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Original paper| Volume 77, P84-91, September 2020

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Patient-specific quality assurance and plan dose errors on breast intensity-modulated proton therapy

Published:August 13, 2020DOI:https://doi.org/10.1016/j.ejmp.2020.08.006

      Highlights

      • The PB plans that passed the Gamma testing did not yield smaller dose errors compared to the plans that failed the Gamma testing.
      • The MU scaling technique leads to an overall smaller dose error.
      • MU scaling, however, for some DVH evaluation point, worsened some cases.

      Abstract

      Purpose

      To investigate, in proton therapy, whether the Gamma passing rate (GPR) is related to the patient dose error and whether MU scaling can improve dose accuracy.

      Methods

      Among 20 consecutively treated breast patients selected for analysis, two IMPT plans were retrospectively generated: (1) the pencil-beam (PB) plan and (2) the Monte Carlo (MC) plan. Patient-specific QA was performed. A 3%/3-mm Gamma analysis was conducted to compare the TPS-calculated PB algorithm dose distribution with the measured 2D dose. Dose errors were compared between the plans that passed the Gamma testing and those that failed. The MU was then scaled to obtain a better GPR. MU-scaled PB plan dose errors were compared to the original PB plan.

      Results

      Of the 20 PB plans, 8 were passed Gamma testing (G_pass_group) and 12 failed (G_fail_group). Surprisingly, the G_pass_group had a greater dose error than the G_fail_group. The median (range) of the PTV DVH RMSE and PTV ΔDmean were 1.36 (1.00–1.91) Gy vs 1.18 (1.02–1.80) Gy and 1.23 (0.92–1.71) Gy vs 1.10 (0.87–1.49) Gy for the G_pass_group and the G_fail_group, respectively. MU scaling reduced overall dose error. However, for PTV D99 and D95, MU scaling worsened some cases.

      Conclusion

      For breast IMPT, the PB plans that passed the Gamma testing did not show smaller dose errors compared to the plans that failed. For individual plans, the MU scaling technique leads to overall smaller dose errors. However, we do not suggest use of the MU scaling technique to replace the MC plans when the MC algorithm is available.

      Keywords

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      References

        • MacDonald S.M.
        • Patel S.A.
        • Hickey S.
        • Specht M.
        • Isakoff S.J.
        • Gadd M.
        • et al.
        Proton therapy for breast cancer after mastectomy: early outcomes of a prospective clinical trial.
        Int J Radiat Oncol Biol Phys. 2013; 86: 484-490
        • Cuaron J.J.
        • Chon B.
        • Tsai H.
        • Goenka A.
        • DeBlois D.
        • Ho A.
        • et al.
        Early toxicity in patients treated with postoperative proton therapy for locally advanced breast cancer.
        Int J Radiat Oncol Biol Phys. 2015; 92: 284-291
        • Bradley J.A.
        • Dagan R.
        • Ho M.W.
        • Rutenberg M.
        • Morris C.G.
        • Li Z.
        • et al.
        Initial report of a prospective dosimetric and clinical feasibility trial demonstrates the potential of protons to increase the therapeutic ratio in breast cancer compared with photons.
        Int J Radiat Oncol Biol Phys. 2016; 95: 411-421
        • Hug E.B.
        Proton therapy for primary breast cancer.
        Breast Care (Basel). 2018; 13: 168-172
        • Kammerer E.
        • Guevelou J.L.
        • Chaikh A.
        • Danhier S.
        • Geffrelot J.
        • Levy C.
        • et al.
        Proton therapy for locally advanced breast cancer: a systematic review of the literature.
        Cancer Treat Rev. 2018; 63: 19-27
        • Braunstein L.Z.
        • Cahlon O.
        Potential morbidity reduction with proton radiation therapy for breast cancer.
        Semin Radiat Oncol. 2018; 28: 138-149
        • Fagundes M.
        • Hug E.B.
        • Pankuch M.
        • Fang C.
        • McNeeley S.
        • Mao L.
        • et al.
        Proton therapy for local-regionally advanced breast cancer maximizes cardiac sparing.
        Int J Particle Ther. 2015; 1: 827-844
        • Paganetti H.
        Monte Carlo simulations will change the way we treat patients with proton beams today.
        Br J Radiol. 2014; 87 (20140293)
        • Yamashita T.
        • Akagi T.
        • Aso T.
        • Kimura A.
        • Sasaki T.
        Effect of inhomogeneity in a patient's body on the accuracy of the pencil beam algorithm in comparison to Monte Carlo.
        Phys Med Biol. 2012; 57: 7673-7688
        • Taylor P.A.
        • Kry S.F.
        • Followill D.S.
        Pencil beam algorithms are unsuitable for proton dose calculations in lung.
        Int J Radiat Oncol Biol Phys. 2017; 99: 750-756
        • Maes D.
        • Bowen S.R.
        • Fung A.
        • Saini J.
        • Bloch C.
        • Egan A.
        • et al.
        Dose comparison between proton pencil beam and monte carlo dose calculation algorithm in lung cancer patients.
        Int J Radiat Oncol • Biol • Phys. 2017; 99: E694
        • Sasidharan B.K.
        • Aljabab S.
        • Saini J.
        • Wong T.
        • Laramore G.
        • Liao J.
        • et al.
        Clinical Monte Carlo versus pencil beam treatment planning in nasopharyngeal patients receiving IMPT.
        Int J Part Ther. 2019; 5: 32-40
        • Liang X.
        • Li Z.
        • Zheng D.
        • Bradley J.A.
        • Rutenberg M.
        • Mendenhall N.
        A comprehensive dosimetric study of Monte Carlo and pencil-beam algorithms on intensity-modulated proton therapy for breast cancer.
        J Appl Clin Med Phys. 2019; 20: 128-136
        • Tommasino F.
        • Fellin F.
        • Lorentini S.
        • Farace P.
        Impact of dose engine algorithm in pencil beam scanning proton therapy for breast cancer.
        Phys Med. 2018; 50: 7-12
        • Rana S.
        • Greco K.
        • Samuel E.J.J.
        • Bennouna J.
        Radiobiological and dosimetric impact of RayStation pencil beam and Monte Carlo algorithms on intensity-modulated proton therapy breast cancer plans.
        J Appl Clin Med Phys. 2019; 20: 36-46
        • Liang X.
        • Bradley J.A.
        • Zheng D.
        • Rutenberg M.
        • Mailhot Vega R.
        • Mendenhall N.
        • et al.
        The impact of dose algorithms on tumor control probability in intensity-modulated proton therapy for breast cancer.
        Phys Med. 2019; 61: 52-57
        • Low D.A.
        • Harms W.B.
        • Mutic S.
        • Purdy J.A.
        A technique for the quantitative evaluation of dose distributions.
        Med Phys. 1998; 25: 656-661
        • Nelms B.E.
        • Zhen H.
        • Tome W.A.
        Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors.
        Med Phys. 2011; 38: 1037-1044
        • Park J.M.
        • Kim J.I.
        • Park S.Y.
        • Oh D.H.
        • Kim S.T.
        Reliability of the gamma index analysis as a verification method of volumetric modulated arc therapy plans.
        Radiat Oncol. 2018; 13: 175
        • Stasi M.
        • Bresciani S.
        • Miranti A.
        • Maggio A.
        • Sapino V.
        • Gabriele P.
        Pretreatment patient-specific IMRT quality assurance: a correlation study between gamma index and patient clinical dose volume histogram.
        Med Phys. 2012; 39: 7626-7634
        • Hussein M.
        • Rowshanfarzad P.
        • Ebert M.A.
        • Nisbet A.
        • Clark C.H.
        A comparison of the gamma index analysis in various commercial IMRT/VMAT QA systems.
        Radiother Oncol. 2013; 109: 370-376
      1. Fredh A, Scherman JB, Fog LS, Munck af Rosenschold P. Patient QA systems for rotational radiation therapy: a comparative experimental study with intentional errors. Med Phys. 2013;40:031716.

