Literature DB >> 23337713

The clinical impact of uncertainties in the mean excitation energy of human tissues during proton therapy.

Abigail Besemer1, Harald Paganetti, Bryan Bednarz.   

Abstract

Uncertainties in the estimated mean excitation energies (I-values) needed for calculating proton stopping powers can be in the order of 10-15%, which introduces a fundamental limitation in the accuracy of proton range determination. Previous efforts have quantified shifts in proton depth dose distributions due to I-value uncertainties in water and homogenous tissue phantoms. This study is the first to quantify the clinical impact of I-value uncertainties on proton dose distributions within patient geometries. A previously developed Geant4 based Monte Carlo code was used to simulate a proton treatment plan for three patients (prostate, pancreases, and liver) with varying tissue I-values. A uniform variation study was conducted in which the tissue I-values were varied by ±5% and ±10% of the nominal values as well as a probabilistic variation study in which the I-values were randomly sampled according to a normal distribution with the mean equal to the nominal I-value and a standard deviation of 5 and 10% of the nominal values. Modification of tissue I-values impacted both the proton range and SOBP width. R(90) range shifts up to 7.7 mm (4.4.%) and R(80) range shifts up to 4.8 mm (1.9%) from the nominal range were recorded. Modulating the tissue I-values by 10% the nominal value resulted in up to a 3.5% difference mean dose in the target volumes and organs at risk compared to the nominal case. The range and dose differences were the largest for the deeper-seated prostate and pancreas cases. The treatments that were simulated with randomly sampled I-values resulted in range and dose differences that were generally within the upper and lower bounds set by the 10% uniform variations. This study demonstrated the impact of I-value uncertainties on patient dose distributions. Clearly, sub-millimeter precision in proton therapy would necessitate a reduction in I-value uncertainties to ensure an efficacious clinical outcome.

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Year:  2013        PMID: 23337713      PMCID: PMC3590005          DOI: 10.1088/0031-9155/58/4/887

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


  12 in total

1.  Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions.

Authors:  W Schneider; T Bortfeld; W Schlegel
Journal:  Phys Med Biol       Date:  2000-02       Impact factor: 3.609

2.  Atomic mean excitation energies for stopping powers from local plasma oscillator strengths.

Authors:  J W Wilson; Y J Xu; C K Chang; E Kamaratos
Journal:  J Appl Phys       Date:  1984-08-01       Impact factor: 2.546

3.  Treatment planning for heavy-ion radiotherapy: physical beam model and dose optimization.

Authors:  M Krämer; O Jäkel; T Haberer; G Kraft; D Schardt; U Weber
Journal:  Phys Med Biol       Date:  2000-11       Impact factor: 3.609

4.  Accurate Monte Carlo simulations for nozzle design, commissioning and quality assurance for a proton radiation therapy facility.

Authors:  H Paganetti; H Jiang; S Y Lee; H M Kooy
Journal:  Med Phys       Date:  2004-07       Impact factor: 4.071

5.  Adaptation of GEANT4 to Monte Carlo dose calculations based on CT data.

Authors:  H Jiang; H Paganetti
Journal:  Med Phys       Date:  2004-10       Impact factor: 4.071

6.  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

7.  Dosimetry robustness with stochastic optimization.

Authors:  Omid Nohadani; Joao Seco; Benjamin C Martin; Thomas Bortfeld
Journal:  Phys Med Biol       Date:  2009-05-13       Impact factor: 3.609

8.  A dielectric response study of the electronic stopping power of liquid water for energetic protons and a new I-value for water.

Authors:  D Emfietzoglou; R Garcia-Molina; I Kyriakou; I Abril; H Nikjoo
Journal:  Phys Med Biol       Date:  2009-05-13       Impact factor: 3.609

9.  Clinical implementation of full Monte Carlo dose calculation in proton beam therapy.

Authors:  Harald Paganetti; Hongyu Jiang; Katia Parodi; Roelf Slopsema; Martijn Engelsman
Journal:  Phys Med Biol       Date:  2008-08-13       Impact factor: 3.609

Review 10.  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

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  11 in total

1.  A linear, separable two-parameter model for dual energy CT imaging of proton stopping power computation.

Authors:  Dong Han; Jeffrey V Siebers; Jeffrey F Williamson
Journal:  Med Phys       Date:  2016-01       Impact factor: 4.071

Review 2.  Empowering Intensity Modulated Proton Therapy Through Physics and Technology: An Overview.

Authors:  Radhe Mohan; Indra J Das; Clifton C Ling
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-10-01       Impact factor: 7.038

Review 3.  The physics of proton therapy.

Authors:  Wayne D Newhauser; Rui Zhang
Journal:  Phys Med Biol       Date:  2015-03-24       Impact factor: 3.609

4.  Energy Deposition around Swift Carbon-Ion Tracks in Liquid Water.

Authors:  Pablo de Vera; Simone Taioli; Paolo E Trevisanutto; Maurizio Dapor; Isabel Abril; Stefano Simonucci; Rafael Garcia-Molina
Journal:  Int J Mol Sci       Date:  2022-05-30       Impact factor: 6.208

5.  Murine-specific Internal Dosimetry for Preclinical Investigations of Imaging and Therapeutic Agents.

Authors:  Bryan Bednarz; Joseph Grudzinski; Ian Marsh; Abby Besemer; Dana Baiu; Jamey Weichert; Mario Otto
Journal:  Health Phys       Date:  2018-04       Impact factor: 1.316

6.  Dosimetric impact of range uncertainty in passive scattering proton therapy.

Authors:  Ruirui Liu; Baozhou Sun; Tiezhi Zhang; Jeffery F Williamson; Joseph A O'Sullivan; Tianyu Zhao
Journal:  J Appl Clin Med Phys       Date:  2021-04-02       Impact factor: 2.102

7.  Physics and biomedical challenges of cancer therapy with accelerated heavy ions.

Authors:  Marco Durante; Jürgen Debus; Jay S Loeffler
Journal:  Nat Rev Phys       Date:  2021-09-17

8.  Proton range verification in inhomogeneous tissue: Treatment planning system vs. measurement vs. Monte Carlo simulation.

Authors:  Dae-Hyun Kim; Sungkoo Cho; Kwanghyun Jo; EunHyuk Shin; Chae-Seon Hong; Youngyih Han; Tae-Suk Suh; Do Hoon Lim; Doo Ho Choi
Journal:  PLoS One       Date:  2018-03-05       Impact factor: 3.240

9.  Deriving the mean excitation energy map from dual-energy and proton computed tomography.

Authors:  Gloria Vilches-Freixas; Catherine Therese Quiñones; Jean Michel Létang; Simon Rit
Journal:  Phys Imaging Radiat Oncol       Date:  2018-04-26

Review 10.  Range Verification Methods in Particle Therapy: Underlying Physics and Monte Carlo Modeling.

Authors:  Aafke Christine Kraan
Journal:  Front Oncol       Date:  2015-07-07       Impact factor: 6.244

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