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Related Experiment Videos

Three dimensional planning target volumes: a model and a software tool

M Austin-Seymour1, I Kalet, J McDonald

  • 1Department of Radiation Oncology, University of Washington Medical Center, Seattle 98195-6043, USA.

International Journal of Radiation Oncology, Biology, Physics
|December 1, 1995
PubMed
Summary
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This study introduces a software tool for automatically creating 3D planning target volumes (PTVs) from clinical target volumes (CTVs). This ensures accurate radiation therapy for lung and head and neck cancers by accounting for treatment variations.

Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Computational Biology

Background:

  • Accurate radiation therapy planning requires precise definition of target volumes.
  • The planning target volume (PTV) encompasses the clinical target volume (CTV) and accounts for treatment uncertainties.
  • Conformal therapy aims to precisely shape the radiation dose to the target volume.

Purpose of the Study:

  • To describe a software tool that automatically generates three-dimensional (3D) planning target volumes (PTVs) from clinical target volumes (CTVs).
  • To address the need for accurate PTV generation in conformal therapy for lung and head and neck cancers.
  • To incorporate interfractional and intrafractional treatment variations into PTV definition.

Main Methods:

  • A literature review and physician interviews were conducted to determine interfractional and intrafractional variation values.

Related Experiment Videos

  • The software tool is a rule-based expert system developed in Common LISP.
  • It utilizes a model combining CTV contours, critical structure contours, and patient-specific qualitative information.

    • Main Results:

    • The software generates PTV contours as a cylindrical expansion of the CTV.
    • The model incorporates setup uncertainty, respiration, and patient motion using multivariate normal statistics.
    • Rules within the system adjust component values based on specific patient conditions, such as immobilization.

    Conclusions:

    • The developed software tool enables rapid, consistent, and automatic generation of 3D PTVs from CTVs.
    • This automation improves the efficiency and reliability of radiation therapy planning.
    • The tool's portability enhances its applicability across different clinical settings.