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Title: SU-G-TeP3-01: A New Approach for Calculating Variable Relative Biological Effectiveness in IMPT Optimization

Abstract

Purpose: To investigate the impact of a new approach for calculating relative biological effectiveness (RBE) in intensity-modulated proton therapy (IMPT) optimization on RBE-weighted dose distributions. This approach includes the nonlinear RBE for the high linear energy transfer (LET) region, which was revealed by recent experiments at our institution. In addition, this approach utilizes RBE data as a function of LET without using dose-averaged LET in calculating RBE values. Methods: We used a two-piece function for calculating RBE from LET. Within the Bragg peak, RBE is linearly correlated to LET. Beyond the Bragg peak, we use a nonlinear (quadratic) RBE function of LET based on our experimental. The IMPT optimization was devised to incorporate variable RBE by maximizing biological effect (based on the Linear Quadratic model) in tumor and minimizing biological effect in normal tissues. Three glioblastoma patients were retrospectively selected from our institution in this study. For each patient, three optimized IMPT plans were created based on three RBE resolutions, i.e., fixed RBE of 1.1 (RBE-1.1), variable RBE based on linear RBE and LET relationship (RBE-L), and variable RBE based on linear and quadratic relationship (RBE-LQ). The RBE weighted dose distributions of each optimized plan were evaluated in terms ofmore » different RBE values, i.e., RBE-1.1, RBE-L and RBE-LQ. Results: The RBE weighted doses recalculated from RBE-1.1 based optimized plans demonstrated an increasing pattern from using RBE-1.1, RBE-L to RBE-LQ consistently for all three patients. The variable RBE (RBE-L and RBE-LQ) weighted dose distributions recalculated from RBE-L and RBE-LQ based optimization were more homogenous within the targets and better spared in the critical structures than the ones recalculated from RBE-1.1 based optimization. Conclusion: We implemented a new approach for RBE calculation and optimization and demonstrated potential benefits of improving tumor coverage and normal sparing in IMPT planning.« less

Authors:
 [1]; ; ;  [2];  [3]
  1. University of Houston, Houston, TX (United States)
  2. UT MD Anderson Cancer Center, Houston, TX (United States)
  3. Rice University, Houston, TX (United States)
Publication Date:
OSTI Identifier:
22649422
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ANIMAL TISSUES; BIOLOGICAL RADIATION EFFECTS; BRAGG CURVE; NONLINEAR PROBLEMS; OPTIMIZATION; PATIENTS; PLANNING; PROTON BEAMS; RADIATION DOSE DISTRIBUTIONS; RADIOTHERAPY; RBE

