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Method for end-user validation of deformable dose accumulation uncertainty modelling tools.

John Kipritidis1, Alexandra Quinn1, Tomasz Morgas2

  • 1Department of Radiation Oncology, Northern Sydney Cancer Center, Sydney, Australia.

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Summary
This summary is machine-generated.

This study introduces a clinical validation method for deformable dose accumulation (DDA) uncertainty models, crucial for adaptive radiotherapy. The proposed sequence ensures reliable DDA uncertainty tool performance for treatment decisions.

Keywords:
adaptive therapydeformable image registrationdose accumulation

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Image Guidance

Background:

  • Deformable dose accumulation (DDA) uncertainty models are vital for informing treatment decisions in radiotherapy by quantifying deformable image registration (DIR) errors.
  • Limited guidance currently exists for clinicians on validating these DDA uncertainty models in a clinical setting.

Purpose of the Study:

  • To propose a standardized end-user validation sequence for DDA uncertainty modeling tools, functioning as an acceptance test.
  • To leverage existing patient data and clinical treatment planning systems (TPS) for evaluating DDA uncertainty tools.

Main Methods:

  • A validation sequence using a single 'fixed' image and a 'moving' image is proposed, simulating fractional DIRs within a TPS.
  • Outputs from the DDA uncertainty tool, including spatial and dose uncertainties, are imported back into the TPS for cross-checks.
  • Evaluation involves visual and semi-quantitative assessments (point-based, DVH-based) using standard tools and vendor equations, considering varying DIR quality.

Main Results:

  • The validation sequence was demonstrated on a non-clinical tool using a clinical bladder case.
  • Achieved agreement within 2 voxels for spatial uncertainties and up to 5% of prescribed dose for dose uncertainties.
  • Demonstrated reliability in regions with plausible deformation and stable dose gradients (<5%/voxel).

Conclusions:

  • DDA uncertainty tools are increasingly important for adaptive radiotherapy as DDA becomes more common.
  • This work formalizes an end-user validation process using readily available clinical tools.
  • Ensures reliable DDA uncertainty model performance to support adaptive treatment decisions.