Analytical probabilistic modeling of RBE-weighted dose for ion therapy

Particle therapy is especially prone to uncertainties. This issue is usually addressed with uncertainty quantification and minimization techniques based on scenario sampling. For proton therapy, however, it was recently shown that it is also possible to use closed-form computations based on analytical probabilistic modeling (APM) for this purpose. APM yields unique features compared to sampling-based approaches, motivating further research in this context. This paper demonstrates the application of APM for intensity-modulated carbon ion therapy to quantify the influence of setup and range uncertainties on the RBE-weighted dose. In particular, we derive analytical forms for the nonlinear computations of the expectation value and variance of the RBE-weighted dose by propagating linearly correlated Gaussian input uncertainties through a pencil beam dose calculation algorithm. Both exact and approximation formulas are presented for the expectation value and variance of the RBE-weighted dose and are subsequently studied in-depth for a one-dimensional carbon ion spread-out Bragg peak. With V and B being the number of voxels and pencil beams, respectively, the proposed approximations induce only a marginal loss of accuracy while lowering the computational complexity from order [Formula: see text] to [Formula: see text] for the expectation value and from [Formula: see text] to [Formula: see text] for the variance of the RBE-weighted dose. Moreover, we evaluated the approximated calculation of the expectation value and standard deviation of the RBE-weighted dose in combination with a probabilistic effect-based optimization on three patient cases considering carbon ions as radiation modality against sampled references. The resulting global γ-pass rates (2 mm,2%) are [Formula: see text]99.15% for the expectation value and [Formula: see text]94.95% for the standard deviation of the RBE-weighted dose, respectively. We applied the derived analytical model to carbon ion treatment planning, although the concept is in general applicable to other ion species considering a variable RBE.

[1]  M Scholz,et al.  Track structure and the calculation of biological effects of heavy charged particles. , 1996, Advances in space research : the official journal of the Committee on Space Research.

[2]  S Nill,et al.  Boosting runtime-performance of photon pencil beam algorithms for radiotherapy treatment planning. , 2012, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[3]  E. Pedroni,et al.  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. , 1999, Physics in medicine and biology.

[4]  Michael Scholz,et al.  Quantification of the relative biological effectiveness for ion beam radiotherapy: direct experimental comparison of proton and carbon ion beams and a novel approach for treatment planning. , 2010, International journal of radiation oncology, biology, physics.

[5]  Radhe Mohan,et al.  Effectiveness of robust optimization in intensity-modulated proton therapy planning for head and neck cancers. , 2013, Medical physics.

[6]  J. Unkelbach,et al.  Inclusion of organ movements in IMRT treatment planning via inverse planning based on probability distributions. , 2004, Physics in medicine and biology.

[7]  Radhe Mohan,et al.  Robust optimization of intensity modulated proton therapy. , 2012, Medical physics.

[8]  M Bangert,et al.  Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy , 2017, Physics in medicine and biology.

[9]  Timothy C Y Chan,et al.  Accounting for range uncertainties in the optimization of intensity modulated proton therapy , 2007, Physics in medicine and biology.

[10]  Wei Chen,et al.  Including robustness in multi-criteria optimization for intensity-modulated proton therapy , 2011, Physics in medicine and biology.

[11]  H. Paganetti Range uncertainties in proton therapy and the role of Monte Carlo simulations , 2012, Physics in medicine and biology.

[12]  Laurence Court,et al.  Fast range-corrected proton dose approximation method using prior dose distribution , 2012, Physics in medicine and biology.

[13]  Uwe Oelfke,et al.  Worst case optimization for interfractional motion mitigation in carbon ion therapy of pancreatic cancer , 2016, Radiation Oncology.

[14]  T. Inaniwa,et al.  A robustness analysis method with fast estimation of dose uncertainty distributions for carbon-ion therapy treatment planning. , 2016, Physics in medicine and biology.

[15]  Katia Parodi,et al.  Development of the open‐source dose calculation and optimization toolkit matRad , 2017, Medical physics.

[16]  Harald Paganetti,et al.  Proton vs carbon ion beams in the definitive radiation treatment of cancer patients. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[17]  A. Lomax,et al.  Is it necessary to plan with safety margins for actively scanned proton therapy? , 2011, Physics in medicine and biology.

[18]  M. Hoogeman,et al.  Fast and accurate sensitivity analysis of IMPT treatment plans using Polynomial Chaos Expansion , 2016, Physics in medicine and biology.

[19]  O Jäkel,et al.  Treatment planning for heavy ion radiotherapy: clinical implementation and application. , 2001, Physics in medicine and biology.

[20]  E. Pedroni,et al.  Intensity modulated proton therapy: a clinical example. , 2001, Medical physics.

[21]  R. Siddon Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[22]  K Parodi,et al.  Variance-based sensitivity analysis of biological uncertainties in carbon ion therapy. , 2014, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[23]  Uwe Oelfke,et al.  Fast multifield optimization of the biological effect in ion therapy , 2006, Physics in medicine and biology.

[24]  Christopher A. Mattson,et al.  Propagating Skewness and Kurtosis Through Engineering Models for Low-Cost, Meaningful, Nondeterministic Design , 2012 .

[25]  M Alber,et al.  Accelerated evaluation of the robustness of treatment plans against geometric uncertainties by Gaussian processes , 2012, Physics in medicine and biology.

[26]  D. Low,et al.  A technique for the quantitative evaluation of dose distributions. , 1998, Medical physics.

[27]  Francesca Albertini,et al.  Incorporating the effect of fractionation in the evaluation of proton plan robustness to setup errors , 2016, Physics in medicine and biology.

[28]  F Nüsslin,et al.  Treatment simulation approaches for the estimation of the distributions of treatment quality parameters generated by geometrical uncertainties , 2004, Physics in medicine and biology.

[29]  M Goitein,et al.  A pencil beam algorithm for proton dose calculations. , 1996, Physics in medicine and biology.

[30]  Philipp Hennig,et al.  Analytical probabilistic modeling for radiation therapy treatment planning , 2013, Physics in medicine and biology.

[31]  Thomas Bortfeld,et al.  Reducing the sensitivity of IMPT treatment plans to setup errors and range uncertainties via probabilistic treatment planning. , 2008, Medical physics.

[32]  M Zaider,et al.  The synergistic effects of different radiations. , 1980, Radiation research.

[33]  G. L. Squires Practical Physics: STATISTICAL TREATMENT OF DATA , 2001 .

[34]  M. Herk Errors and margins in radiotherapy. , 2004 .

[35]  Anders Forsgren,et al.  Minimax optimization for handling range and setup uncertainties in proton therapy. , 2011, Medical physics.

[36]  Colin Rose,et al.  mathStatica: Mathematical Statistics with Mathematica , 2002, COMPSTAT.

[37]  M Scholz,et al.  Rapid calculation of biological effects in ion radiotherapy , 2006, Physics in medicine and biology.

[38]  A J Lomax,et al.  Intensity modulated proton therapy and its sensitivity to treatment uncertainties 2: the potential effects of inter-fraction and inter-field motions , 2008, Physics in medicine and biology.

[39]  G. Chen,et al.  Treatment planning for heavy ion radiotherapy. , 1979, International journal of radiation oncology, biology, physics.

[40]  A. Kellerer,et al.  A generalized formulation of dual radiation action. , 2012, Radiation research.

[41]  U Oelfke,et al.  Worst case optimization: a method to account for uncertainties in the optimization of intensity modulated proton therapy , 2008, Physics in medicine and biology.

[42]  Marco Nolden,et al.  The Medical Imaging Interaction Toolkit , 2005, Medical Image Anal..