Literature DB >> 31696729

Is an analytical dose engine sufficient for intensity modulated proton therapy in lung cancer?

Suliana Teoh1,2, Francesca Fiorini1,3, Ben George1,2, Katherine A Vallis1,2, Frank Van den Heuvel1,2.   

Abstract

OBJECTIVE: To identify a subgroup of lung cancer plans where the analytical dose calculation (ADC) algorithm may be clinically acceptable compared to Monte Carlo (MC) dose calculation in intensity modulated proton therapy (IMPT).
METHODS: Robust-optimised IMPT plans were generated for 20 patients to a dose of 70 Gy (relative biological effectiveness) in 35 fractions in Raystation. For each case, four plans were generated: three with ADC optimisation using the pencil beam (PB) algorithm followed by a final dose calculation with the following algorithms: PB (PB-PB), MC (PB-MC) and MC normalised to prescription dose (PB-MC scaled). A fourth plan was generated where MC optimisation and final dose calculation was performed (MC-MC). Dose comparison and γ analysis (PB-PB vs PB-MC) at two dose thresholds were performed: 20% (D20) and 99% (D99) with PB-PB plans as reference.
RESULTS: Overestimation of the dose to 99% and mean dose of the clinical target volume was observed in all PB-MC compared to PB-PB plans (median: 3.7 Gy(RBE) (5%) (range: 2.3 to 6.9 Gy(RBE)) and 1.8 Gy(RBE) (3%) (0.5 to 4.6 Gy(RBE))). PB-MC scaled plans resulted in significantly higher CTVD2 compared to PB-PB (median difference: -4 Gy(RBE) (-6%) (-5.3 to -2.4 Gy(RBE)), p ≤ .001). The overall median γ pass rates (3%-3 mm) at D20 and D99 were 93.2% (range:62.2-97.5%) and 71.3 (15.4-92.0%). On multivariate analysis, presence of mediastinal disease and absence of range shifters were significantly associated with high γ pass rates. Median D20 and D99 pass rates with these predictors were 96.0% (95.3-97.5%) and 85.4% (75.1-92.0%). MC-MC achieved similar target coverage and doses to OAR compared to PB-PB plans.
CONCLUSION: In the presence of mediastinal involvement and absence of range shifters Raystation ADC may be clinically acceptable in lung IMPT. Otherwise, MC algorithm would be recommended to ensure accuracy of treatment plans. ADVANCES IN KNOWLEDGE: Although MC algorithm is more accurate compared to ADC in lung IMPT, ADC may be clinically acceptable where there is mediastinal involvement and absence of range shifters.

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Year:  2019        PMID: 31696729      PMCID: PMC7066954          DOI: 10.1259/bjr.20190583

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.629


  28 in total

1.  Influence of dose engine accuracy on the optimum dose distribution in intensity-modulated proton therapy treatment plans.

Authors:  Martin Soukup; Markus Alber
Journal:  Phys Med Biol       Date:  2007-01-12       Impact factor: 3.609

2.  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

3.  Proton therapy dose distribution comparison between Monte Carlo and a treatment planning system for pediatric patients with ependymoma.

Authors:  Yingcui Jia; Chris Beltran; Daniel J Indelicato; Stella Flampouri; Zuofeng Li; Thomas E Merchant
Journal:  Med Phys       Date:  2012-08       Impact factor: 4.071

4.  Radiation and the heart: systematic review of dosimetry and cardiac endpoints.

Authors:  Joshua R Niska; Cameron S Thorpe; Sorcha M Allen; Thomas B Daniels; William G Rule; Steven E Schild; Carlos E Vargas; Farouk Mookadam
Journal:  Expert Rev Cardiovasc Ther       Date:  2018-11-01

5.  Pencil Beam Algorithms Are Unsuitable for Proton Dose Calculations in Lung.

Authors:  Paige A Taylor; Stephen F Kry; David S Followill
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-06-13       Impact factor: 7.038

Review 6.  Proton beams in cancer treatments: Clinical outcomes and dosimetric comparisons with photon therapy.

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Journal:  Cancer Treat Rev       Date:  2016-01-05       Impact factor: 12.111

Review 7.  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

8.  A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer.

Authors:  Geoffrey D Hugo; Elisabeth Weiss; William C Sleeman; Salim Balik; Paul J Keall; Jun Lu; Jeffrey F Williamson
Journal:  Med Phys       Date:  2017-02-02       Impact factor: 4.071

9.  Technical Note: Defining cyclotron-based clinical scanning proton machines in a FLUKA Monte Carlo system.

Authors:  Francesca Fiorini; Niek Schreuder; Frank Van den Heuvel
Journal:  Med Phys       Date:  2017-12-22       Impact factor: 4.071

10.  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
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  4 in total

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Authors:  Kathryn D Held; Antony J Lomax; Esther G C Troost
Journal:  Br J Radiol       Date:  2020-03       Impact factor: 3.039

2.  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

3.  Lung cancer screening with low-dose computed tomography at a tertiary hospital in Anhui, China and secondary analysis of trial data.

Authors:  Wulin Shan; Zhaowu Chen; Donghua Wei; Ming Li; Liting Qian
Journal:  Br J Radiol       Date:  2020-12-22       Impact factor: 3.039

4.  Assessment of robustness against setup uncertainties using probabilistic scenarios in lung cancer: a comparison of proton with photon therapy.

Authors:  Suliana Teoh; Ben George; Francesca Fiorini; Katherine A Vallis; Frank Van den Heuvel
Journal:  Br J Radiol       Date:  2020-02-04       Impact factor: 3.629

  4 in total

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