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Area of Science:

  • Medical Physics
  • Radiation Oncology

Background:

  • Fractionated radiation therapy requires precise dose delivery.
  • Interfractional geometric variations can compromise treatment quality.
  • Adaptive strategies aim to correct for these variations.

Purpose of the Study:

  • To develop a framework for robust treatment planning and adaptive re-optimization.
  • To maintain high treatment quality despite geometric variations.
  • To identify patients who benefit most from adaptive fractionation.

Main Methods:

  • Simulated treatments using stochastic minimax optimization on a 1D phantom.
  • Three adaptive strategies investigated: updating error scenarios, adapting conservativeness, and recalculating margins.
  • Simulations included anticipated and unpredictably larger errors.

Main Results:

  • Robustly optimized plans ensure quality for anticipated errors.
  • Adaptive strategies improve organ-at-risk protection.
  • The first adaptive strategy (updating error scenarios) with weekly adaptation performed best for larger errors.

Conclusions:

  • A framework for robust re-optimization and margin adaptation in response to geometric errors was presented.
  • Robust adaptive strategies can identify candidates for individualized adaptive treatment.
  • The simulated framework guides the development of optimally controlled robust adaptive therapy.