Pretreatment patient‐specific IMRT quality assurance: A correlation study between gamma index and patient clinical dose volume histogram

This study investigated the impact of beam complexities on planar quality assurance and plan quality robustness by introducing MLC errors in intensity‐modulate radiation therapy. Forty patients' planar quality assurance (QA) plans were enrolled in this study, including 20 dynamic MLC (DMLC) IMRT plans and 20 static MLC (SMLC) IMRT plans. The total beam numbers were 150 and 160 for DMLC and SMLC, respectively. Six different magnitudes of MLC errors were introduced to these beams. Gamma pass rates were calculated by comparing error‐free fluence and error‐induced fluence. The plan quality variation was acquired by comparing PTV coverage. Eight complexity scores were calculated based on the beam fluence and the MLC sequence. The complexity scores include fractal dimension, monitor unit, modulation index, fluence map complexity, weighted average of field area, weighted average of field perimeter, and small aperture ratio false(<5cm2 and<50 cm2false). The Spearman's rank correlation coefficient was calculated to analyze the correlation between these scores and gamma pass rate and plan quality variation. For planar QA, the most significant complexity index was fractal dimension for DMLC false(p=−0.40false) and weighted segment area for SMLC false(p=0.27false) at low magnitude MLC error. For plan quality, the most significant complexity index was weighted segment perimeter for DMLC false(p=0.56false) and weighted segment area for SMLC false(p=0.497false) at low magnitude MLC error. The sensitivity of planar QA was weakly associated with the field complexity with low magnitude MLC error, but the plan quality robustness was associated with beam complexity. Plans with simple beams were more robust to MLC error.PACS number(s): 87.55

Section: Discussionmentioning

confidence: 99%

Are simple IMRT beams more robust against MLC error? Exploring the impact of MLC errors on planar quality assurance and plan quality for different complexity beams

Wang

Jin

Peng

et al. 2016

“…Although it is well documented that a single quality assurance check cannot detect all errors, 12 , 13 widespread debate has not led to a consensus on which combination of quality assurance checks would provide the most robust, efficient, economic resilience against treatment delivery errors 14 , 15 , 16 . Furthermore, it has been suggested that errors detected pretreatment may not be representative of errors on‐treatment 13 , 17 or, indeed, be clinically relevant 18 , 19 , 20 …”

Section: Introductionmentioning

confidence: 99%

Correlation of phantom‐based and log file patient‐specific QA with complexity scores for VMAT

Agnew

Irvine

McGarry

2014

The motivation for this study was to reduce physics workload relating to patient‐specific quality assurance (QA). VMAT plan delivery accuracy was determined from analysis of pre‐ and on‐treatment trajectory log files and phantom‐based ionization chamber array measurements. The correlation in this combination of measurements for patient‐specific QA was investigated. The relationship between delivery errors and plan complexity was investigated as a potential method to further reduce patient‐specific QA workload. Thirty VMAT plans from three treatment sites — prostate only, prostate and pelvic node (PPN), and head and neck (H&N) — were retrospectively analyzed in this work. The 2D fluence delivery reconstructed from pretreatment and on‐treatment trajectory log files was compared with the planned fluence using gamma analysis. Pretreatment dose delivery verification was also carried out using gamma analysis of ionization chamber array measurements compared with calculated doses. Pearson correlations were used to explore any relationship between trajectory log file (pretreatment and on‐treatment) and ionization chamber array gamma results (pretreatment). Plan complexity was assessed using the MU/ arc and the modulation complexity score (MCS), with Pearson correlations used to examine any relationships between complexity metrics and plan delivery accuracy. Trajectory log files were also used to further explore the accuracy of MLC and gantry positions. Pretreatment 1%/1 mm gamma passing rates for trajectory log file analysis were 99.1% (98.7%–99.2%), 99.3% (99.1%–99.5%), and 98.4% (97.3%–98.8%) (median (IQR)) for prostate, PPN, and H&N, respectively, and were significantly correlated to on‐treatment trajectory log file gamma results (normalR=0.989,p<0.001). Pretreatment ionization chamber array (2%/2 mm) gamma results were also significantly correlated with on‐treatment trajectory log file gamma results (normalR=0.623,p<0.001). Furthermore, all gamma results displayed a significant correlation with MCS (normalR>0.57,p<0.001), but not with MU/arc. Average MLC position and gantry angle errors were 0.001±0.002.15emmm and 0.025°±0.008° over all treatment sites and were not found to affect delivery accuracy. However, variability in MLC speed was found to be directly related to MLC position accuracy. The accuracy of VMAT plan delivery assessed using pretreatment trajectory log file fluence delivery and ionization chamber array measurements were strongly correlated with on‐treatment trajectory log file fluence delivery. The strong correlation between trajectory log file and phantom‐based gamma results demonstrates potential to reduce our current patient‐specific QA. Additionally, insight into MLC and gantry position accuracy through trajectory log file analysis and the strong correlation between gamma analysis results and the MCS could also provide further methodologies to both optimize the VMAT planning and QA process.PACS number: 87.53.Bn, 87.55.Kh, 87.55.Qr

