Feasibility of Estimating Patient-Specific Dose Verification Results Directly from Linear Accelerator Log Files in Volumetric Modulated Arc Therapy

Kosaka, Kengo; Tanooka, Masao; Doi, Hiroshi; Inoue, H.; Tarutani, K.; Suzuki, Hitomi; Takada, Yasuhiro; Kamikonya, Norihiko; Hirota, Shun

doi:10.4236/ijmpcero.2016.54031

Cited by 6 publications

(11 citation statements)

References 13 publications

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“…Standard deviation (SD) of the dGPR was calculated as a function of pGPR. 7)] for each tolerance. Red, blue, and green lines are data of the 3%/3-mm, 3%/2-mm, and 2%/2-mm tolerances.…”

Section: C Prediction Of Gamma Passing Ratementioning

confidence: 99%

“…Recently, a variety of attempts have been made to estimate the IM complexity and to investigate their relationship between the IM and gamma passing rate (GPR), which may potentially help to omit the patient-specific QA and to improve the efficiency of the clinical QA practice. A complexity metric (CM) that may be related to the GPR has been proposed by several authors, [3][4][5][6][7][8][9][10] resulting in limited predictability of the GPR. Deep learning (DL)-based prediction showed a good performance for the planar dose distribution measured using the film.…”

Section: Introductionmentioning

confidence: 99%

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Predictive gamma passing rate for three‐dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model

Shiba¹,

Saito²,

Furumi³

et al. 2020

Medical Physics

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Purpose We aim to develop a method to predict the gamma passing rate (GPR) of a three‐dimensional (3D) dose distribution measured by the Delta4 detector system using the dose uncertainty potential (DUP) accumulation model. Methods Sixty head‐and‐neck intensity‐modulated radiation therapy (IMRT) treatment plans were created in the XiO treatment planning system. All plans were created using nine step‐and‐shoot beams of the ONCOR linear accelerator. Verification plans were created and measured by the Delta4 system. The planar DUP (pDUP) manifesting on a field edge was generated from the segmental aperture shape with a Gaussian folding on the beam's‐eye view. The DUP at each voxel (u) was calculated by projecting the pDUP on the Delta4 phantom with its attenuation considered. The learning model (LM), an average GPR as a function of the DUP, was approximated by an exponential function aGPRu=e-qu to compensate for the low statistics of the learning data due to a finite number of the detectors. The coefficient q was optimized to ensure that the difference between the measured and predicted GPRs (dGPR) was minimized. The standard deviation (SD) of the dGPR was evaluated for the optimized LM. Results It was confirmed that the coefficient q was larger for tighter tolerance. This result corresponds to the expectation that the attenuation of the aGPRu will be large for tighter tolerance. The pGPR and mGPR were observed to be proportional for all tolerances investigated. The SD of dGPR was 2.3, 4.1, and 6.7% for tolerances of 3%/3 mm, 3%/2 mm, 2%/2 mm, respectively. Conclusion The DUP‐based predicting method of the GPR was extended to 3D by introducing DUP attenuation and an optimized analytical LM to compensate for the low statistics of the learning data due to a finite number of detector elements. The precision of the predicted GPR is expected to be improved by improving the LM and by involving other metrics.

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“…Standard deviation (SD) of the dGPR was calculated as a function of pGPR. 7)] for each tolerance. Red, blue, and green lines are data of the 3%/3-mm, 3%/2-mm, and 2%/2-mm tolerances.…”

Section: C Prediction Of Gamma Passing Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive gamma passing rate for three‐dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model

Shiba¹,

Saito²,

Furumi³

et al. 2020

Medical Physics

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“…Specifically, the correlation of the substitute with the gamma passing rate (GPR) is examined in order to maintain the criteria of current clinical QA practice. Complexity metrics of IMRT were proposed and investigated by several authors in recent years . The modulation complexity score (MCS) developed by McNiven et al .…”

Section: Introductionmentioning

confidence: 99%

“…The researchers investigated the correlation between the MCS and the delivered dose measured by a two‐dimensional (2D) diode array in terms of beam parameters such as the number of segments and monitor units (MUs). Other authors reported correlations between the MCS and GPR in volumetric‐modulated arc therapy (VMAT) . Although there are various other approaches, there is no satisfactory technique to substitute for the gamma analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Complexity metrics of IMRT were proposed and investigated by several authors in recent years. [7][8][9][10][11][12][13][14] The modulation complexity score (MCS) developed by McNiven et al 7 characterizes the average plan complexity using the beam parameters of leaf positions, degree of irregularity in the field shape, segment weight, and area. The researchers investigated the correlation between the MCS and the delivered dose measured by a twodimensional (2D) diode array in terms of beam parameters such as the number of segments and monitor units (MUs).…”

