Literature DB >> 23391569

Advantages and limitations of the 'worst case scenario' approach in IMPT treatment planning.

M Casiraghi1, F Albertini, A J Lomax.   

Abstract

The 'worst case scenario' (also known as the minimax approach in optimization terms) is a common approach to model the effect of delivery uncertainties in proton treatment planning. Using the 'dose-error-bar distribution' previously reported by our group as an example, we have investigated in more detail one of the underlying assumptions of this method. That is, the dose distributions calculated for a limited number of worst case patient positioning scenarios (i.e. limited number of shifts sampled on a spherical surface) represent the worst dose distributions that can occur during the patient treatment under setup uncertainties. By uniformly sampling patient shifts from anywhere within a spherical error-space, a number of treatment scenarios have been simulated and dose deviations from the nominal dose distribution have been computed. The dose errors from these simulations (comprehensive approach) have then been compared to the dose-error-bar approach previously reported (surface approximation) using both point-by-point and dose- and error-volume-histogram analysis (DVH/EVHs). This comparison has been performed for two different clinical cases treated using intensity modulated proton therapy (IMPT): a skull-base and a spinal-axis tumor. Point-by-point evaluation shows that the surface approximation leads to a correct estimation (95% accuracy) of the potential dose errors for the 96% and 85% of the irradiated voxels, for the two investigated cases respectively. We also found that the voxels for which the surface approximation fails are generally localized close to sharp soft tissue-bone interfaces and air cavities. Moreover, analysis of EVHs and DVHs for the two cases shows that the percentage of voxels of a given volume of interest potentially affected by a certain maximum dose error is correctly estimated using the surface approximation and that this approach also accurately predicts the upper and lower bounds of the DVH curves that can occur under positioning uncertainties. In conclusion, the assumption that the larger the patient shift the worse the dose error does not always hold on a point-by-point basis. Nevertheless, when performing a volumetric analysis, a limited set of worst case error scenarios correctly represents the worst quality of the plan in presence of setup errors. As a consequence of these results, we believe that the worst case scenario approach can be used in the IMPT planning procedure for estimating plan robustness provided that the possible limitations of this approach are known.

Entities:  

Mesh:

Year:  2013        PMID: 23391569     DOI: 10.1088/0031-9155/58/5/1323

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  10 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.  Robustness Analysis for External Beam Radiation Therapy Treatment Plans: Describing Uncertainty Scenarios and Reporting Their Dosimetric Consequences.

Authors:  Adam D Yock; Radhe Mohan; Stella Flampouri; Walter Bosch; Paige A Taylor; David Gladstone; Siyong Kim; Jason Sohn; Robert Wallace; Ying Xiao; Jeff Buchsbaum
Journal:  Pract Radiat Oncol       Date:  2018-12-15

3.  New strategies in radiation therapy: exploiting the full potential of protons.

Authors:  Radhe Mohan; Anita Mahajan; Bruce D Minsky
Journal:  Clin Cancer Res       Date:  2013-09-27       Impact factor: 12.531

4.  Robust optimization in intensity-modulated proton therapy to account for anatomy changes in lung cancer patients.

Authors:  Heng Li; Xiaodong Zhang; Peter Park; Wei Liu; Joe Chang; Zhongxing Liao; Steve Frank; Yupeng Li; Falk Poenisch; Radhe Mohan; Michael Gillin; Ronald Zhu
Journal:  Radiother Oncol       Date:  2015-02-20       Impact factor: 6.280

5.  Impact of respiratory motion on worst-case scenario optimized intensity modulated proton therapy for lung cancers.

Authors:  Wei Liu; Zhongxing Liao; Steven E Schild; Zhong Liu; Heng Li; Yupeng Li; Peter C Park; Xiaoqiang Li; Joshua Stoker; Jiajian Shen; Sameer Keole; Aman Anand; Mirek Fatyga; Lei Dong; Narayan Sahoo; Sujay Vora; William Wong; X Ronald Zhu; Martin Bues; Radhe Mohan
Journal:  Pract Radiat Oncol       Date:  2014-09-11

6.  Robustness quantification methods comparison in volumetric modulated arc therapy to treat head and neck cancer.

Authors:  Wei Liu; Samir H Patel; Jiajian Jason Shen; Yanle Hu; Daniel P Harrington; Xiaoning Ding; Michele Y Halyard; Steven E Schild; William W Wong; Gary A Ezzell; Martin Bues
Journal:  Pract Radiat Oncol       Date:  2016-02-13

7.  Comparison of linear and nonlinear programming approaches for "worst case dose" and "minmax" robust optimization of intensity-modulated proton therapy dose distributions.

Authors:  Maryam Zaghian; Wenhua Cao; Wei Liu; Laleh Kardar; Sharmalee Randeniya; Radhe Mohan; Gino Lim
Journal:  J Appl Clin Med Phys       Date:  2017-03-13       Impact factor: 2.102

8.  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

9.  Assessing the robustness of passive scattering proton therapy with regard to local recurrence in stage III non-small cell lung cancer: a secondary analysis of a phase II trial.

Authors:  Zhengfei Zhu; Wei Liu; Michael Gillin; Daniel R Gomez; Ritsuko Komaki; James D Cox; Radhe Mohan; Joe Y Chang
Journal:  Radiat Oncol       Date:  2014-05-06       Impact factor: 3.481

10.  Potential for Improvements in Robustness and Optimality of Intensity-Modulated Proton Therapy for Lung Cancer with 4-Dimensional Robust Optimization.

Authors:  Shuaiping Ge; Xiaochun Wang; Zhongxing Liao; Lifei Zhang; Narayan Sahoo; Jinzhong Yang; Fada Guan; Radhe Mohan
Journal:  Cancers (Basel)       Date:  2019-01-01       Impact factor: 6.639

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.