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Related Experiment Video

Updated: Jul 29, 2025

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
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Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks.

Dragos Grama1, Max Dahele1, Ward van Rooij1

  • 1Department of Radiation Oncology, Amsterdam UMC, Amsterdam, The Netherlands.

Medical Physics
|May 23, 2023
PubMed
Summary

This study introduces a novel Siamese network for real-time tumor tracking during radiation therapy, significantly improving accuracy and tracking rates compared to conventional methods. This advancement enhances lung tumor treatment by ensuring precise radiation delivery.

Keywords:
deep learningradiotherapytumor tracking

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Artificial Intelligence in Medicine

Background:

  • Radiotherapy (RT) is crucial for cancer treatment, with external beam RT being the most common modality.
  • Volumetric Modulated Arc Therapy (VMAT) is an advanced RT technique involving continuous gantry rotation.
  • Accurate tumor monitoring during SBRT for lung tumors is vital for maximizing tumor control and minimizing dose to surrounding organs.

Purpose of the Study:

  • To investigate patient-specific deep Siamese networks for real-time tumor tracking during VMAT.
  • To address limitations of conventional tracking methods, which often suffer from errors or low tracking rates, especially for small tumors near bony structures.

Main Methods:

  • Developed and trained patient-specific deep Siamese networks using synthetic data (DRRs) from 4D CT scans.
  • Evaluated the Siamese model on a 3D printed anthropomorphic phantom and clinical data (x-rays) from six patients.
  • Assessed performance by comparing with a benchmark template matching method (RTR) and correlating with surface marker data (RPM).

Main Results:

  • The Siamese model achieved a mean absolute distance of 0.57-0.79 mm to ground truth tumor locations on a phantom, outperforming RTR (1.04-1.56 mm).
  • On patient data, the Siamese model showed a high correlation (0.71-0.98) with RPM for longitudinal tumor position, compared to RTR (0.07-0.85).
  • The Siamese model demonstrated a 100% tracking rate, significantly higher than RTR (62%-82%).

Conclusions:

  • Siamese-based real-time 2D markerless tumor tracking is feasible during radiation delivery.
  • This approach shows potential for improving the accuracy and reliability of lung cancer radiotherapy.
  • Further research into 3D tracking using this methodology is warranted.