposeGiven tissue inhomogeneity and lack of backscatter media, superficial brachytherapy necessitates more accurate dosimetry than TG-43 formalism. However, the introduction of modern model-based dose calculation algorithms into clinical practice should be carefully evaluated. The aim of this work was to compare dose distributions calculated with TG-43 and advanced collapsed cone engine (ACE) algorithms for individual multi-catheter moulds, and investigate the impact of target size and the lack of bolus to differences between plans.Material and methodsEleven treatment plans for individual mould multi-catheter high-dose-rate brachytherapy (IMM HDR) were selected for retrospective analysis. All treatment plans were initially calculated with TG-43 formula and re-calculated using ACE algorithm. Plan re-calculation with ACE was repeated for each plan in order to assess the impact of bolus. To evaluate differences between TG-43 and ACE dose distributions, dose-volume histogram (DVH) parameters for each ROI were compared. Dmax (maximal point dose), D0.1cc, and D2cc were calculated for each risk’s organ (OARs) and for external contour. For clinical target volume (CTV), D98, D90, D50, CTV coverage (CTV-V100), and dose delivered to reference point were compared between the plans.ResultsA significantly lower values (p < 0.05) of CTV parameters were observed for treatment plans calculated with ACE algorithm comparing to TG-43. Further analysis showed that differences between CTV-V100 for ACE and TG-43 plans depended on CTV volume. Dosimetric parameters for OARs were significantly lower in ACE plans than those of TG-43. Only D2cc for external and D0.1cc for both eye lenses in ACE plans were insignificantly different comparing to TG-43 plans.ConclusionsResults show that differences between dosimetric parameters are statistically significant. However, their clinical relevance is still undetermined. Careful re-evaluation of the clinical results based on long-term research on TG-43 is necessary to safely introduce modern algorithms to clinical practice.

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