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Automating IMRT planning for cervical cancer using dimension-scaled prior-based Vanilla Bayesian optimization.

Vinay Saini1,2, Sanjay Barman2, Satyajit Pradhan2

  • 1School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India.

Physics in Medicine and Biology
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

Automated intensity-modulated radiation therapy (IMRT) planning for cervical cancer (CaCx) using dimension-scaled prior Vanilla Bayesian optimization (BO) improved organ at risk sparing compared to manual planning. This efficient method offers comparable target coverage and can be integrated into treatment planning systems.

Keywords:
IMRTautomated planningcervical cancerhigh-dimensional Bayesian optimizationhyperparameter optimization

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Computational Biology

Background:

  • Manual inverse planning for radiation therapy is time-consuming and inconsistent.
  • Automated treatment planning can address challenges like variable planner experience and time constraints.
  • Intensity-modulated radiation therapy (IMRT) is a complex planning process.

Purpose of the Study:

  • To investigate the effectiveness of dimension-scaled prior (DSP) Vanilla Bayesian optimization (BO) with a log-expected improvement (logEI) acquisition function for automating IMRT planning.
  • To automate intensity-modulated radiation therapy (IMRT) planning for cervical cancer (CaCx) in high-dimensional settings.
  • To compare the automated plans with manual plans and other BO methods.

Main Methods:

  • A Python-based script using DSP Vanilla BO with logEI was developed to optimize IMRT planning hyperparameters for CaCx.
  • The method was applied to 30 retrospectively selected pelvic node-positive CaCx cases using the Varian Eclipse treatment planning system (TPS).
  • Dosimetric parameters were compared against manual planning, Sparse Axis-Aligned Subspace BO (SAASBO), and stopping criterion-based DSP Vanilla BO, following the EMBRACE-II protocol.

Main Results:

  • DSP Vanilla BO plans showed superior dose conformity and organ at risk (OAR) sparing compared to manual planning (p < 0.05).
  • Clinical target coverage was maintained adequately for CTVN, PTV55, ITV45, and PTV45.
  • DSP Vanilla BO achieved comparable dosimetric quality to SAASBO but with significantly reduced computation time (∼94 vs 360 min). Addition of stopping criteria further reduced time to ∼44 min.

Conclusions:

  • DSP Vanilla BO automated IMRT plans achieved comparable target coverage and improved OAR sparing versus manual plans.
  • This demonstrates DSP Vanilla BO as an efficient, data-independent method for automating IMRT planning.
  • The method is suitable for integration into treatment planning systems, benefiting clinics with limited resources.