“…The TG‐186 of the AAPM (36) recognize the necessity of define a dose accuracy tolerance requirements considering a gamma‐index metrics for brachytherapy as an important first step, proposing an incipient criterion of

2 % / 2 mm

with a 99% pass rate for clinically relevant points. On the other hand, a growing amount of literature is warning us about how the gamma analysis may overlook important errors in the TPS causing nonnegligible deviations in the DVH 37 , 38 , 39 , 40 . It should be noted that the ODIN problems here described, correspond to a systematic error similar to those tested (real or induced for the purpose of sensitivity analysis) in some publications cited above.…”

Section: Discussionmentioning

confidence: 74%

Brachytherapy treatment planning commissioning: effect of the election of proper bibliography and finite size of TG‐43 input data on standard treatments

Valdés

Piriz

Lozano

2015

The aim of this work is to evaluate the performance of a commercial brachytherapy treatment planning system (TPS) with TG‐43 Vendors Input Data (VID), analyze possible discrepancies with respect to a proper reference source and its implications for standard treatments, and judge the effectiveness of certain widespread recommended quality controls to find potential errors related with the interpolations of TG‐43 VID tables. The TPS evaluated was a BrachyVision 8.6 loaded with TG‐43 VID for a VariSource high‐dose‐rate 192Ir source (Vs2000). The reference data chosen were the TG‐43 data published in the literature. In the first step, we compared TG‐43 VID with respect to the chosen reference data. Next, we used percent dose‐rate differences in a point array matrix to compare the outcomes of the TPS on standard treatment setup with respect to an in‐house developed program (MATLAB R2009a‐based) loaded with the chosen full TG‐43 reference data. The cases with major discrepancies were evaluated using the gamma‐index analysis. The comparison with the reference data indicated a lack of sample in the angles between near to the tip (between 165<θ<180) and cable (0<θ<15) of the F(r,θ)VID, which causes a dose underestimation of approximately 17% in the investigated points due to inaccurate interpolations. The differences over 2% encompassed approximately 17% of the surrounding source volume. These results have special relevance in treatment using one applicator with a few dwell steps or in Fletcher treatments where 10% dose underestimates were identified within the tumor or in organs at risk, respectively. Our results suggest that the differences found in the TPS under study are created by a lack of information on the angles in high‐gradient zones in the F(r,θ)VID, which generates important differences in dosimetric results. In contrast, the gamma analysis shows very good results (between 90% and 100% of passed points) in the analyzed treatments (one dwell and Fletcher). Further studies are required to exclude the possibility of finding noticeable effects in the DVH of treatment plans caused by the discrepancies here described. To achieve more strict control over the TPS dose‐rate calculation, we recommend using QA test thinking in a source with nonaxial symmetry, adding a control point on the angles of the high‐dose gradient zones (e.g., between 0° and 15° and between 165° and 180°). More studies are required to achieve full understanding of the clinical implication of such discrepancies.PACS number: 87.55.Qr