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Related Experiment Video

Updated: Jun 13, 2026

Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition
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Implementation of an AI-Driven Workflow for Daily Dose Reconstruction in Prostate Cancer Radiotherapy.

Jessica Prunaretty1, Tom Baudouin1, Olivier Riou1

  • 1Institut du Cancer de Montpellier, 34090 Montpellier, France.

Cancers
|June 12, 2026
PubMed
Summary

This study shows artificial intelligence (AI) software can accurately reconstruct daily radiation doses for prostate cancer patients, identifying significant organ-at-risk dose deviations for potential adaptive therapy.

Keywords:
AdaptboxCBCTartificial intelligenceautomationdose reconstructionprostate cancer

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Prostate cancer radiotherapy requires precise dose delivery to maximize tumor control and minimize toxicity.
  • Daily dose verification is crucial for adaptive radiotherapy strategies.
  • Automated tools are needed to efficiently assess dose delivery in clinical practice.

Purpose of the Study:

  • To evaluate the daily delivered dose in prostate cancer patients using AI-based software (Adaptbox).
  • To assess target volume coverage and organ-at-risk (OAR) exposure during radiotherapy.
  • To determine the utility of AI for identifying clinically relevant dose deviations.

Main Methods:

  • Twenty prostate cancer patients undergoing VMAT were included.
  • Daily CBCT images were processed using AI software (Adaptbox) for synthetic CT generation and OAR segmentation.
  • Dose reconstruction and comparison with planned doses were performed for all 800 fractions.

Main Results:

  • AI software accurately reconstructed daily doses with minimal PTV deviation.
  • A significant percentage of fractions exceeded planned rectal dose constraints (V70Gy, V76Gy, V80Gy).
  • Bladder dose constraints were also exceeded in over 50% of fractions, partly due to contouring issues.

Conclusions:

  • AI-based dose reconstruction is reliable for identifying OAR dose deviations in prostate cancer radiotherapy.
  • This workflow supports adaptive interventions by highlighting potential treatment inaccuracies.
  • Accurate OAR contouring remains critical for the effective implementation of AI-driven adaptive radiotherapy.