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Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
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Automatically tracking brain metastases after stereotactic radiosurgery.

Dylan G Hsu1, Åse Ballangrud1, Kayla Prezelski1

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States.

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Summary

Automatic tracking of brain metastases (BMs) using deep learning is now possible. The METRO system accurately quantifies tumor response to radiotherapy, aiding in patient monitoring and treatment planning.

Keywords:
Brain metastasesDeep learningImage registrationLongitudinal tumor trackingStereotactic radiosurgeryT1 MR post-Gd

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Area of Science:

  • Radiology and Imaging
  • Oncology
  • Artificial Intelligence in Medicine

Background:

  • Patients with brain metastases (BMs) are surviving longer, necessitating repeated stereotactic radiosurgery.
  • Follow-up magnetic resonance (MR) imaging is crucial for monitoring BMs every 2-3 months.
  • Automated tracking and response quantification of BMs after radiotherapy are needed.

Purpose of the Study:

  • To investigate the feasibility of automatically tracking brain metastases (BMs) on longitudinal imaging.
  • To quantify tumor response to radiotherapy using automated methods.
  • To develop and validate a system for longitudinal BM tracking and volumetric measurement.

Main Methods:

  • Developed the METRO (MEtastasis Tracking with Repeated Observations) system for automated patient data processing and BM tracking.
  • Implemented a longitudinal intrapatient registration method for T1 MR post-Gd.
  • Utilized a deep learning model for BM detection and volumetric measurements, validated against manual measurements and radiologist assessments.

Main Results:

  • Successfully tracked 123 irradiated and 38 new BMs.
  • Demonstrated high correlation (Pearson's r=0.88) between manual and METRO measurements of BM diameter changes.
  • Achieved a mean registration error of 1.5 ± 0.2 mm, indicating high accuracy.

Conclusions:

  • Automatic, longitudinal tracking of BMs using deep learning is feasible.
  • The METRO software system effectively tracks and quantifies volumetric changes of BMs.
  • This technology addresses the need for automated monitoring of BMs before and after radiation therapy.