Correlation Between Gamma Index Passing Rate and Clinical Dosimetric Difference for Pre-treatment 2D and 3D Volumetric Modulated Arc Therapy Dosimetric Verification
Overview
Affiliations
Objective: To investigate comparatively the percentage gamma passing rate (%GP) of two-dimensional (2D) and three-dimensional (3D) pre-treatment volumetric modulated arc therapy (VMAT) dosimetric verification and their correlation and sensitivity with percentage dosimetric errors (%DE).
Methods: %GP of 2D and 3D pre-treatment VMAT quality assurance (QA) with different acceptance criteria was obtained by ArcCHECK® (Sun Nuclear Corporation, Melbourne, FL) for 20 patients with nasopharyngeal cancer (NPC) and 20 patients with oesophageal cancer. %DE were calculated from planned dose-volume histogram (DVH) and patients' predicted DVH calculated by 3DVH® software (Sun Nuclear Corporation). Correlation and sensitivity between %GP and %DE were investigated using Pearson's correlation coefficient (r) and receiver operating characteristics (ROCs).
Results: Relatively higher %DE on some DVH-based metrics were observed for both patients with NPC and oesophageal cancer. Except for 2%/2 mm criterion, the average %GPs for all patients undergoing VMAT were acceptable with average rates of 97.11% ± 1.54% and 97.39% ± 1.37% for 2D and 3D 3%/3 mm criteria, respectively. The number of correlations for 3D was higher than that for 2D (21 vs 8). However, the general correlation was still poor for all the analysed metrics (9 out of 26 for 3D 3%/3 mm criterion). The average area under the curve (AUC) of ROCs was 0.66 ± 0.12 and 0.71 ± 0.21 for 2D and 3D evaluations, respectively.
Conclusions: There is a lack of correlation between %GP and %DE for both 2D and 3D pre-treatment VMAT dosimetric evaluation. DVH-based dose metrics evaluation obtained from 3DVH will provide more useful analysis.
Advances In Knowledge: Correlation and sensitivity of %GP with %DE for VMAT QA were studied for the first time.
Feasibility study of structural similarity index for patient-specific quality assurance.
Lee J, Park H, Kang Y J Appl Clin Med Phys. 2024; 26(3):e14591.
PMID: 39625100 PMC: 11905251. DOI: 10.1002/acm2.14591.
Han C, Zhang J, Yu B, Zheng H, Wu Y, Lin Z Radiat Oncol. 2023; 18(1):116.
PMID: 37434171 PMC: 10334519. DOI: 10.1186/s13014-023-02311-7.
Evaluation of the dataset quality in gamma passing rate predictions using machine learning methods.
Quintero P, Benoit D, Cheng Y, Moore C, Beavis A Br J Radiol. 2023; 96(1147):20220302.
PMID: 37129359 PMC: 10321263. DOI: 10.1259/bjr.20220302.
DIR-based models to predict weekly anatomical changes in head and neck cancer proton therapy.
Zhang Y, McGowan Holloway S, Zoe Wilson M, Alshaikhi J, Tan W, Royle G Phys Med Biol. 2022; 67(9).
PMID: 35316795 PMC: 10437002. DOI: 10.1088/1361-6560/ac5fe2.
Lee Y, Kim Y J Radiosurg SBRT. 2021; 7(4):295-307.
PMID: 34631231 PMC: 8492049.