Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk
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...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/164317 |
| _version_ | 1811690577723392000 |
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| author | 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 |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences 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 |
| author_sort | Koh, Calvin Wei Yang |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:06:13Z |
| format | Journal Article |
| id | ntu-10356/164317 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:06:13Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1643172023-02-28T20:09:46Z Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk 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 School of Physical and Mathematical Sciences National Cancer Centre, Singapore Science::Physics Science::Medicine Cancer Patient Clinical Target Volume 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. Published version This work is partially supported by the Duke- NUS Oncology Academic Clinical Programme Proton Research fund (08/FY2019/EX(SL)/65-A111) and Duke-NUS Oncology Academic Clinical Programme Proton Research fund (08/FY2020/EX(SL)/76-A152). 2023-01-16T03:01:41Z 2023-01-16T03:01:41Z 2022 Journal Article 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-. https://dx.doi.org/10.1016/j.adro.2021.100844 2452-1094 https://hdl.handle.net/10356/164317 10.1016/j.adro.2021.100844 35036633 2-s2.0-85123244854 2 7 100844 en 08/FY2019/EX(SL)/65-A111 08/FY2020/EX(SL)/76-A152 Advances in Radiation Oncology © 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 (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
| spellingShingle | Science::Physics Science::Medicine Cancer Patient Clinical Target Volume 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 Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk |
| title | Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk |
| title_full | Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk |
| title_fullStr | Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk |
| title_full_unstemmed | Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk |
| title_short | Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in organ at risk |
| title_sort | quantifying systematic rbe weighted dose uncertainty arising from multiple variable rbe models in organ at risk |
| topic | Science::Physics Science::Medicine Cancer Patient Clinical Target Volume |
| url | https://hdl.handle.net/10356/164317 |
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