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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

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.

motivation
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
recomputation
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)

monte carlo calculations
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.

data analysis in cerr
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

testing the method
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
test case 1
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)

case 1 dvhs
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
test case 2
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

slide10
Water-Based

Heterogeneity Corrected

TPS (S/C)

Monte Carlo

case 2 dvhs
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
conclusions
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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