Literature DB >> 24990623

Site-specific range uncertainties caused by dose calculation algorithms for proton therapy.

J Schuemann1, S Dowdell, C Grassberger, C H Min, H Paganetti.   

Abstract

The purpose of this study was to assess the possibility of introducing site-specific range margins to replace current generic margins in proton therapy. Further, the goal was to study the potential of reducing margins with current analytical dose calculations methods. For this purpose we investigate the impact of complex patient geometries on the capability of analytical dose calculation algorithms to accurately predict the range of proton fields. Dose distributions predicted by an analytical pencil-beam algorithm were compared with those obtained using Monte Carlo (MC) simulations (TOPAS). A total of 508 passively scattered treatment fields were analyzed for seven disease sites (liver, prostate, breast, medulloblastoma-spine, medulloblastoma-whole brain, lung and head and neck). Voxel-by-voxel comparisons were performed on two-dimensional distal dose surfaces calculated by pencil-beam and MC algorithms to obtain the average range differences and root mean square deviation for each field for the distal position of the 90% dose level (R90) and the 50% dose level (R50). The average dose degradation of the distal falloff region, defined as the distance between the distal position of the 80% and 20% dose levels (R80-R20), was also analyzed. All ranges were calculated in water-equivalent distances. Considering total range uncertainties and uncertainties from dose calculation alone, we were able to deduce site-specific estimations. For liver, prostate and whole brain fields our results demonstrate that a reduction of currently used uncertainty margins is feasible even without introducing MC dose calculations. We recommend range margins of 2.8% + 1.2 mm for liver and prostate treatments and 3.1% + 1.2 mm for whole brain treatments, respectively. On the other hand, current margins seem to be insufficient for some breast, lung and head and neck patients, at least if used generically. If no case specific adjustments are applied, a generic margin of 6.3% + 1.2 mm would be needed for breast, lung and head and neck treatments. We conclude that the currently used generic range uncertainty margins in proton therapy should be redefined site specific and that complex geometries may require a field specific adjustment. Routine verifications of treatment plans using MC simulations are recommended for patients with heterogeneous geometries.

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Year:  2014        PMID: 24990623      PMCID: PMC4136435          DOI: 10.1088/0031-9155/59/15/4007

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  38 in total

1.  Dosimetric accuracy of planning and delivering small proton therapy fields.

Authors:  Bryan Bednarz; Juliane Daartz; Harald Paganetti
Journal:  Phys Med Biol       Date:  2010-11-19       Impact factor: 3.609

2.  The impact of uncertainties in the CT conversion algorithm when predicting proton beam ranges in patients from dose and PET-activity distributions.

Authors:  Samuel España; Harald Paganetti
Journal:  Phys Med Biol       Date:  2010-11-19       Impact factor: 3.609

3.  Is it necessary to plan with safety margins for actively scanned proton therapy?

Authors:  F Albertini; E B Hug; A J Lomax
Journal:  Phys Med Biol       Date:  2011-06-27       Impact factor: 3.609

4.  The influence of CT image noise on proton range calculation in radiotherapy planning.

Authors:  Alexei V Chvetsov; Sandra L Paige
Journal:  Phys Med Biol       Date:  2010-02-24       Impact factor: 3.609

5.  Does kV-MV dual-energy computed tomography have an advantage in determining proton stopping power ratios in patients?

Authors:  M Yang; G Virshup; J Clayton; X R Zhu; R Mohan; L Dong
Journal:  Phys Med Biol       Date:  2011-06-30       Impact factor: 3.609

6.  Theoretical variance analysis of single- and dual-energy computed tomography methods for calculating proton stopping power ratios of biological tissues.

Authors:  M Yang; G Virshup; J Clayton; X R Zhu; R Mohan; L Dong
Journal:  Phys Med Biol       Date:  2010-02-10       Impact factor: 3.609

7.  Uncertainties in planned dose due to the limited voxel size of the planning CT when treating lung tumors with proton therapy.