Section: Introductionmentioning

confidence: 99%

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Predictive gamma passing rate by dose uncertainty potential accumulation model

et al. 2018

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Purpose Intensity‐modulated radiation therapy (IMRT) utilizes many small fields for producing a uniform dose distribution. Therefore, there are many field junctions in the target region, and resulting dose uncertainties are accumulated. However, such accumulation of the dose uncertainty has not been implemented in the current practice of IMRT dose verification. The purpose of this study is to develop a method to predict the gamma passing rate (GPR) using a dose uncertainty accumulation model. Methods Thirty‐three intensity‐modulated (IM) beams for head‐and‐neck cases with step‐and‐shoot techniques were used in this study. The treatment plan was created using the XiO treatment planning system (TPS). The IM beam was produced by the ONCOR Impression Plus linear accelerator. MapCHECK was used to measure the dose distribution. The distribution of a dose uncertainty potential (DUP) was generated by in‐house software that accumulated field shapes weighted by a segmental monitor unit, followed by Gaussian folding. The width of the Gaussian was determined from the width of the lateral penumbra. The dose difference between the calculated and measured doses was compared with the estimated DUP at each point. The GPR of each beam was predicted for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances by its own DUP histogram and a GPR‐vs‐DUP correlation of other beams using the leave‐one‐out cross‐validation method. The predicted GPR was compared with the measured GPR to evaluate the performance of this prediction method. The criteria for the predicted GPR corresponding to a measured GPR ≥ 90% were estimated to examine the feasibility of estimating the measured GPR by this GPR prediction method. Results The DUP was confirmed to have proportionality to the standard deviation (SD) of the dose difference. The SDs of the difference between the measured and predicted GPRs were 3.1, 1.7, and 1.4% for 2%/2‐mm, 3%/2‐mm, and 3%/3‐mm tolerances, respectively. The criteria of the predicted GPR corresponding to the measured GPR ≥ 90% were 94.1 and 95.0% with confidence levels of 99 and 99.9%, respectively. Conclusion In this study, we confirmed the good proportionality between the dose difference and the estimated DUP. The results showed a feasibility to predict the dose difference from DUP as estimated by a DUP accumulation model. The predicted GPR developed in this study showed good accuracy for planar dose distributions of head and neck IMRT. The prediction method developed in this study is considered to be feasible as a substitute for the current practice of measurement‐based verification of the dose distribution with gamma analysis.

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A method for patient‐specific DVH verification using a high‐sampling‐rate log file in an Elekta linac

Nishiyama

Takemura

2022

J Applied Clin Med Phys

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We have proposed a method for patient-specific dose-volume histogram (DVH) verification using a 40-ms high-sampling-rate log file (HLF) available in an Elekta linac. Ten prostate volumetric-modulated arc therapy plans were randomly selected, and systematic leaf position errors of ±0.2, ±0.4, or ±0.8 mm were added to the 10 plans, thereby producing a total of 70 plans. An RTP file was created by interpolating each leaf position in the HLF to obtain values at each control point, which is subsequently exported to a treatment planning system. The isocenter dose calculated by the HLF-based plan to a phantom (Delta4 Phantom+) was compared to that measured by the diode in the phantom in order to evaluate the accuracy of the HLF-based dose calculation. The D95 of the planning target volume (PTV) was also compared between the HLFbased plans and the original plans with the systematic leaf position errors, the latter being referred to as theory-based plans. Sensitivities of the DVH parameters in the target, the rectum, and the bladder were also calculated with the varied systematic leaf position errors. The relative differences in the isocenter doses between the HLF-based calculations and the measurements among the 70 plans were 0.21% ± 0.67% (SD). The maximum relative differences in PTV D95 between the HLF-based and the theory-based plans among the 70 cases were 0.11%. The patient-specific DVH verification method detected a change in the target DVH parameters of less than 1% when the systematic leaf position error was ±0.2 mm. It is therefore suggested that the proposed DVH verification method may simplify patient-specific dose quality assurance procedures without compromising accuracy and sensitivity.

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Feasibility of Estimating Patient-Specific Dose Verification Results Directly from Linear Accelerator Log Files in Volumetric Modulated Arc Therapy

Cited by 6 publications

References 13 publications

Predictive gamma passing rate for three‐dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model

Predictive gamma passing rate for three‐dimensional dose verification with finite detector elements via improved dose uncertainty potential accumulation model

Predictive gamma passing rate by dose uncertainty potential accumulation model

A method for patient‐specific DVH verification using a high‐sampling‐rate log file in an Elekta linac

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