Monte carlo corrected dvhs for retrospective dose volume modeling
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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

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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


Monte carlo corrected dvhs for retrospective dose volume modeling

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


Case 2 summary

Case 2 – Summary


Archived patient recomputations

Archived patient recomputations


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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