Authors:  Samuel España; Harald Paganetti
Journal:  Phys Med Biol       Date:  2011-05-31       Impact factor: 3.609

8.  Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration.

Authors:  Ming Yang; X Ronald Zhu; Peter C Park; Uwe Titt; Radhe Mohan; Gary Virshup; James E Clayton; Lei Dong
Journal:  Phys Med Biol       Date:  2012-06-07       Impact factor: 3.609

Review 9.  Range uncertainties in proton therapy and the role of Monte Carlo simulations.

Authors:  Harald Paganetti
Journal:  Phys Med Biol       Date:  2012-05-09       Impact factor: 3.609

10.  Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4.

Authors:  J Schümann; H Paganetti; J Shin; B Faddegon; J Perl
Journal:  Phys Med Biol       Date:  2012-05-09       Impact factor: 3.609

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  34 in total

Review 1.  Robust Proton Treatment Planning: Physical and Biological Optimization.

Authors:  Jan Unkelbach; Harald Paganetti
Journal:  Semin Radiat Oncol       Date:  2018-04       Impact factor: 5.934

2.  Differential hepatic avoidance radiation therapy: Proof of concept in hepatocellular carcinoma patients.

Authors:  Stephen R Bowen; Jatinder Saini; Tobias R Chapman; Robert S Miyaoka; Paul E Kinahan; George A Sandison; Tony Wong; Hubert J Vesselle; Matthew J Nyflot; Smith Apisarnthanarax
Journal:  Radiother Oncol       Date:  2015-04-28       Impact factor: 6.280

3.  Intensity modulated proton therapy.

Authors:  H M Kooy; C Grassberger
Journal:  Br J Radiol       Date:  2015-05-27       Impact factor: 3.039

4.  Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints.

Authors:  Lisa Polster; Jan Schuemann; Ilaria Rinaldi; Lucas Burigo; Aimee L McNamara; Robert D Stewart; Andrea Attili; David J Carlson; Tatsuhiko Sato; José Ramos Méndez; Bruce Faddegon; Joseph Perl; Harald Paganetti
Journal:  Phys Med Biol       Date:  2015-06-10       Impact factor: 3.609

5.  Proton beam therapy: the next disruptive innovation in healthcare?

Authors:  Samuel Swisher-McClure; Stephen M Hahn; Justin Bekelman
Journal:  Postgrad Med J       Date:  2015-05       Impact factor: 2.401

6.  Linear energy transfer incorporated intensity modulated proton therapy optimization.

Authors:  Wenhua Cao; Azin Khabazian; Pablo P Yepes; Gino Lim; Falk Poenisch; David R Grosshans; Radhe Mohan
Journal:  Phys Med Biol       Date:  2017-12-19       Impact factor: 3.609

7.  Monte Carlo simulations will change the way we treat patients with proton beams today.

Authors:  H Paganetti
Journal:  Br J Radiol       Date:  2014-06-04       Impact factor: 3.039

8.  Density overwrites of internal tumor volumes in intensity modulated proton therapy plans for mobile lung tumors.

Authors:  Pablo Botas; Clemens Grassberger; Gregory Sharp; Harald Paganetti
Journal:  Phys Med Biol       Date:  2018-01-30       Impact factor: 3.609

9.  Recent developments and comprehensive evaluations of a GPU-based Monte Carlo package for proton therapy.

Authors:  Nan Qin; Pablo Botas; Drosoula Giantsoudi; Jan Schuemann; Zhen Tian; Steve B Jiang; Harald Paganetti; Xun Jia
Journal:  Phys Med Biol       Date:  2016-10-03       Impact factor: 3.609

10.  The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research.

Authors:  Bruce Faddegon; José Ramos-Méndez; Jan Schuemann; Aimee McNamara; Jungwook Shin; Joseph Perl; Harald Paganetti
Journal:  Phys Med       Date:  2020-04-03       Impact factor: 2.685

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