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Sterpin E, Widesott L, Poels K, Hoogeman M, Korevaar EW, Lowe M, Molinelli S, Fracchiolla F. Robustness evaluation of pencil beam scanning proton therapy treatment planning: A systematic review. Radiother Oncol 2024; 197:110365. [PMID: 38830538 DOI: 10.1016/j.radonc.2024.110365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 04/30/2024] [Accepted: 05/29/2024] [Indexed: 06/05/2024]
Abstract
Compared to conventional radiotherapy using X-rays, proton therapy, in principle, allows better conformity of the dose distribution to target volumes, at the cost of greater sensitivity to physical, anatomical, and positioning uncertainties. Robust planning, both in terms of plan optimization and evaluation, has gained high visibility in publications on the subject and is part of clinical practice in many centers. However, there is currently no consensus on the methods and parameters to be used for robust optimization or robustness evaluation. We propose to overcome this deficiency by following the modified Delphi consensus method. This method first requires a systematic review of the literature. We performed this review using the PubMed and Web Of Science databases, via two different experts. Potential conflicts were resolved by a third expert. We then explored the different methods before focusing on clinical studies that evaluate robustness on a significant number of patients. Many robustness assessment methods are proposed in the literature. Some are more successful than others and their implementation varies between centers. Moreover, they are not all statistically or mathematically equivalent. The most sophisticated and rigorous methods have seen more limited application due to the difficulty of their implementation and their lack of widespread availability.
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Affiliation(s)
- E Sterpin
- KU Leuven - Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium; UCLouvain - Institution de Recherche Expérimentale et Clinique, Center of Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium; Particle Therapy Interuniversity Center Leuven - PARTICLE, Leuven, Belgium.
| | - L Widesott
- Proton Therapy Center - UO Fisica Sanitaria, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - K Poels
- Particle Therapy Interuniversity Center Leuven - PARTICLE, Leuven, Belgium; UZ Leuven, Department of Radiation Oncology, Leuven, Belgium
| | - M Hoogeman
- Erasmus Medical Center, Cancer Institute, Department of Radiotherapy, Rotterdam, the Netherlands; HollandPTC, Delft, the Netherlands
| | - E W Korevaar
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, the Netherlands
| | - M Lowe
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - S Molinelli
- Fondazione CNAO - Medical Physics Unit, Pavia, Italy
| | - F Fracchiolla
- Proton Therapy Center - UO Fisica Sanitaria, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
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Stammer P, Burigo L, Jäkel O, Frank M, Wahl N. Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting. Phys Med Biol 2021; 66. [PMID: 34544068 DOI: 10.1088/1361-6560/ac287f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/20/2021] [Indexed: 11/12/2022]
Abstract
Objective. To present an efficient uncertainty quantification method for range and set-up errors in Monte Carlo (MC) dose calculations. Further, we show that uncertainty induced by interplay and other dynamic influences may be approximated using suitable error correlation models.Approach. We introduce an importance (re-)weighting method in MC history scoring to concurrently construct estimates for error scenarios, the expected dose and its variance from a single set of MC simulated particle histories. The approach relies on a multivariate Gaussian input and uncertainty model, which assigns probabilities to the initial phase space sample, enabling the use of different correlation models. Through modification of the phase space parameterization, accuracy can be traded between that of the uncertainty or the nominal dose estimate.Main results. The method was implemented using the MC code TOPAS and validated for proton intensity-modulated particle therapy (IMPT) with reference scenario estimates. We achieve accurate results for set-up uncertainties (γ2 mm/2%≥ 99.01% (E[d]),γ2 mm/2%≥ 98.04% (σ(d))) and expectedly lower but still sufficient agreement for range uncertainties, which are approximated with uncertainty over the energy distribution. Here pass rates of 99.39% (E[d])/ 93.70% (σ(d)) (range errors) and 99.86% (E[d])/ 96.64% (σ(d)) (range and set-up errors) can be achieved. Initial evaluations on a water phantom, a prostate and a liver case from the public CORT dataset show that the CPU time decreases by more than an order of magnitude.Significance. The high precision and conformity of IMPT comes at the cost of susceptibility to treatment uncertainties in particle range and patient set-up. Yet, dose uncertainty quantification and mitigation, which is usually based on sampled error scenarios, becomes challenging when computing the dose with computationally expensive but accurate MC simulations. As the results indicate, the proposed method could reduce computational effort while also facilitating the use of high-dimensional uncertainty models.
