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Implementation of Machine Learning Models to Ensure Radiotherapy Quality for Multicenter Clinical Trials: Report from

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Knowledge-based Planning (KBP) models improve radiotherapy plan quality assurance for clinical trials. Machine learning tools enhance organ-at-risk sparing, particularly for proton therapy, ensuring adherence to protocol requirements.

Keywords:
clinical trial quality assuranceknowledge-based planningnon-small-cell lung cancerradiotherapy

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Machine Learning

Background:

  • Radiotherapy (RT) plan quality assurance is crucial for patient outcomes and clinical trial success.
  • Knowledge-based Planning (KBP) models, trained on high-quality plans, are used to guide RT treatment planning.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning-driven KBP tools in guiding quality assurance for multicenter clinical trial RT plans.
  • To compare the quality of KBP-generated plans against original submitted plans.

Main Methods:

  • Utilized data from 130 patients in the RTOG1308 trial.
  • Trained separate photon and proton KBP models using principal component analysis on 50 patient cases.
  • Evaluated KBP model performance on 80 patient cases, comparing KBP plans with original plans.

Main Results:

  • Both photon and proton KBP plans showed statistically significant improvements in organ-at-risk (OAR) sparing.
  • Proton KBP plans demonstrated greater improvements compared to photon KBP plans.
  • KBP models proved effective in ensuring RT plans meet protocol requirements.

Conclusions:

  • KBP models are valuable tools for enhancing the quality assurance of radiotherapy plans in multicenter clinical trials.
  • The KBP proton model is particularly useful for generating high-quality proton therapy plans that comply with trial protocols.