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Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer
PURPOSE: Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the cur...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855940/ https://www.ncbi.nlm.nih.gov/pubmed/34153379 http://dx.doi.org/10.1016/j.ijrobp.2021.06.016 |
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author | Engeseth, Grete May He, Renjie Mirkovic, Dragan Yepes, Pablo Mohamed, Abdallah Sherif Radwan Stieb, Sonja Fuller, Clifton Dave Wu, Richard Zhang, Xiadong Hysing, Liv Bolstad Pettersen, Helge Egil Seime Stokkevåg, Camilla Hanquist Mohan, Radhe Frank, Steven Jay Gunn, Gary Brandon |
author_facet | Engeseth, Grete May He, Renjie Mirkovic, Dragan Yepes, Pablo Mohamed, Abdallah Sherif Radwan Stieb, Sonja Fuller, Clifton Dave Wu, Richard Zhang, Xiadong Hysing, Liv Bolstad Pettersen, Helge Egil Seime Stokkevåg, Camilla Hanquist Mohan, Radhe Frank, Steven Jay Gunn, Gary Brandon |
author_sort | Engeseth, Grete May |
collection | PubMed |
description | PURPOSE: Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT. METHODS AND MATERIALS: For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LET(d)) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve. RESULTS: An overall statistically significant RAIC association with dose and LET(d) was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30–2.72) for dose and 2.68 (1.93–4.93) for LET(d), respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LET(d) effect demonstrated increased risk of RAIC with increasing LET(d) for the majority of patients. Estimated probability of RAIC with LET(d) = 1 keV/μm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD(15) were estimated to be 63.6 and 50.1 Gy with LET(d) equal to 2 and 5 keV/μm, respectively. CONCLUSIONS: Our results suggest that the LET(d) effect could be of clinical significance for some patients; LET(d) assessment in clinical treatment plans should therefore be taken into consideration. |
format | Online Article Text |
id | pubmed-8855940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-88559402022-02-18 Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer Engeseth, Grete May He, Renjie Mirkovic, Dragan Yepes, Pablo Mohamed, Abdallah Sherif Radwan Stieb, Sonja Fuller, Clifton Dave Wu, Richard Zhang, Xiadong Hysing, Liv Bolstad Pettersen, Helge Egil Seime Stokkevåg, Camilla Hanquist Mohan, Radhe Frank, Steven Jay Gunn, Gary Brandon Int J Radiat Oncol Biol Phys Article PURPOSE: Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT. METHODS AND MATERIALS: For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LET(d)) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve. RESULTS: An overall statistically significant RAIC association with dose and LET(d) was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30–2.72) for dose and 2.68 (1.93–4.93) for LET(d), respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LET(d) effect demonstrated increased risk of RAIC with increasing LET(d) for the majority of patients. Estimated probability of RAIC with LET(d) = 1 keV/μm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD(15) were estimated to be 63.6 and 50.1 Gy with LET(d) equal to 2 and 5 keV/μm, respectively. CONCLUSIONS: Our results suggest that the LET(d) effect could be of clinical significance for some patients; LET(d) assessment in clinical treatment plans should therefore be taken into consideration. 2021-11-01 2021-06-19 /pmc/articles/PMC8855940/ /pubmed/34153379 http://dx.doi.org/10.1016/j.ijrobp.2021.06.016 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ) |
spellingShingle | Article Engeseth, Grete May He, Renjie Mirkovic, Dragan Yepes, Pablo Mohamed, Abdallah Sherif Radwan Stieb, Sonja Fuller, Clifton Dave Wu, Richard Zhang, Xiadong Hysing, Liv Bolstad Pettersen, Helge Egil Seime Stokkevåg, Camilla Hanquist Mohan, Radhe Frank, Steven Jay Gunn, Gary Brandon Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer |
title | Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer |
title_full | Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer |
title_fullStr | Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer |
title_full_unstemmed | Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer |
title_short | Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer |
title_sort | mixed effect modeling of dose and linear energy transfer correlations with brain image changes after intensity modulated proton therapy for skull base head and neck cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855940/ https://www.ncbi.nlm.nih.gov/pubmed/34153379 http://dx.doi.org/10.1016/j.ijrobp.2021.06.016 |
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