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Affiliation(s)
- P Stammer
- Karlsruhe Institute of Technology, Steinbuch Centre for Computing, Karlsruhe, Germany.,German Cancer Research Center-DKFZ, Department of Medical Physics in Radiation Oncology, Heidelberg, Germany.,HIDSS4Health-Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - L Burigo
- German Cancer Research Center-DKFZ, Department of Medical Physics in Radiation Oncology, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
| | - O Jäkel
- German Cancer Research Center-DKFZ, Department of Medical Physics in Radiation Oncology, Heidelberg, Germany.,HIDSS4Health-Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany.,Heidelberg Ion Beam Therapy Center-HIT, Department of Medical Physics in Radiation Oncology, Heidelberg, Germany
| | - M Frank
- Karlsruhe Institute of Technology, Steinbuch Centre for Computing, Karlsruhe, Germany.,HIDSS4Health-Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
| | - N Wahl
- German Cancer Research Center-DKFZ, Department of Medical Physics in Radiation Oncology, Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany
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Wahl N, Hennig P, Wieser HP, Bangert M. Analytical probabilistic modeling of dose-volume histograms. Med Phys 2020; 47:5260-5273. [PMID: 32740930 DOI: 10.1002/mp.14414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 06/16/2020] [Accepted: 07/06/2020] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Radiotherapy, especially with charged particles, is sensitive to executional and preparational uncertainties that propagate to uncertainty in dose and plan quality indicators, for example, dose-volume histograms (DVHs). Current approaches to quantify and mitigate such uncertainties rely on explicitly computed error scenarios and are thus subject to statistical uncertainty and limitations regarding the underlying uncertainty model. Here we present an alternative, analytical method to approximate moments, in particular expectation value and (co)variance, of the probability distribution of DVH-points, and evaluate its accuracy on patient data. METHODS We use Analytical Probabilistic Modeling (APM) to derive moments of the probability distribution over individual DVH-points based on the probability distribution over dose. By using the computed moments to parameterize distinct probability distributions over DVH-points (here normal or beta distributions), not only the moments but also percentiles, that is, α - DVHs, are computed. The model is subsequently evaluated on three patient cases (intracranial, paraspinal, prostate) in 30- and single-fraction scenarios by assuming the dose to follow a multivariate normal distribution, whose moments are computed in closed-form with APM. The results are compared to a benchmark based on discrete random sampling. RESULTS The evaluation of the new probabilistic model on the three patient cases against a sampling benchmark proves its correctness under perfect assumptions as well as good agreement in realistic conditions. More precisely, ca. 90% of all computed expected DVH-points and their standard deviations agree within 1% volume with their empirical counterpart from sampling computations, for both fractionated and single fraction treatments. When computing α - DVH, the assumption of a beta distribution achieved better agreement with empirical percentiles than the assumption of a normal distribution: While in both cases probabilities locally showed large deviations (up to ±0.2), the respective - DVHs for α={0.05,0.5,0.95} only showed small deviations in respective volume (up to ±5% volume for a normal distribution, and up to 2% for a beta distribution). A previously published model from literature, which was included for comparison, exhibited substantially larger deviations. CONCLUSIONS With APM we could derive a mathematically exact description of moments of probability distributions over DVH-points given a probability distribution over dose. The model generalizes previous attempts and performs well for both choices of probability distributions, that is, normal or beta distributions, over DVH-points.
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Affiliation(s)
- Niklas Wahl
- German Cancer Research Center - DKFZ, Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Heidelberg Institute for Radiation Oncology - HIRO, Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Department of Physics and Astronomy, Ruprecht Karls University Heidelberg, Grabengasse 1, Heidelberg, 69117, Germany
| | - Philipp Hennig
- Probabilistics Numerics, Max Planck Institute for Intelligent Systems, Tübingen, 72076, Germany.,Chair for the Methods of Machine Learning, Eberhard Karls University Tübingen, Tübingen, 72024, Germany
| | - Hans-Peter Wieser
- German Cancer Research Center - DKFZ, Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Heidelberg Institute for Radiation Oncology - HIRO, Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Medical Faculty, Ruprecht Karls University Heidelberg, Grabengasse 1, Heidelberg, 69117, Germany.,Department for Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, München, 85748, Germany
| | - Mark Bangert
- German Cancer Research Center - DKFZ, Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Heidelberg Institute for Radiation Oncology - HIRO, Im Neuenheimer Feld 280, Heidelberg, 69120, Germany
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Wieser HP, Karger CP, Wahl N, Bangert M. Impact of Gaussian uncertainty assumptions on probabilistic optimization in particle therapy. ACTA ACUST UNITED AC 2020; 65:145007. [DOI: 10.1088/1361-6560/ab8d77] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Her EJ, Haworth A, Reynolds HM, Sun Y, Kennedy A, Panettieri V, Bangert M, Williams S, Ebert MA. Voxel-level biological optimisation of prostate IMRT using patient-specific tumour location and clonogen density derived from mpMRI. Radiat Oncol 2020; 15:172. [PMID: 32660504 PMCID: PMC7805066 DOI: 10.1186/s13014-020-01568-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 05/13/2020] [Indexed: 12/24/2022] Open
