Literature DB >> 23927301

An algorithm to assess the need for clinical Monte Carlo dose calculation for small proton therapy fields based on quantification of tissue heterogeneity.

M Bueno1, H Paganetti, M A Duch, J Schuemann.   

Abstract

PURPOSE: In proton therapy, complex density heterogeneities within the beam path constitute a challenge to dose calculation algorithms. This might question the reliability of dose distributions predicted by treatment planning systems based on analytical dose calculation. For cases in which substantial dose errors are expected, resorting to Monte Carlo dose calculations might be essential to ensure a successful treatment outcome and therefore the benefit is worth a presumably long computation time. The aim of this study was to define an indicator for the accuracy of dose delivery based on analytical dose calculations in treatment planning systems for small proton therapy fields to identify those patients for which Monte Carlo dose calculation is warranted.
METHODS: Fourteen patients treated at our facility with small passively scattered proton beams (apertures diameters below 7 cm) were selected. Plans were generated in the XiO treatment planning system in combination with a pencil beam algorithm developed at the Massachusetts General Hospital and compared to Monte Carlo dose calculations. Differences in the dose to the 50% of the gross tumor volume (D50, GTV) were assessed in a field-by-field basis. A simple and fast methodology was developed to quantify the inhomogeneity of the tissue traversed by a single small proton beam using a heterogeneity index (HI)-a concept presented by Plugfelder et al. [Med. Phys. 34, 1506-1513 (2007)] for scanned proton beams. Finally, the potential correlation between the error made by the pencil beam based treatment planning algorithm for each field and the level of tissue heterogeneity traversed by the proton beam given by the HI was evaluated.
RESULTS: Discrepancies up to 5.4% were found in D50 for single fields, although dose differences were within clinical tolerance levels (<3%) when combining all of the fields involved in the treatment. The discrepancies found for each field exhibited a strong correlation to their associated HI-values (Spearman's ρ=0.8, p<0.0001); the higher the level of tissue inhomogeneities for a particular field, the larger the error by the analytical algorithm. With the established correlation a threshold for HI can be set by choosing a tolerance level of 2-3%-commonly accepted in radiotherapy.
CONCLUSIONS: The HI is a good indicator for the accuracy of proton field delivery in terms of GTV prescription dose coverage when small fields are delivered. Each HI-value was obtained from the CT image in less than 3 min on a computer with 2 GHz CPU allowing implementation of this methodology in clinical routine. For HI-values exceeding the threshold, either a change in beam direction (if feasible) or a recalculation of the dose with Monte Carlo would be highly recommended.

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Year:  2013        PMID: 23927301     DOI: 10.1118/1.4812682

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  11 in total

1.  Robust optimization for intensity-modulated proton therapy with soft spot sensitivity regularization.

Authors:  Wenbo Gu; Dan Ruan; Daniel O'Connor; Wei Zou; Lei Dong; Min-Yu Tsai; Xun Jia; Ke Sheng
Journal:  Med Phys       Date:  2019-01-21       Impact factor: 4.071

Review 2.  Advanced Proton Beam Dosimetry Part I: review and performance evaluation of dose calculation algorithms.

Authors:  Jatinder Saini; Erik Traneus; Dominic Maes; Rajesh Regmi; Stephen R Bowen; Charles Bloch; Tony Wong
Journal:  Transl Lung Cancer Res       Date:  2018-04

3.  Assessing the Clinical Impact of Approximations in Analytical Dose Calculations for Proton Therapy.

Authors:  Jan Schuemann; Drosoula Giantsoudi; Clemens Grassberger; Maryam Moteabbed; Chul Hee Min; Harald Paganetti
Journal:  Int J Radiat Oncol Biol Phys       Date:  2015-04-08       Impact factor: 7.038

4.  Validation of a GPU-based Monte Carlo code (gPMC) for proton radiation therapy: clinical cases study.

Authors:  Drosoula Giantsoudi; Jan Schuemann; Xun Jia; Stephen Dowdell; Steve Jiang; Harald Paganetti
Journal:  Phys Med Biol       Date:  2015-02-26       Impact factor: 3.609

5.  Improving Proton Dose Calculation Accuracy by Using Deep Learning.

Authors:  Chao Wu; Dan Nguyen; Yixun Xing; Ana Barragan Montero; Jan Schuemann; Haijiao Shang; Yuehu Pu; Steve Jiang
Journal:  Mach Learn Sci Technol       Date:  2021-04-06

6.  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

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

Authors:  J Schuemann; S Dowdell; C Grassberger; C H Min; H Paganetti
Journal:  Phys Med Biol       Date:  2014-07-03       Impact factor: 3.609

8.  Three-dimensional gamma criterion for patient-specific quality assurance of spot scanning proton beams.

Authors:  Chang Chang; Kendra L Poole; Anthony V Teran; Scott Luckman; Dennis Mah
Journal:  J Appl Clin Med Phys       Date:  2015-09-08       Impact factor: 2.102

9.  Advanced proton beam dosimetry part II: Monte Carlo vs. pencil beam-based planning for lung cancer.

Authors:  Dominic Maes; Jatinder Saini; Jing Zeng; Ramesh Rengan; Tony Wong; Stephen R Bowen
Journal:  Transl Lung Cancer Res       Date:  2018-04

10.  Contour-based lung dose prediction for breast proton therapy.

Authors:  Chuan Zeng; Kevin Sine; Dennis Mah
Journal:  J Appl Clin Med Phys       Date:  2018-08-23       Impact factor: 2.102

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