        • Nelms B.E.
        • Simon J.A.
        A survey on planar IMRT QA analysis.
        J Appl Clin Med Phys. 2007; 8: 2448
        • Barber J.
        • Vial P.
        • White P.
        • Menzies N.
        • Deshpande S.
        • Bromley R.
        • et al.
        A survey of modulated radiotherapy use in Australia & New Zealand in 2015.
        Australas Phys Eng Sci Med. 2017; 40: 811-822
      2. Schreuder AN, Bridges DS, Rigsby L, Blakey M, Janson M, Hedrick SG et al. Validation of the RayStation Monte Carlo Dose calculation algorithm using a realistic lung phantom. J Appl Clin Med Phys. 2019;20(12):127–137.

      3. Schreuder AN, Bridges DS, Rigsby L, Blakey M, Janson M, Hedrick SG et al. Validation of the RayStation Monte Carlo dose calculation algorithm using realistic animal tissue phantoms. J Appl Clin Med Phys. 2019;20(10):160-171.

        • Saini J.
        • Maes D.
        • Egan A.
        • Bowen S.R.
        • James S.S.
        • Janson M.
        • et al.
        Dosimetric evaluation of a commercial proton spot scanning Monte-Carlo Dose algorithm: comparisons against measurements and simulations.
        Phys Med Biol. 2017; 62: 7659-7681
        • Zhen H.
        • Nelms B.E.
        • Tome W.A.
        Moving from gamma passing rates to patient DVH-based QA metrics in pretreatment dose QA.
        Med Phys. 2011; 38: 5477-5489
        • Kry S.F.
        • Molineu A.
        • Kerns J.R.
        • Faught A.M.
        • Huang J.Y.
        • Pulliam K.B.
        • et al.
        Institutional patient-specific IMRT QA does not predict unacceptable plan delivery.
        Int J Radiat Oncol Biol Phys. 2014; 90: 1195-1201
        • Montero A.M.B.
        • Souris K.
        • Sanchez-Parcerisa D.
        • Sterpin E.
        • Lee J.A.
        • et al.
        Performance of a Hybrid Monte Carlo-pencil beam dose algorithm for proton therapy inverse planning.
        Med Phys. 2018; 45: 846-862
        • Hong L.
        • Goitein M.
        • Bucciolini M.
        • Gottschalk B.
        • Rosenthal S.
        • Serago C.
        • et al.
        A pencil beam algorithm for proton dose calculations.
        Phys Med Biol. 1996; 41: 1305-1330
        • Schaffner B.
        • Pedroni E.
        • Lomax A.
        Dose calculation models for proton treatment planning using a dynamic beam delivery system: an attempt to include density heterogeneity effects in the analytical dose calculation.
        Phys Med Biol. 1999; 44: 27-41
        • Saini J.
        • Traneus E.
        • Maes D.
        • Regmi R.
        • Bowen S.R.
        • Bloch C.
        • et al.
        Advanced proton beam dosimetry part I: Review and performance evaluation of dose calculation algorithms.
        Transl Lung Cancer Res. 2018; 7: 171-179
        • Taylor P.A.
        • Kry S.F.
        • Followill D.S.
        Pencil beam algorithms are unsuitable for proton dose calculations in lung.
        Int J Radiat Oncol Biol Phys. 2017; 99: 750-756