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An image-guided radiotherapy decision support framework incorporating a Bayesian network and visualization tool.

Catriona Hargrave1,2,3, Timothy Deegan1, Tomasz Bednarz2,4,5

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Summary
This summary is machine-generated.

This study introduces a Bayesian network and visualization tool to aid decisions in online cone-beam CT-guided radiotherapy for prostate cancer. The tools effectively identify treatment variations, guiding decisions on patient repositioning or proceeding with treatment.

Keywords:
Bayesian networkcone-beam computed tomographydecision supportimage-guided radiotherapyvisualization tool

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Area of Science:

  • Medical Physics and Radiation Oncology
  • Radiotherapy Decision Support Systems

Background:

  • Online cone-beam computed tomography (CBCT)-based image-guided radiotherapy (IGRT) requires robust decision-making for prostate cancer patients.
  • Accurate assessment of tumor volume (TV) targeting accuracy and treatment plan compliance (TPC) is crucial for effective IGRT.

Purpose of the Study:

  • To develop and evaluate a Bayesian network (BN) and a complementary visualization tool (IGRTREV) for supporting online CBCT-based IGRT decisions in prostate cancer.
  • To represent relationships between anatomical variations, image alignment, dose delivery, and TPC within the BN framework.

Main Methods:

  • A Bayesian network was constructed to model relationships between prostate, seminal vesicle, bladder, and rectum volume variations, image alignment scores, delivered dose, and TPC.
  • A novel IGRT visualization tool (IGRTREV) using Mollweide projection plots was developed to summarize residual errors post-registration.
  • Sensitivity and scenario analyses were performed to assess BN performance and variable influence on TPC and treatment decisions, validated with retrospective data.

Main Results:

  • The BN indicated a low probability of prostate/seminal vesicle volumes being within thresholds, with rectum and bladder variations showing the highest influence on TPC.
  • Excluding TV targeting errors, TPC was sensitive to seminal vesicle and rectum variations, while treatment decisions were influenced by prostate and seminal vesicle variations.
  • The IGRTREV tool effectively identified anatomical variations, supporting BN recommendations for patient repositioning or treatment continuation, even in cases with borderline metrics.

Conclusions:

  • The developed Bayesian network and IGRTREV plots are effective decision support tools for online CBCT-based IGRT in prostate cancer.
  • Further research is needed to explore alternative TV targeting error modeling approaches and extend the BN for offline adaptive radiotherapy decisions.