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Automatic catheter digitization in breast brachytherapy.

Sébastien Quetin1,2,3, Hossein Jafarzadeh1,2,3, Jonathan Kalinowski1,3

  • 1Medical Physics Unit, Department of Oncology, McGill University, Montreal, Canada.

Medical Physics
|September 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated pipeline for digitizing catheters in breast high dose rate (HDR) brachytherapy, significantly reducing manual effort. The AI-powered system accurately digitizes catheters and calculates dose, improving efficiency and precision in cancer treatment.

Keywords:
automatic catheter digitizationbreast brachytherapydeep learning

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Area of Science:

  • Medical Physics
  • Radiotherapy Oncology
  • Artificial Intelligence in Medicine

Background:

  • Manual catheter digitization in high dose rate (HDR) brachytherapy is time-consuming and complex, particularly for breast cancer due to irregular anatomy.
  • Clinical experience heavily influences the accuracy of manual catheter digitization, leading to potential variability.

Purpose of the Study:

  • To develop and validate the first automated pipeline for catheter digitization specifically for breast HDR brachytherapy.
  • To introduce a comprehensive pipeline that automates catheter digitization, dwell position generation, and dose calculation for breast cancer patients.

Main Methods:

  • Utilized treatment data from 117 breast cancer patients undergoing HDR brachytherapy.
  • Trained an nnU-Net pipeline to segment pseudo-contours of catheters from CT images, followed by automated digitization and spline fitting.
  • Developed algorithms to handle colliding catheters and identify catheter tips, then generated dwell positions and predicted absorbed dose using an AI model.

Main Results:

  • The automated pipeline digitized 408 catheters with an average shaft distance of 0.70 ± 3.91 mm and tip distance of 1.37 ± 5.25 mm.
  • Dosimetric errors between manual and automated plans were below 3% for key planning target volume and organs at risk indices.
  • Pipeline execution averaged 118 seconds and successfully flagged all cases requiring manual review.

Conclusions:

  • This pipeline represents the first automation of catheter digitization for breast brachytherapy, including dwell position generation and AI-based dose prediction.
  • The automated digitization shows excellent agreement with manual methods and provides a more accurate representation of catheter anatomy.
  • The system offers a faster, more consistent, and potentially more accurate approach to breast HDR brachytherapy planning.