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Monte Carlo Corrected DVHs for Retrospective Dose-Volume Modeling

Monte Carlo Corrected DVHs for Retrospective Dose-Volume Modeling. Patricia Lindsay, Joseph Deasy, Issam El Naqa, Milos Vicic. Department of Radiation Oncology Washington University, St. Louis.

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Monte Carlo Corrected DVHs for Retrospective Dose-Volume Modeling

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  1. Monte Carlo Corrected DVHs for Retrospective Dose-Volume Modeling Patricia Lindsay, Joseph Deasy, Issam El Naqa, Milos Vicic Department of Radiation Oncology Washington University, St. Louis Partially supported by NIH grant R01 CA 90445 and a grant from Computerized Medical Systems, Inc.

  2. Motivation • Retrospective analyses of tumor control and complications of lung cancer • Need accurate 3-D dose distributions • Archived dose distributions typically calculated without accounting for tissue heterogeneity (or using a very simple heterogeneity correction) • More accurate dose distributions may lead to different correlations between dose-volume factors and complication rates

  3. Recomputation Match Monte Carlo to Water-based archived dose distribution Recalculate MC using full CT Determine beamlet weights and wedge effects Predict actual dose received by patient Computation Time – about 2 hours per plan (10 nodes on cluster of 1.6 GHz AMD processors)

  4. Monte Carlo Calculations • VMC++ Monte Carlo code (Kawrakow†) • Monte Carlo model of patient transport only • Nominal input spectra (6 or 18 MV) • Not accounting for scatter from beam modifiers (blocks, MLCs, wedges) † Kawrakow I., VMC++, Electron and Photon Monte Carlo Calculations Optimized for Radiation Treatment Planning, Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications: Proceedings of the Monte Carlo 2000 Conference.

  5. Data Analysis in CERR • 3-D treatment plan archives (RTOG format) imported into in-house software (CERR†) • Information in CERR plan: • Beam energy • Gantry angles • MLC or block field shapes • Original 3-D dose distributions • CT scan • Not beam weights †CERR(A Computational Environment for Radiotherapy Research) can be obtained from http://radium.wustl.edu/CERR

  6. Testing the method • Dose distributions prospectively generated with commercial TPS using Clarkson or Superposition/convolution algorithm, with or without heterogeneity corrections • Compared with MC with and without heterogeneity corrections • 6 and 18 MV plans • Five different patient geometries

  7. Test Case 1 • 4 fields • AP-PA, RPO-LAO • No wedges or compensators • MLC shaped • 6 MV Beam Weights: TPS: 35, 25, 24, 16 MC: 34, 26, 24, 16 (Plan review using CERR)

  8. Case 1 – DVHs • Small differences between Clarkson and Superposition/Convolution (S/C) DVHs when heterogeneity is included • MC agrees very well with S/C dose distributions in water and with heterogeneity

  9. Test Case 2 • 3 Fields • AP-PA wedged pair, LAT field • MLC shaped • 18 MV Beam Weights: TPS: 45, 46, 9 MC: 44, 43, 13

  10. Water-Based Heterogeneity Corrected TPS (S/C) Monte Carlo

  11. Case 2 – DVHs • Large differences between Clarkson and S/C DVHs when heterogeneity is included • Again MC agrees very well with S/C dose distributions both in water and with heterogeneity

  12. Case 2 – Summary

  13. Archived patient recomputations

  14. Conclusions • A novel method has been introduced to recompute Monte Carlo dose distributions from treatment planning system data. • Monte Carlo recomputation of 3D dose distributions accounting for patient heterogeneity accurately reproduce superposition/convolution dose distributions. • Using inaccurate heterogeneity corrections may lead to significant errors. • Dose distributions which accurately account for heterogeneity may have an impact on predictions of tumor control and normal tissue complication rates.

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