On the new metrics for IMRT QA verification

García-Romero, Alejandro; Hernández-Vitoria, Araceli; Millan-Cebrian, Esther; Alba-Escorihuela, Verónica; Serrano-Zabaleta, S.; Ortega-Pardina, Pablo

doi:10.1118/1.4964796

Cited by 7 publications

(11 citation statements)

References 28 publications

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“…Some authors have proposed DVH‐based QA metrics as a response to these criticisms . A number of studies have proposed or evaluated alternative dose comparison techniques, or assessed variation in behavior of gamma evaluation for both local and global gamma evaluations with different criteria and lower dose thresholds (LDT) …”

Section: Introductionmentioning

confidence: 99%

“…However, although optimal gamma parameters and performance of alternative metrics were tested in the literature, few studies have provided suggestions to radiation oncology treatment centers looking to compare between these alternative dose comparison techniques, between different ∆D and DTA criteria pairs (for local gamma evaluation techniques) or LDT. This study evaluated the D&C, MADD, local and global gamma dose comparison techniques across a variety of agreement criteria and LDT, for 429 existing PSQA measurements obtained using ArcCheck helical diode array (Sun Nuclear Corporation, Melbourne, FL), including 57 beams that failed departmental quality assurance tests, for both HT and VMAT treatments.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of dose comparison techniques for patient‐specific quality assurance in radiation therapy

Tang

Cassim

et al. 2019

J Applied Clin Med Phys

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PurposeGamma evaluation is the most commonly used technique for comparison of dose distributions for patient‐specific pretreatment quality assurance in radiation therapy. Alternative dose comparison techniques have been developed but not widely implemented. This study aimed to compare and evaluate the performance of several previously published alternatives to the gamma evaluation technique, by systematically evaluating a large number of patient‐specific quality assurance results.MethodsThe agreement indices (or pass rates) for global and local gamma evaluation, maximum allowed dose difference (MADD) and divide and conquer (D&C) techniques were calculated using a selection of acceptance criteria for 429 patient‐specific pretreatment quality assurance measurements. Regression analysis was used to quantify the similarity of behavior of each technique, to determine whether possible variations in sensitivity might be present.ResultsThe results demonstrated that the behavior of D&C gamma analysis and MADD box analysis differs from any other dose comparison techniques, whereas MADD gamma analysis exhibits similar performance to the standard global gamma analysis. Local gamma analysis had the least variation in behavior with criteria selection. Agreement indices calculated for 2%/2 mm and 2%/3 mm, and 3%/2 mm and 3%/3 mm were correlated for most comparison techniques.ConclusionRadiation oncology treatment centers looking to compare between different dose comparison techniques, criteria or lower dose thresholds may apply the results of this study to estimate the expected change in calculated agreement indices and possible variation in sensitivity to delivery dose errors.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of dose comparison techniques for patient‐specific quality assurance in radiation therapy

Tang

Cassim

et al. 2019

J Applied Clin Med Phys

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“…Although gamma analysis is a simple and easy-to-implement approach, limitations of the gamma index have been reported by many groups. [13][14][15][16][17][18][19][20] These primarily include the following: insensitive to the dose error; no correlation between the gamma analysis results and clinical dose errors; the gamma map might indicate the location of failing points, but it does not provide information regarding the types of errors it reveals (such as absolute dose error or dose gradient error); the gamma passing rate largely depends on the criteria used that could vary from 7%/4 mm in Imaging and Radiation Oncology Core (IROC) evaluation to 3%/2 mm in the latest TG218 report and 1%/2 mm for the identification of robust beams. To overcome these limitations, efforts have been made to improve gamma analysis by either incorporating other information, such as dose volume histogram (DVH) metrics and relative biological effectiveness (RBE) calculations, into the evaluation or adopting region-specific gamma criteria.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the structural SIMilarity (SSIM) index as a quantitative evaluation tool for dose distribution error detection

et al. 2020

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Purpose: To apply an imaging metric of the structural SIMilarity (SSIM) index to the radiotherapy dose verification field and evaluate its capability to reveal the different types of errors between two dose distributions. Method: The SSIM index consists of three sub-indices: luminance, contrast, and structure. Given two images, luminance analysis compares the local mean result, contrast analysis compares the local standard deviation, and the structure index represents the local Pearson correlation. Three test error patterns (absolute dose error, dose gradient error, and dose structure error) were designed to characterize the response of SSIM and its sub-indices and establish the correlation between the indices and different dose error types. After establishing the correlation, four radiotherapy plans (one MLC picket-fence test plan, one brain stereotactic radiotherapy plan, and two head-and-neck plans) were tested by computing each index and compared with the gamma analysis results to determine their similarities and differences. Results: Among the three test error patterns, the luminance index decreased from 1 to 0.1 when the absolute dose agreement fell from 100% to 5%, the contrast index decreased from 1 to 0.36 when the dose gradient agreement fell from 100% to 10%, and the structure index decreased from 1 to 0.23 when the periodical dose pattern shifted (leading to a lower correlation). Thus, the luminance, contrast and structure index can detect the absolute dose error, gradient discrepancy, and dose structure error, respectively. For the four clinical cases, the sub-indices can reveal the type of error when gamma analysis only provided limited information. Conclusions: The correlation between the subcomponents of the SSIM index and the error types of the dose distribution were established. The SSIM index provides additional error information compared to that provided by gamma analysis.

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“…The capabilities of producing DVH of the Delta4DVH Anatomy 3D QA system (Scandidos, Uppsala), and both MapCHECK 2 and the ArcCHECK with 3DVH system (Sun Nuclear, Melbourne) have been evaluated in previous studies . New metrics for IMRT QA verification were explored in a study that utilized the COMPASS system (IBA Dosimetry, Bartlett, Tennessee) to incorporate pretreatment DVH into tumor control probability (TCP) and normal tissue complication probability (NTCP) models …”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16] New metrics for IMRT QA verification were explored in a study that utilized the COMPASS system (IBA Dosimetry, Bartlett, Tennessee) to incorporate pretreatment DVH into tumor control probability (TCP) and normal tissue complication probability (NTCP) models. 17 TCP provides additional insight to plan quality as it is associated with the clinically observed tumor control rates. Similar association exists between NTCP and radiation-induced toxicity to organs at risk (OAR).…”

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confidence: 99%

Incorporating biological modeling into patient‐specific plan verification

Alexandrian

Mavroidis

Narayanasamy

et al. 2020

J Applied Clin Med Phys

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Purpose Dose–volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods. Methods A commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient‐specific QA measurements on a phantom. Biological modeling was performed on the DVHs produced by both the treatment planning system and the quality assurance system. Results The complication‐free tumor control probability, P+, has been calculated for previously treated intensity modulated radiotherapy (IMRT) patients with diseases in the following sites: brain (−3.9% ± 5.8%), head‐neck (+4.8% ± 8.5%), lung (+7.8% ± 1.3%), pelvis (+7.1% ± 12.1%), and prostate (+0.5% ± 3.6%). Conclusion Dose measurements on a phantom can be used for pretreatment estimation of tumor control and normal tissue complication probabilities. Results in this study show how biological modeling can be used to provide additional insight about accuracy of delivery during pretreatment verification.

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On the new metrics for IMRT QA verification

Cited by 7 publications

References 28 publications

Analysis of dose comparison techniques for patient‐specific quality assurance in radiation therapy

Analysis of dose comparison techniques for patient‐specific quality assurance in radiation therapy

Implementation of the structural SIMilarity (SSIM) index as a quantitative evaluation tool for dose distribution error detection

Incorporating biological modeling into patient‐specific plan verification

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