Changes in Patterns of Intensity-modulated Radiotherapy Verification and Quality Assurance in the UK

Abolaban, Fouad A.; Zaman, S; Cashmore, J.; Nisbet, Andrew; Clark, Catharine H.

doi:10.1016/j.clon.2016.01.013

Cited by 20 publications

(23 citation statements)

References 8 publications

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“…A UK survey of patient‐specific QA practice noted that recent approaches have reduced the role of measurement and it reported a move away from individual patient measurement, especially in prostate treatment . To solve the problems related to the workload, a practical and precise predictive method has been reported.…”

Section: Introductionmentioning

confidence: 99%

“…A UK survey of patient-specific QA practice noted that recent approaches have reduced the role of measurement and it reported a move away from individual patient measurement, especially in prostate treatment. 8 To solve the problems related to the workload, a practical and precise predictive method has been reported. The prediction models reported in previous studies have used the 3D predicted dose, with the gamma evaluation of the planar dose distribution conducted using criteria greater than 3%(global)/3 mm.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance

et al. 2018

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance

et al. 2018

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“…The ΔD of 3% was chosen for this work because it is very widely recommended and used. Recent surveys suggested that 3%/3 mm are currently the most commonly used gamma evaluation criteria, and the AAPM's task group report on IMRT commissioning (TG‐119) used 3%/3 mm and the AAPM's more recent task group report on modulated radiotherapy quality assurance (TG‐218) recommended 3%/2 mm. Investigations of cranial and extra‐cranial stereotactic radiotherapy have reported the use of 3%/2 mm, 3%/1.5 mm, 3%/1 mm and 3%/0.3 mm criteria.…”

Section: Methodsmentioning

confidence: 99%

Technical note: A modified gamma evaluation method for dose distribution comparisons

Kairn

Trapp

et al. 2019

J Applied Clin Med Phys

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Purpose In this work we have developed a novel method of dose distribution comparison, the inverse gamma (IG) evaluation, by modifying the commonly used gamma evaluation method. Methods The IG evaluation calculates the gamma criteria (dose difference criterion, ΔD, or distance‐to‐agreement criterion, Δd) that are needed to achieve a predefined pass rate or gamma agreement index (GAI). In‐house code for evaluating IG with a fixed ΔD of 3% was developed using Python (v3.5.2) and investigated using treatment plans and measurement data from 25 retrospective patient specific quality assurance tests (53 individual arcs). Results It was found that when the desired GAI was set to 95%, approximately three quarters of the arcs tested were able to achieve Δd within 1 mm (mean Δd: 0.7 ± 0.5 mm). The mean Δd required in order for all points to pass the gamma evaluation (i.e., GAI = 100%) was 4.5 ± 3.1 mm. The possibility of evaluating IG by fixing the Δd or ΔD/Δd, instead of fixing the ΔD at 3%, was also investigated. Conclusion The IG method and its indices have the potential to be implemented clinically to quantify the minimum dose and distance criteria based on a specified GAI. This method provides additional information to augment standard gamma evaluation results during patient specific quality assurance testing of individual treatment plans. The IG method also has the potential to be used in retrospective audits to determine an appropriate set of local gamma criteria and action levels based on a cohort of patient specific quality assurance plans.

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“…Therefore, reducing the effort for IMRT preparation attracts attention in these contexts. In practice, the number of institutions performing the measurement‐based patient‐specific QA is dropping . For example, the verification measurement for specific treatment sites such as the prostate or breast, in which a successful result is highly expected, has been reduced or stopped in some institutions.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, the number of institutions performing the measurement-based patient-specific QA is dropping. 1 For example, the verification measurement for specific treatment sites such as the prostate or breast, in which a successful result is highly expected, has been reduced or stopped in some institutions. However, there is a need to develop a verification method that can substitute the currently used gamma analysis 2 without losing the safety level in the IMRT delivery.…”

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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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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Changes in Patterns of Intensity-modulated Radiotherapy Verification and Quality Assurance in the UK

Cited by 20 publications

References 8 publications

A deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance

A deep learning‐based prediction model for gamma evaluation in patient‐specific quality assurance

Technical note: A modified gamma evaluation method for dose distribution comparisons

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

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