A case-control study of linear energy transfer and relative biological effectiveness related to symptomatic brainstem toxicity following pediatric proton therapy

Fjæra, Lars Fredrik; Indelicato, Daniel J.; Handeland, Andreas H.; Ytre-Hauge, Kristian S.; Lassen‐Ramshad, Yasmin; Muren, L.P.; Stokkevåg, C.H.

doi:10.1016/j.radonc.2022.07.022

Cited by 8 publications

(2 citation statements)

References 45 publications

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“…The dose-averaged LET (LET d ) is commonly utilized as a representation for LET in clinical dose distributions resulting from multi-energetic protons. Studies have reported a direct link between adverse treatment side effects and LET d (Eulitz et al 2019, Bahn et al 2020, Ödén et al 2020, Fjaera et al 2022, making it desirable to optimize LET d and biological dose alongside the physical dose to ensure patient safety. Although advanced proton centers have taken steps to mitigate the effects of variable LET d and RBE (Paganetti andGiantsoudi 2018, Mutter et al 2020), there is still no commercially available treatment planning system that integrates LET d and RBE optimization (Sørensen et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Deep learning based linear energy transfer calculation for proton therapy

Tang,

Wan Chan Tseung,

Moseley

et al. 2024

Phys. Med. Biol.

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Objective: This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LETd) using patient anatomy and dose-to-water (DW) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems.Approach: 275 4-field prostate proton Stereotactic Body Radiotherapy (SBRT) plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LETd distributions from CT images and DW. The accuracy of the LETd of the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis.Main Results: The proposed model accurately inferred LETd distributions for each proton field in the test dataset. A single-field LETd calculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94±0.14 MeV/cm and a gamma passing rate of 97.4% ± 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest.Significance: This study demonstrates that deep-learning-based models can efficiently calculate LETd with high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model’s performance and evaluating its adaptability to different clinical scenarios.

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Section: Introductionmentioning

confidence: 99%

Deep learning based linear energy transfer calculation for proton therapy

Tang,

Wan Chan Tseung,

Moseley

et al. 2024

Phys. Med. Biol.

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“…As a result of the LET build up with depth, elevated RBE values have been observed in the distal part of proton beams (2007,2008). This variation is disregarded by applying a constant RBE value, which may have negative clinical implications as the biological effect to organs at risk (OARs) distal to the target can be underestimated, increasing the risk of normal tissue toxicity (Wang et al 2020, Fjaera et al 2022, Eulitz et al 2023.…”

Section: Introductionmentioning

confidence: 99%

An updated variable RBE model for proton therapy

Lyngholm,

Stokkevåg,

Lühr

et al. 2024

Phys. Med. Biol.

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Objective. While a constant relative biological effectiveness (RBE) of 1.1 forms the basis for clinical proton therapy, variable RBE models are increasingly being used in plan evaluation. However, there is substantial variation across RBE models, and several new in vitro datasets have not yet been included in the existing models. In this study, an updated in vitro proton RBE database was collected and used to examine current RBE model assumptions, and to propose an up-to-date RBE model as a tool for evaluating RBE effects in clinical settings. Approach. A proton database (471 data points) was collected from the literature, almost twice the size of the previously largest model database. Each data point included linear-quadratic model parameters and linear energy transfer (LET). Statistical analyses were performed to test the validity of commonly applied assumptions of phenomenological RBE models, and new model functions were proposed for RBEmax and RBEmin (RBE at the lower and upper dose limits). Previously published models were refitted to the database and compared to the new model in terms of model performance and RBE estimates. Main results. The statistical analysis indicated that the intercept of the RBEmax function should be a free fitting parameter and RBE estimates were clearly higher for models with free intercept. RBEmin increased with increasing LET, while a dependency of RBEmin on the reference radiation fractionation sensitivity ((α/β)x) did not significantly improve model performance. Evaluating the models, the new model gave overall lowest RMSE and highest R2 score. RBE estimates in the distal part of a Spread-Out-Bragg-Peak in water ((α/β)x=2.1Gy) were 1.24-1.51 for original models, 1.25-1.49 for refits and 1.42 for the new model. Significance. An updated RBE model based on the currently largest database among published phenomenological models was proposed. Overall, the new model showed better performance compared to refitted published RBE models.

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