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A two-step optimization method for improving multiple brain lesion treatments with robotic radiosurgery.

L Ma1, A Sahgal, A Hwang

  • 1University of California San Francisco, Department of Radiation Oncology and Neurosurgery, UCSF Medical Center 505 Parnassus Avenue, Room L08 San Francisco, CA 94143, USA. ijunma@radonc.ucsf.edu

Technology in Cancer Research & Treatment
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Summary

A new two-step optimization method significantly improves robotic radiosurgery plans for multiple brain lesions. This technique enhances target dose distribution and normal brain sparing, reducing planning effort for complex treatments.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Neurosurgery

Background:

  • Planning robotic radiosurgery for multiple brain metastases (n > 3) is complex.
  • Existing methods struggle with numerous dose-volume constraints and varied target doses.

Purpose of the Study:

  • To develop and evaluate a sequential two-step optimization technique for multi-target robotic radiosurgery.
  • To improve treatment plan quality and reduce planning effort.

Main Methods:

  • A two-step optimization approach was developed: individual target planning followed by 3D dose matrix optimization.
  • A singular-value-decomposition (SVD) algorithm minimized dose interference between targets.
  • Optimized plans were compared against conventional simultaneous planning methods.

Main Results:

  • The two-step optimization yielded significant improvements in target dose distribution and normal brain sparing.
  • Normal brain volume receiving low doses (e.g., 12-Gy) was reduced by an average of 42%.
  • Improvements increased with the number of targets, though normal brain dose rose non-linearly.

Conclusions:

  • The sequential two-step optimization technique effectively enhances treatment plan quality for multi-target robotic radiosurgery.
  • This method reduces planning complexity and improves outcomes for patients with multiple brain lesions.