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An interactive plan and model evolution method for knowledge-based pelvic VMAT planning.

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Summary

A closed-loop evolution process significantly improved RapidPlan models and training plans, reducing organ at risk doses. This iterative approach enhances treatment planning by interactively refining both the model and its constituent plans.

Keywords:
RapidPlanknowledge-based planningmodel improvementrectal cancer

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Machine Learning in Healthcare

Background:

  • Automated treatment planning systems like RapidPlan aim to improve efficiency and plan quality in radiation therapy.
  • The effectiveness of these models depends on the quality and diversity of their training data.
  • Interactive refinement of models and plans could lead to further optimization.

Purpose of the Study:

  • To evaluate the efficacy of a closed-loop evolution process for interactively improving a RapidPlan dose-volume histogram (DVH) estimation model and its training plans.
  • To determine if this iterative refinement enhances treatment plan quality compared to standard methods.

Main Methods:

  • Eighty-one manual plans (P0) were used to train an initial RapidPlan model (M0).
  • These plans were reoptimized using M0 (closed-loop) to create P1 plans, and a new model (M1) was trained.
  • A second closed-loop reoptimization created P2 plans, leading to model M2. Models were validated on 30 independent VMAT cases.

Main Results:

  • The first closed-loop reoptimization significantly reduced mean doses to the femoral head, urinary bladder, and small bowel in training plans (P < 0.01).
  • A second reoptimization further reduced these doses significantly (P < 0.01).
  • Open-loop validation showed decreased bladder and bowel doses but increased femoral head dose with M1 and M2 compared to M0; overfitting was addressed with M2_new.

Conclusions:

  • The RapidPlan model and its training plans can be interactively improved through a closed-loop evolution process.
  • Incorporating new patient data into the training library interactively enhances both the RapidPlan model and future treatment plans.