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Percentile-based probabilistic optimization for systematic and random uncertainties in radiation therapy.

Albin Fredriksson1, Erik Engwall1, Jenneke de Jong2,3

  • 1RaySearch Laboratories,Stockholm, Sweden.

Physics in Medicine and Biology
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new probabilistic optimization framework for radiation therapy planning. It explicitly controls the probability of achieving clinical goals, improving treatment quality for prostate and brain cancer cases.

Keywords:
percentile-based optimizationprobabilistic evaluationprobabilistic treatment planningrobust optimizationtreatment course simulation

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Geometric uncertainty in radiation therapy can compromise treatment quality.
  • Current methods like margins and robust optimization offer indirect control over clinical goal achievement probabilities.
  • There is a need for a framework that explicitly targets and quantifies the probability of meeting clinical goals throughout the entire treatment course.

Purpose of the Study:

  • To develop and evaluate a probabilistic optimization framework for radiation therapy planning.
  • The framework aims to explicitly control the probability of fulfilling clinical goals over a full treatment course.
  • Ensuring computational tractability for practical application in treatment planning.

Main Methods:

  • Formulated a probabilistic planning framework utilizing a percentile-based optimization function.
  • Explicitly modeled systematic and random uncertainties across the entire treatment course.
  • Employed interpolation between precomputed dose distributions for efficient dose approximation during optimization.
  • Evaluated the framework on prostate VMAT and brain PBS proton therapy cases, comparing against margin-based and worst-case robust optimization.

Main Results:

  • Probabilistic optimization enhanced organ at risk (OAR) sparing in prostate VMAT cases, increasing OAR goal fulfillment probability by 13.3% and reducing 90th percentile OAR doses by 3.5 Gy.
  • In brain PBS cases, it significantly improved target minimum dose passing probabilities (84% vs. 19% for D95) and brainstem core maximum dose passing probability (56% vs. 2%).
  • The framework maintained or improved OAR sparing compared to conventional methods in both evaluated cases.

Conclusions:

  • The percentile-based probabilistic optimization framework provides explicit and interpretable control over clinical goal fulfillment probabilities.
  • It enables practical incorporation of probabilities into treatment planning by combining full treatment course modeling with efficient dose approximation.
  • This approach offers a significant advancement in optimizing radiation therapy delivery with quantifiable confidence in achieving desired outcomes.