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Title: Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk
Authors: Koh, Calvin Wei Yang
Tan, Hong Qi
Ng, Yan Yee
Lin, Yen Hwa
Ang, Khong Wei
Lew, Wen Siang
Lee, James Cheow Lei
Park, Sung Yong
Keywords: Science::Physics
Issue Date: 2022
Source: Koh, C. W. Y., Tan, H. Q., Ng, Y. Y., Lin, Y. H., Ang, K. W., Lew, W. S., Lee, J. C. L. & Park, S. Y. (2022). Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk. Advances in Radiation Oncology, 7(2), 100844-.
Project: 08/FY2019/EX(SL)/65-A111
Journal: Advances in Radiation Oncology
Abstract: Purpose: Relative biological effectiveness (RBE) uncertainties have been a concern for treatment planning in proton therapy, particularly for treatment sites that are near organs at risk (OARs). In such a clinical situation, the utilization of variable RBE models is preferred over constant RBE model of 1.1. The problem, however, lies in the exact choice of RBE model, especially when current RBE models are plagued with a host of uncertainties. This paper aims to determine the influence of RBE models on treatment planning, specifically to improve the understanding of the influence of the RBE models with regard to the passing and failing of treatment plans. This can be achieved by studying the RBE-weighted dose uncertainties across RBE models for OARs in cases where the target volume overlaps the OARs. Multi-field optimization (MFO) and single-field optimization (SFO) plans were compared in order to recommend which technique was more effective in eliminating the variations between RBE models. Methods: Fifteen brain tumor patients were selected based on their profile where their target volume overlaps with both the brain stem and the optic chiasm. In this study, 6 RBE models were analyzed to determine the RBE-weighted dose uncertainties. Both MFO and SFO planning techniques were adopted for the treatment planning of each patient. RBE-weighted dose uncertainties in the OARs are calculated assuming [Formula presented] of 3 Gy and 8 Gy. Statistical analysis was used to ascertain the differences in RBE-weighted dose uncertainties between MFO and SFO planning. Additionally, further investigation of the linear energy transfer (LET) distribution was conducted to determine the relationship between LET distribution and RBE-weighted dose uncertainties. Results: The results showed no strong indication on which planning technique would be the best for achieving treatment planning constraints. MFO and SFO showed significant differences (P <.05) in the RBE-weighted dose uncertainties in the OAR. In both clinical target volume (CTV)-brain stem and CTV-chiasm overlap region, 10 of 15 patients showed a lower median RBE-weighted dose uncertainty in MFO planning compared with SFO planning. In the LET analysis, 8 patients (optic chiasm) and 13 patients (brain stem) showed a lower mean LET in MFO planning compared with SFO planning. It was also observed that lesser RBE-weighted dose uncertainties were present with MFO planning compared with SFO planning technique. Conclusions: Calculations of the RBE-weighted dose uncertainties based on 6 RBE models and 2 different [Formula presented] revealed that MFO planning is a better option as opposed to SFO planning for cases of overlapping brain tumor with OARs in eliminating RBE-weighted dose uncertainties. Incorporation of RBE models failed to dictate the passing or failing of a treatment plan. To eliminate RBE-weighted dose uncertainties in OARs, the MFO planning technique is recommended for brain tumor when CTV and OARs overlap.
ISSN: 2452-1094
DOI: 10.1016/j.adro.2021.100844
Schools: School of Physical and Mathematical Sciences 
Organisations: National Cancer Centre, Singapore
Rights: © 2021 The Authors. Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY-NC-ND license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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