Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors

Herschtal, Alan; Marvelde, Luc te; Mengersen, Kerrie; Hosseinifard, Z; Foroudi, Farshad; Devereux, Tomas; Pham, Daniel; Ball, David; Greer, Peter B.; Pichler, Peter-Paul; Eade, Thomas; Kneebone, Andrew; Bell, Lauren; Caine, H.; Hindson, Ben; Kron, Tomas

doi:10.1088/0031-9155/60/5/1793

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…It is known that the estimation of treatment specific standard deviations from a limited number of samples only slowly converges ∼n −1/2 . The inclusion of a Bayesian prior improves the result but only slightly (Herschtal et al 2015). In other words: with a limited number of fractions, we have too little information to rule out a relatively large outlier in one of the next fractions, which might dominate the required margin.…”

Section: Discussionmentioning

confidence: 95%

Adaptive margins for online adaptive radiotherapy

Bijl¹,

Remeijer²,

Sonke³

et al. 2022

Phys. Med. Biol.

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Objective: In online adaptive radiotherapy a new plan is generated every fraction based on the organ and clinical target volume (CTV) delineations of that fraction. This allows for a planning target volume (PTV) margin that does not need to be constant over the whole course of treatment, as is the case in conventional radiotherapy. This work aims to introduce an approach to update the margins each fraction based on the per-patient treatment history and explore the potential benefits of such adaptive margins. Approach: We introduce a novel methodology to implement adaptive margins, isotropic and anisotropic, during a treatment course based on the accumulated dose to the CTV. We then simulate treatment histories for treatments delivered in up to 20 fractions using various choices for the standard deviations of the systematic and random errors and homogeneous and inhomogeneous dose distributions. The treatment-averaged adaptive margin was compared to standard constant margins. The change in the minimum dose delivered to the CTV was compared on a patient and a population level. All simulations were performed within the van Herk approach and its known limitations. Main results: The population mean treatment-averaged margins are down to 70% and 55% of the corresponding necessary constant margins for the isotropic and anisotropic approach. The reduction increases with longer fractionation schemes and an inhomogeneous target dose distribution. Most of the benefit can be attributed to the elimination of the effective systematic error over the course of treatment. Interpatient differences in treatment-averaged margins were largest for the isotropic margins. For the 10% of patients that would receive a lower than prescribed dose to the CTV this minimum dose to the CTV is increased using the adaptive margin approaches. Significance: Adaptive margins can allow to reduce margins in most patients without compromising patients with greater than average target motion.

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Section: Discussionmentioning

confidence: 95%

Adaptive margins for online adaptive radiotherapy

Bijl¹,

Remeijer²,

Sonke³

et al. 2022

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…If we had considered σ to be a single value (the classic approach), the margins calculated would have been much smaller than those really needed (except in cases with very tight margins, such as posterior margins in the prostate and prostate bed). This effect was shown by Herschtal et al, although no setup protocol was assumed in that study. Predicted margins were calculated by including systematic errors in the population, with the result that the effects of random errors were less important in that situation.…”

Section: Discussionmentioning

confidence: 99%

“…In‐room volumetric imaging enabled us to have access to huge datasets and, therefore, to analyze the variation in random errors within patients. Herschtal et al showed that heterogeneity in random errors was related to an increase in treatment margins compared with those calculated with the VHF. The fact that patients have different characteristics and that these can be expressed mathematically paves the way for personalized treatment.…”

Section: Introductionmentioning

confidence: 99%

On the use of Bayesian statistics in the application of adaptive setup protocols in radiotherapy

Sevillano

Capuz

Gómez³

et al. 2019

Medical Physics

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Purpose To propose adaptive setup protocols using Bayesian statistics that facilitate, based on a prediction of coverage probability, making a decision on which patients should follow daily imaging prior to treatment delivery. Materials and Methods The suitability of the treatment margins was assessed combining interfraction variability measurements of the first days of treatment with previous data gathered from our patient population. From this information, we decided if a patient needs an online imaging protocol to perform daily isocenter correction before each treatment fraction. We applied our method to five different datasets. Protocol parameters were selected from each dataset based on coverage probability, the expected imaging workload of the treatment unit, and the accuracy of patient classification. Time trends were assessed and included in the proposed protocols. To validate the accuracy of the protocols, they were applied to a validation dataset of prostate cancer patients. Results Adaptive setup protocols lead expected population coverage >97% in all datasets analyzed when time trends were considered. The reduction in imaging workload ranged from 40% in lung treatments to 28.5% in prostate treatments. Results of the protocol on the validation dataset were very similar to those previously predicted. Conclusions The adaptive setup protocols based on Bayesian statistics presented in this study enable the optimization of imaging workload in the treatment unit ensuring that appropriate dose coverage remains unchanged.

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“…Several authors have attempted to individualise margins based on patient factors [13][14][15][16]. For example, Thompson et al, found that patients with a higher BMI have less intrafraction displacement of the prostate in the superior-inferior dimension compared with patients with a lower BMI [16].…”

Section: Introductionmentioning

confidence: 99%

Intrafraction motion during CyberKnife® prostate SBRT: impact of imaging frequency and patient factors

et al. 2023

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To determine the relationship between imaging frequencies and prostate motion during CyberKnife stereotactic body radiotherapy (SBRT) for prostate cancer. Methods: Intrafraction displacement data for 331 patients who received treatment with CyberKnife for prostate cancer were retrospectively analysed. Prostate positions were tracked with a large variation in imaging frequencies. The percent of treatment time that patients remained inside various motion thresholds for both real and simulated imaging frequencies was calculated. Results: 84,920 image acquisitions over 1635 fractions were analysed. Fiducial distance travelled between consecutive images were less than 2, 3, 5, and 10 mm for 92.4%, 94.4%, 96.2%, and 97.7% of all consecutive imaging pairs respectively. The percent of treatment time that patients received adequate geometric coverage increased with more frequent imaging intervals. No significant correlations between age, weight, height, Motion during CyberKnife prostate SBRT BMI, rectal, bladder or prostate volumes and intrafraction prostate motion were observed. Conclusions: There are several combinations of imaging intervals and movement thresholds that may be suitable for consideration during treatment planning with respect to imaging and CTV-to-PTV margin calculation, resulting in adequate geometric coverage for approximately 95% of treatment time. Rectal toxicities and treatment duration need to be considered when implementing combinations clinically.

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Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors

Cited by 4 publications

References 18 publications

Adaptive margins for online adaptive radiotherapy

Adaptive margins for online adaptive radiotherapy

On the use of Bayesian statistics in the application of adaptive setup protocols in radiotherapy

Intrafraction motion during CyberKnife® prostate SBRT: impact of imaging frequency and patient factors

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