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RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies.

A Fogliata1, L Cozzi2,3, G Reggiori2

  • 1Radiotherapy Department, Humanitas Research Hospital and Cancer Center, Via Manzoni 56, 20089 Rozzano, Milan, Italy. antonella.fogliata@humanitas.it.

Radiation Oncology (London, England)
|November 1, 2019
PubMed
Summary
This summary is machine-generated.

Iterative learning improved RapidPlan (RP) models for head and neck cancer radiotherapy. Re-training with generated plans enhanced model performance and plan quality, particularly for organs at risk.

Keywords:
Knowledge based planningRapidPlan

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Machine Learning in Healthcare

Background:

  • Knowledge-based treatment planning systems like RapidPlan (RP) are crucial in radiotherapy.
  • Optimizing RP model performance through iterative learning is an area of active research.

Purpose of the Study:

  • To investigate if an iterative learning process can enhance the performance of a knowledge-based RapidPlan (RP) model.
  • To determine if re-training an RP model with its own generated plans improves treatment plan quality.

Main Methods:

  • Trained an initial RP model (CL-1) using 83 head and neck cancer VMAT plans.
  • Generated new plans with CL-1 and used them to train a second model (CL-2).
  • Validated both models on 20 patients, comparing dosimetric outcomes.

Main Results:

  • The CL-2 model demonstrated improved performance over CL-1, indicated by higher R² values and better regression.
  • CL-2 resulted in decreased mean doses to parallel organs at risk.
  • D1% to serial organs increased slightly but not significantly with CL-2.

Conclusions:

  • An iterative learning approach refines RapidPlan models, leading to improved treatment plan quality.
  • The enhanced RP model (CL-2) showed benefits primarily for parallel organs at risk.
  • Iterative retraining may enhance the intrinsic quality of knowledge-based planning models.