Proposing a clinical model for RBE based on proton track-end counts

Nicholas T Henthorn, Lydia L Gardner, Adam H Aitkenhead, Benjamin C Rowland, Jungwook Shin, Edward A K Smith, Michael J Merchant, Ranald I Mackay, Karen J Kirkby, Pankaj Chaudhary, Kevin M Prise, Stephen J McMahon, Tracy S A Underwood

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
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BACKGROUND: In proton therapy, the clinical application of linear energy transfer (LET) optimisation remains contentious, in part due to challenges associated with the definition and calculation of LET and its exact relationship with RBE due to large variation in experimental in vitro data. This has raised interest in other metrics with favourable properties for biological optimisation, such as the number of proton track-ends in a voxel. In this work, we propose a novel model for clinical calculations of RBE, based on proton track-end counts.

METHODS: We develop an 'effective dose concept' to translate between the total proton track-end count per unit mass in a voxel, and a proton relative biological effectiveness (RBE) value. Dose, track-end and dose-averaged LET (LETd) distributions were simulated using Monte Carlo models for a series of water phantoms, in vitro radiobiological studies, and patient treatment plans. We evaluated the correlation between track-ends and regions of elevated biological effectiveness in comparison to LETd-based models of RBE.

RESULTS: Track-ends were found to correlate with biological effects in in vitro experiments with an accuracy comparable to LETd. In patient simulations, our track-end model identified the same biological hotspots as predicted by LETd based radiobiological models of proton RBE.

CONCLUSION: These results suggest that, for clinical optimisation and evaluation, an RBE model based on proton track-end counts may match LETd-based models in terms of information provided, while also offering superior statistical properties.

Original languageEnglish
JournalInternational journal of radiation oncology, biology, physics
Early online date12 Jan 2023
Publication statusEarly online date - 12 Jan 2023

Bibliographical note

Copyright © 2023. Published by Elsevier Inc.


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