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Machine learning prediction-informed gaze optimization in ocular proton therapy with NTCP evaluation.

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Summary

This study introduces an automated approach for ocular proton therapy (OPT) planning, optimizing eye orientation to minimize radiation exposure and toxicity risks. Automated plans were comparable or preferable in 92% of cases, enhancing treatment efficiency and safety.

Keywords:
automationintra‐ocular tumorsradiotherapytreatment planning

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Ocular proton therapy (OPT) planning has relied on manual methods for over 40 years, using systems like EyePlan.
  • Current manual planning optimizes eye orientation via gaze fixation points but lacks direct correlation with toxicity risks.
  • This manual approach is inefficient, prone to planner bias, and misses opportunities for automated, toxicity-informed guidance.

Purpose of the Study:

  • To develop a computationally efficient, automated method for OPT treatment planning.
  • Optimize eye orientation to minimize healthy tissue exposure and reduce radiation-induced toxicities.
  • Provide clinicians with intuitive, clinically relevant information for improved treatment decisions.

Main Methods:

  • Utilized an in-house treatment planning system (OCULARIS) integrated with machine learning (ML) gaze predictions.
  • Employed a tailored cost-function to rapidly identify optimal eye orientations for healthy tissue sparing.
  • Evaluated automated orientations using cost metrics and normal tissue complication probability (NTCP) models, validated by experienced medical physicists.

Main Results:

  • Automated plans were preferable or comparable to clinical plans in 92% of cases (25% rated as preferable).
  • Comparable toxicity levels were observed in 67% of cases.
  • 47% of automated plans showed at least a 1% improvement in one NTCP without negatively impacting others.

Conclusions:

  • The developed methodology offers a more informed, efficient, and automated strategy for OPT planning.
  • Provides planners with direct information linking eye orientation to toxicity risks.
  • Automatically generated eye orientations for OPT demonstrated comparable or superior suitability in 92% of patients.