PURPOSE: The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LETd ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. METHOD: The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LETd based models for a simulated spread out Bragg peak (SOBP) scenario. RESULTS: The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). CONCLUSION: The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LETd based models should be further evaluated in clinically realistic scenarios.
PURPOSE: The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LETd ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. METHOD: The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LETd based models for a simulated spread out Bragg peak (SOBP) scenario. RESULTS: The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). CONCLUSION: The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LETd based models should be further evaluated in clinically realistic scenarios.
Authors: Mark Newpower; Darshana Patel; Lawrence Bronk; Fada Guan; Pankaj Chaudhary; Stephen J McMahon; Kevin M Prise; Giuseppe Schettino; David R Grosshans; Radhe Mohan Journal: Int J Radiat Oncol Biol Phys Date: 2019-02-05 Impact factor: 7.038
Authors: Kristian S Ytre-Hauge; Lars Fredrik Fjæra; Eivind Rørvik; Tordis J Dahle; Jon Espen Dale; Sara Pilskog; Camilla H Stokkevåg Journal: Sci Rep Date: 2020-04-10 Impact factor: 4.379
Authors: Elisabeth Mara; Monika Clausen; Suphalak Khachonkham; Simon Deycmar; Clara Pessy; Wolfgang Dörr; Peter Kuess; Dietmar Georg; Sylvia Gruber Journal: Med Phys Date: 2020-05-15 Impact factor: 4.071
Authors: Lars Fredrik Fjæra; Daniel J Indelicato; Kristian S Ytre-Hauge; Ludvig P Muren; Yasmin Lassen-Ramshad; Laura Toussaint; Olav Dahl; Camilla H Stokkevåg Journal: Adv Radiat Oncol Date: 2020-08-28
Authors: Wei Yang Calvin Koh; Hong Qi Tan; Yan Yee Ng; Yen Hwa Lin; Khong Wei Ang; Wen Siang Lew; James Cheow Lei Lee; Sung Yong Park Journal: Adv Radiat Oncol Date: 2021-11-11
Authors: Helge Henjum; Tordis J Dahle; Lars Fredrik Fjæra; Eivind Rørvik; Sara Pilskog; Camilla H Stokkevåg; Andrea Mairani; Kristian S Ytre-Hauge Journal: Adv Radiat Oncol Date: 2021-08-17
Authors: Edward A K Smith; N T Henthorn; J W Warmenhoven; S P Ingram; A H Aitkenhead; J C Richardson; P Sitch; A L Chadwick; T S A Underwood; M J Merchant; N G Burnet; N F Kirkby; K J Kirkby; R I Mackay Journal: Sci Rep Date: 2019-12-27 Impact factor: 4.379