Abstract
AIMS This study aimed to develop a framework for optimising prostate intensity-modulated radiotherapy (IMRT) based on patient-specific tumour biology, derived from multiparametric MRI (mpMRI). The framework included a probabilistic treatment planning technique in the effort to yield dose distributions with an improved expected treatment outcome compared with uniform-dose planning approaches. METHODS IMRT plans were generated for five prostate cancer patients using two inverse planning methods: uniform-dose to the planning target volume and probabilistic biological optimisation for clinical target volume tumour control probability (TCP) maximisation. Patient-specific tumour location and clonogen density information were derived from mpMRI and geometric uncertainties were incorporated in the TCP calculation. Potential reduction in dose to sensitive structures was assessed by comparing dose metrics of uniform-dose plans with biologically-optimised plans of an equivalent level of expected tumour control. RESULTS The planning study demonstrated biological optimisation has the potential to reduce expected normal tissue toxicity without sacrificing local control by shaping the dose distribution to the spatial distribution of tumour characteristics. On average, biologically-optimised plans achieved 38.6% (p-value: < 0.01) and 51.2% (p-value: < 0.01) reduction in expected rectum and bladder equivalent uniform dose, respectively, when compared with uniform-dose planning. CONCLUSIONS It was concluded that varying the dose distribution within the prostate to take account for each patient's clonogen distribution was feasible. Lower doses to normal structures compared to uniform-dose plans was possible whilst providing robust plans against geometric uncertainties. Further validation in a larger cohort is warranted along with considerations for adaptive therapy and limiting urethral dose.
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Affiliation(s)
- E J Her
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia.
| | - A Haworth
- Institute of Medical Physics, University of Sydney, Sydney, Australia
| | - H M Reynolds
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Y Sun
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - A Kennedy
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Australia
| | - V Panettieri
- Alfred Health Radiation Oncology, Melbourne, Australia
| | - M Bangert
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Medical Physics in Radiation Oncology, Heidelberg Institute for Radiation Oncology, Heidelberg, Germany
| | - S Williams
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - M A Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, Australia.,Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Australia.,5D Clinics, Perth, Australia
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Bedford JL, Blasiak‐Wal I, Hansen VN. Dose prescription with spatial uncertainty for peripheral lung SBRT. J Appl Clin Med Phys 2019; 20:160-167. [PMID: 30552738 PMCID: PMC6333140 DOI: 10.1002/acm2.12504] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 10/16/2018] [Accepted: 10/26/2018] [Indexed: 11/11/2022] Open
Abstract
Current clinical practice is to prescribe to 95% of the planning target volume (PTV) in 4D stereotactic body radiotherapy (SBRT) for lung. Frequently the PTV margin has a very low physical density so that the internal target volume (ITV) receives an unnecessarily high dose. This study investigates the alternative of prescribing to the ITV while including the effects of positional uncertainties. Five patients were retrospectively studied with volumetric modulated arc therapy treatment plans. Five plans were produced for each patient: a static plan prescribed to PTV D95% , three probabilistic plans prescribed to ITV D95% and a static plan re-prescribed to ITV D95% after inverse planning. For the three probabilistic plans, the scatter kernel in the dose calculation was convolved with a spatial uncertainty distribution consisting of either a uniform distribution extending ±5 mm in the three orthogonal directions, a distribution consisting of delta functions at ±5 mm, or a Gaussian distribution with standard deviation 5 mm. Median ITV D50% is 23% higher than the prescribed dose for static planning and only 10% higher than the prescribed dose for prescription to the ITV. The choice of uncertainty distribution has less than 2% effect on the median ITV dose. Re-prescribing a static plan and evaluating with a probabilistic dose calculation results in a median ITV D95% which is 1.5% higher than when planning probabilistically. This study shows that a robust probabilistic approach to planning SBRT lung treatments results in the ITV receiving a dose closer to the intended prescription. The exact form of the uncertainty distribution is not found to be critical.
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Affiliation(s)
- James L. Bedford
- Joint Department of PhysicsThe Institute of Cancer ResearchThe Royal Marsden NHS Foundation TrustLondonUK
| | - Irena Blasiak‐Wal
- Joint Department of PhysicsThe Institute of Cancer ResearchThe Royal Marsden NHS Foundation TrustLondonUK
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Unkelbach J, Alber M, Bangert M, Bokrantz R, Chan TCY, Deasy JO, Fredriksson A, Gorissen BL, van Herk M, Liu W, Mahmoudzadeh H, Nohadani O, Siebers JV, Witte M, Xu H. Robust radiotherapy planning. ACTA ACUST UNITED AC 2018; 63:22TR02. [DOI: 10.1088/1361-6560/aae659] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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