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This study evaluates the Internal Target Volume (ITV) approach in Stereotactic Body Radiotherapy (SBRT) for lung cancer, covering the entire treatment workflow from simulation to dose delivery using a QUASAR™ respiratory motion phantom. Three respiratory patterns regular, slightly irregular, and rapidly irregular were analyzed to assess the impact of motion on dose accuracy. Mechanical and dosimetric verifications performed with a Semiflex 3D ionization chamber confirmed strong agreement between measured and calculated doses, with VMAT plans, especially multi-arc configurations, demonstrating superior precision. The ITV-based approach proved effective for regular and slightly irregular cycles, while advanced motion management techniques such as respiratory gating are recommended for highly irregular patterns. To further enhance treatment accuracy and reduce uncertainties, the integration of artificial intelligence (AI) systems is proposed, enabling automated motion prediction, adaptive planning, and real-time optimization. Combined with intelligent ventilation technologies, AI solutions can significantly improve precision and outcomes in SBRT treatments for lung cancer.

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Stereotactic Body Radiotherapy of Lung Cancer Using Internal Target Volume: A Phantom Based End to End Test

  • 会议论文

  • 发表日期：

    2026年

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  • M. Bahri,
  • A. El Haouat,
  • A. Ait Errouhi,
  • W. Rhalem,
  • L. Bellarbi,
  • S. Ziti,
  • M. A. Youssoufi,
  • R. El Baydaoui,
  • S. Boutayeb,
  • K. Hassouni,
  • T. Kebdani,
  • A. Lachgare

摘要

This study evaluates the Internal Target Volume (ITV) approach in Stereotactic Body Radiotherapy (SBRT) for lung cancer, covering the entire treatment workflow from simulation to dose delivery using a QUASAR™ respiratory motion phantom. Three respiratory patterns regular, slightly irregular, and rapidly irregular were analyzed to assess the impact of motion on dose accuracy. Mechanical and dosimetric verifications performed with a Semiflex 3D ionization chamber confirmed strong agreement between measured and calculated doses, with VMAT plans, especially multi-arc configurations, demonstrating superior precision. The ITV-based approach proved effective for regular and slightly irregular cycles, while advanced motion management techniques such as respiratory gating are recommended for highly irregular patterns. To further enhance treatment accuracy and reduce uncertainties, the integration of artificial intelligence (AI) systems is proposed, enabling automated motion prediction, adaptive planning, and real-time optimization. Combined with intelligent ventilation technologies, AI solutions can significantly improve precision and outcomes in SBRT treatments for lung cancer.