Citation Formats

Cao, W, Randeniya, K, Grosshans, D, Mohan, R, and Yepes, P. SU-G-TeP3-01: A New Approach for Calculating Variable Relative Biological Effectiveness in IMPT Optimization. United States: N. p., 2016. Web. doi:10.1118/1.4957081.
Cao, W, Randeniya, K, Grosshans, D, Mohan, R, & Yepes, P. SU-G-TeP3-01: A New Approach for Calculating Variable Relative Biological Effectiveness in IMPT Optimization. United States. doi:10.1118/1.4957081.
Cao, W, Randeniya, K, Grosshans, D, Mohan, R, and Yepes, P. 2016. "SU-G-TeP3-01: A New Approach for Calculating Variable Relative Biological Effectiveness in IMPT Optimization". United States. doi:10.1118/1.4957081.
@article{osti_22649422,
title = {SU-G-TeP3-01: A New Approach for Calculating Variable Relative Biological Effectiveness in IMPT Optimization},
author = {Cao, W and Randeniya, K and Grosshans, D and Mohan, R and Yepes, P},
abstractNote = {Purpose: To investigate the impact of a new approach for calculating relative biological effectiveness (RBE) in intensity-modulated proton therapy (IMPT) optimization on RBE-weighted dose distributions. This approach includes the nonlinear RBE for the high linear energy transfer (LET) region, which was revealed by recent experiments at our institution. In addition, this approach utilizes RBE data as a function of LET without using dose-averaged LET in calculating RBE values. Methods: We used a two-piece function for calculating RBE from LET. Within the Bragg peak, RBE is linearly correlated to LET. Beyond the Bragg peak, we use a nonlinear (quadratic) RBE function of LET based on our experimental. The IMPT optimization was devised to incorporate variable RBE by maximizing biological effect (based on the Linear Quadratic model) in tumor and minimizing biological effect in normal tissues. Three glioblastoma patients were retrospectively selected from our institution in this study. For each patient, three optimized IMPT plans were created based on three RBE resolutions, i.e., fixed RBE of 1.1 (RBE-1.1), variable RBE based on linear RBE and LET relationship (RBE-L), and variable RBE based on linear and quadratic relationship (RBE-LQ). The RBE weighted dose distributions of each optimized plan were evaluated in terms of different RBE values, i.e., RBE-1.1, RBE-L and RBE-LQ. Results: The RBE weighted doses recalculated from RBE-1.1 based optimized plans demonstrated an increasing pattern from using RBE-1.1, RBE-L to RBE-LQ consistently for all three patients. The variable RBE (RBE-L and RBE-LQ) weighted dose distributions recalculated from RBE-L and RBE-LQ based optimization were more homogenous within the targets and better spared in the critical structures than the ones recalculated from RBE-1.1 based optimization. Conclusion: We implemented a new approach for RBE calculation and optimization and demonstrated potential benefits of improving tumor coverage and normal sparing in IMPT planning.},
doi = {10.1118/1.4957081},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: Validate implementation of a published RBE model for DSB induction (RBEDSB) in several general purpose Monte Carlo (MC) code systems and the RayStation™ treatment planning system (TPS). For protons and other light ions, DSB induction is a critical initiating molecular event that correlates well with the RBE for cell survival. Methods: An efficient algorithm to incorporate information on proton and light ion RBEDSB from the independently tested Monte Carlo Damage Simulation (MCDS) has now been integrated into MCNP (Stewart et al. PMB 60, 8249–8274, 2015), FLUKA, TOPAS and a research build of the RayStation™ TPS. To cross-validate the RBEDSBmore » model implementation LET distributions, depth-dose and lateral (dose and RBEDSB) profiles for monodirectional monoenergetic (100 to 200 MeV) protons incident on a water phantom are compared. The effects of recoil and secondary ion production ({sub 2}H{sub +}, {sub 3}H{sub +}, {sub 3}He{sub 2+}, {sub 4}He{sub 2+}), spot size (3 and 10 mm), and transport physics on beam profiles and RBEDSB are examined. Results: Depth-dose and RBEDSB profiles among all of the MC models are in excellent agreement using a 1 mm distance criterion (width of a voxel). For a 100 MeV proton beam (10 mm spot), RBEDSB = 1.2 ± 0.03 (− 2–3%) at the tip of the Bragg peak and increases to 1.59 ± 0.3 two mm distal to the Bragg peak. RBEDSB tends to decrease as the kinetic energy of the incident proton increases. Conclusion: The model for proton RBEDSB has been accurately implemented into FLUKA, MCNP, TOPAS and the RayStation™TPS. The transport of secondary light ions (Z > 1) has a significant impact on RBEDSB, especially distal to the Bragg peak, although light ions have a small effect on (dosexRBEDSB) profiles. The ability to incorporate spatial variations in proton RBE within a TPS creates new opportunities to individualize treatment plans and increase the therapeutic ratio. Dr. Erik Traneus is employed full-time as a Research Scientist at RaySearch Laboratories. The research build of the RayStation used in the study was made available to the University of Washington free of charge. RaySearch Laboratories did not provide any monetary support for the reported studies.« less
