Universal evaluation of MLC models in treatment planning systems based on a common set of dynamic tests

Saez, Jordi; Bar-Deroma, R.; Bogaert, Evelien; Cayez, R.; Chow, Tom; Clark, Catharine H.; Esposito, Marco; Feygelman, Vladimir; Monti, A.F.; García-Miguel, J.; Gershkevitsh, E.; Goossens, Jo; Herrero, Carmen; Hussein, Mohammad; Khamphan, C.; Kierkels, Roel G J; Lechner, Wolfgang; Lemire, Martine; Nevelsky, A.; Nguyen, D.; Paganini, L.; Pasler, Marlies; Azorín, José Fernando Pérez; García, Luis Isaac Ramos; Russo, S.; Shakeshaft, John; Vieillevigne, L.; Hernández, Víctor

doi:10.1016/j.radonc.2023.109775

Cited by 7 publications

(7 citation statements)

References 48 publications

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“…It is expected that there will be some spread in community parameter selection to account for differences between machines, however, discrepancies of 1−2% (among matched or unmatched machines) are not large enough to account for the observed dose deviations with respect to 50‐percentile community values 1,21,22,30–33 . A recent publication by Saez et al 2 . found minimal differences between machine physical characteristic (less than 2%) compared to the differences in TPS modeling (resulting in differences of greater than 10% between calculated and measured doses).…”

Section: Discussionmentioning

confidence: 99%

“…found minimal differences between machine physical characteristic (less than 2%) compared to the differences in TPS modeling (resulting in differences of greater than 10% between calculated and measured doses). As such, we can expect that the differences in modeling will be much larger than the differences in the actual physical characteristics 2 . Great care must be taken when using atypical values to ensure errors associated with suboptimal modeling are avoided, as they lead directly to errors in phantom audits and subsequently, incorrect dose in the patient.…”

Section: Discussionmentioning

confidence: 99%

“… 1 , 21 , 22 , 30 , 31 , 32 , 33 A recent publication by Saez et al. 2 found minimal differences between machine physical characteristic (less than 2%) compared to the differences in TPS modeling (resulting in differences of greater than 10% between calculated and measured doses). As such, we can expect that the differences in modeling will be much larger than the differences in the actual physical characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…As such, we can expect that the differences in modeling will be much larger than the differences in the actual physical characteristics. 2 Great care must be taken when using atypical values to ensure errors associated with suboptimal modeling are avoided, as they lead directly to errors in phantom audits and subsequently, incorrect dose in the patient. A comparison between selected and published 50‐percentile parameter values, followed by a dosimetry audit, can provide valuable insight as to the accuracy of beam model configuration.…”

Section: Discussionmentioning

confidence: 99%

“…While every instance of atypical beam modeling does not necessitate a change in modeling parameters, there is evidence that parameter values different from community median values can be a cause for concern. 1 , 2 Previous studies have examined the impact multi‐leaf collimator (MLC) modeling errors can have on dose calculation accuracy and found clinically impactful dose errors. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 However, these studies have been based on arbitrary parameter values and therefore may not reflect the true realities of clinical TPS modeling errors.…”

Section: Introductionmentioning

confidence: 99%

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A radiotherapy community data‐driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy

Brooks,

Glenn,

Hernandez

et al. 2024

J Applied Clin Med Phys

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PurposeTo quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy.MethodsTen beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non‐small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th‐percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R‐squared values were used to determine the best metric.ResultsPerturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites.ConclusionExtreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A radiotherapy community data‐driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy

Brooks,

Glenn,

Hernandez

et al. 2024

J Applied Clin Med Phys

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Which failures do patient‐specific quality assurance systems need to catch?

O'Daniel,

Masgrau,

Clark

et al. 2024

Medical Physics

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BackgroundThe Joint AAPM‐ESTRO TG‐360 is developing a quantitative framework to evaluate treatment verification systems used for patient‐specific quality assurance (PSQA). A subgroup was commissioned to determine which potential failure modes had the greatest risk to treatment quality and safety, and therefore should be evaluated as part of the PSQA verification.PurposeTo create an extensive database of potential radiotherapy failure modes that should be detected by PSQA and to determine their relative importance for maximizing treatment quality.MethodsThe subgroup consisted of eight physicists from seven countries, including representatives from three international quality assurance groups. We collected error reports from RO‐ILS, SAFRON, AAPM TG publications, and other literature, including international audits. We focused on the subset of failure modes that impact whether the planned dose matches the dose received by the patient. We performed a failure‐mode‐and‐effects analysis (FMEA), estimating the severity (S), occurrence (O), and detectability (D) of each failure mode. Detectability was scored assuming that PSQA was not done but other routine clinical QA was performed, which allowed us to see the importance of PSQA for detecting each specific failure mode. We analyzed the risk priority number (RPN = O*S*D), O*S, and severity rankings to determine the priority of each failure mode.ResultsWe collected 394 error reports, which we categorized into 33 failure modes that underwent FMEA. Five failure modes were in the top ranks for both RPN and O*S analysis: four involving treatment planning system (TPS) commissioning and one regarding patient model errors. The highest‐ranking RPN failure modes were: TPS algorithm limitations, TPS commissioning errors [multileaf collimator (MLC) modeling, output factor, percent‐depth‐dose/tissue‐maximum‐ratio (PDD/TMR), off‐axis factor], and patient weight variation. The highest O*S failure modes were similar, with the addition of external patient position variation and incorrect linear accelerator isocenter and cGy/monitor units calibration. RPN and O*S analyses prioritized failure modes that impacted multiple patients with high occurrence and detectability scores, while severity analysis gave higher priority to single‐patient modes with high severity scores. The highest‐ranking severity modes were MLC sequence deletion, collision, and TPS isocenter incorrect.ConclusionWe have developed a list of failure modes critical to be detected during PSQA and ranked them in order of importance. The top failure modes emphasize the importance of utilizing a variety of treatment verification systems for PSQA, from secondary dose calculation through in‐vivo dosimetry, in order to detect all possible errors. For failure modes in the top quartile, PSQA is critical. Without adequate PSQA, these errors may go undetected unless caught by an external audit. This analysis can be useful for optimizing PSQA workflows and for designing evaluations of treatment verification systems, and will be used by the Joint AAPM‐ESTRO TG‐360 to determine an appropriate validation strategy.

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Improving the modelling of a multi-leaf collimator with tilted leaf sides used in radiotherapy

Hussein,

Angerud,

Saez

et al. 2024

Physics and Imaging in Radiation Oncology

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Universal evaluation of MLC models in treatment planning systems based on a common set of dynamic tests

Cited by 7 publications

References 48 publications

A radiotherapy community data‐driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy

A radiotherapy community data‐driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy

Which failures do patient‐specific quality assurance systems need to catch?

Improving the modelling of a multi-leaf collimator with tilted leaf sides used in radiotherapy

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