  • Purpose: The current practice of considering the relative biological effectiveness (RBE) of protons in intensity modulated proton therapy (IMPT) planning is to use a generic RBE value of 1.1. However, RBE is indeed a variable depending on the dose per fraction, the linear energy transfer, tissue parameters, etc. In this study, we investigate the impact of using variable RBE based optimization (vRBE-OPT) on IMPT dose distributions compared by conventional fixed RBE based optimization (fRBE-OPT). Methods: Proton plans of three head and neck cancer patients were included for our study. In order to calculate variable RBE, tissue specific parameters were obtainedmore » from the literature and dose averaged LET values were calculated by Monte Carlo simulations. Biological effects were calculated using the linear quadratic model and they were utilized in the variable RBE based optimization. We used a Polak-Ribiere conjugate gradient algorithm to solve the model. In fixed RBE based optimization, we used conventional physical dose optimization to optimize doses weighted by 1.1. IMPT plans for each patient were optimized by both methods (vRBE-OPT and fRBE-OPT). Both variable and fixed RBE weighted dose distributions were calculated for both methods and compared by dosimetric measures. Results: The variable RBE weighted dose distributions were more homogenous within the targets, compared with the fixed RBE weighted dose distributions for the plans created by vRBE-OPT. We observed that there were noticeable deviations between variable and fixed RBE weighted dose distributions if the plan were optimized by fRBE-OPT. For organs at risk sparing, dose distributions from both methods were comparable. Conclusion: Biological dose based optimization rather than conventional physical dose based optimization in IMPT planning may bring benefit in improved tumor control when evaluating biologically equivalent dose, without sacrificing OAR sparing, for head and neck cancer patients. The research is supported in part by National Institutes of Health Grant No. 2U19CA021239-35.« less
  • Purpose: To find and evaluate the way of applying deliverable MU constraints into robust spot intensity optimization in Intensity-Modulated- Proton-Therapy (IMPT) to prevent plan quality and robustness from degrading due to machine deliverable MU-constraints. Methods: Currently, the influence of the deliverable MU-constraints is retrospectively evaluated by post-processing immediately following optimization. In this study, we propose a new method based on the quasi-Newton-like L-BFGS-B algorithm with which we turn deliverable MU-constraints on and off alternatively during optimization. Seven patients with two different machine settings (small and large spot size) were planned with both conventional and new methods. For each patient, threemore » kinds of plans were generated — conventional non-deliverable plan (plan A), conventional deliverable plan with post-processing (plan B), and new deliverable plan (plan C). We performed this study with both realistic (small) and artificial (large) deliverable MU-constraints. Results: With small minimum MU-constraints considered, new method achieved a slightly better plan quality than conventional method (D95% CTV normalized to the prescription dose: 0.994[0.992∼0.996] (Plan C) vs 0.992[0.986∼0.996] (Plan B)). With large minimum MU constraints considered, results show that the new method maintains plan quality while plan quality from the conventional method is degraded greatly (D95% CTV normalized to the prescription dose: 0.987[0.978∼0.994] (Plan C) vs 0.797[0.641∼1.000] (Plan B)). Meanwhile, plan robustness of these two method’s results is comparable. (For all 7 patients, CTV DVH band gap at D95% normalized to the prescription dose: 0.015[0.005∼0.043] (Plan C) vs 0.012[0.006∼0.038] (Plan B) with small MU-constraints and 0.019[0.009∼0.039] (Plan C) vs 0.030[0.015∼0.041] (Plan B) with large MU-constraints) Conclusion: Positive correlation has been found between plan quality degeneration and magnitude of deliverable minimal MU. Compared to conventional post-processing method, our new method of incorporating deliverable minimal MU-constraints directly into plan optimization, can produce machine-deliverable plans with better plan qualities and non-compromised plan robustness. This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant and by The Kemper Marley Foundation.« less
  • Purpose: To investigate in a simulation study whether using a variable relative biological effectiveness (RBE) in calculation and optimization of intensity-modulated proton therapy (IMPT) instead of using an RBE of 1.1 would result in significant changes in the RBE-weighted dose (RWD) distributions. Methods and Materials: For 4 patients with head-and-neck tumors, three IMPT plans were prepared respectively. The first plan was physically optimized (IMPT-PO plan), and the RWD was calculated with a constant RBE of 1.1. Then the plan's RWD was recalculated (IMPT-R plan) using a variable RBE model taking into account the linear energy transfer (LET) and tissue-specific radiobiologicalmore » parameters. The third IMPT plan was optimized using a biological optimization routine (IMPT-BO plan). Results: Comparing the IMPT-PO and IMPT-R plans, we observed that the RWD in radioresistant tissues was more sensitive to the LET than in radiosensitive tissues. The IMPT-R plans were in general more inhomogeneous than the IMPT-PO plans. The differences of RWD distributions for all volumes between IMPT-PO and IMPT-BO plans complied with predefined dose-volume constraints. The average LET was significantly lower in IMPT-BO plans than in IMPT-R plans. Conclusion: In radioresistant normal tissues caution has to be used regarding the LET distribution because these are most sensitive to changes in the LET. Biological optimization of IMPT plans based on the organ-specific biological parameters and LET distributions is feasible.« less