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

Objective.Manual inverse planning for radiation therapy is labor-intensive and often prone to inconsistent plan quality due to multiple adjustable planning hyperparameters, varying planner experience, and differing time constraints. Automated treatment planning offers a solution to these challenges. The present study investigates the effectiveness of dimension-scaled prior (DSP) Vanilla Bayesian optimization (BO) with a log-expected improvement (logEI) acquisition function in automating intensity-modulated radiation therapy (IMRT) planning for cancer cervix (CaCx) in high-dimensional settings.Approach.A Python-based auto-optimization script utilizing DSP Vanilla BO with logEI was employed to iteratively optimize the planning hyperparameters, including dose objectives and their corresponding weights for CaCx case IMRT plans on the Varian Eclipse treatment planning system (TPS) v18.0. This approach was assessed in 30 retrospectively selected pelvic node-positive CaCx cases, and the dosimetric parameters, based on EMBRACE-II protocol, were compared with plans generated from manual, Sparse Axis-Aligned Subspace BO (SAASBO), and stopping criterion-based DSP Vanilla BO.Main results.DSP Vanilla BO plans demonstrated superior dose conformity (CI95%PTV45&CI80%PTV45) and organs at risk (OAR) sparing (V40GyBowel,V30GyBowel,V40GyBladder,V40GyRectum,D0.01%femoral heads,Dmeanfemoral heads, andDmeankidneys) compared to manual planning with significant improvements (p< 0.05), while maintaining adequate clinical target coverage for CTV_N, PTV₅₅, ITV₄₅, and PTV₄₅. Compared to SAASBO, DSP Vanilla BO achieved comparable dosimetric quality but with less computation time (∼94 vs 360 min). The addition of stopping criteria further reduced the optimization time to ∼44 min while maintaining a plan quality comparable to manual planning.Significance.The study demonstrated that DSP Vanilla BO automated plans achieved comparable target coverage, with an improvement in OAR sparing, compared to manual plans. This highlights its effectiveness as an efficient, data-independent method for automating IMRT planning, which can be easily integrated into a TPS and benefit clinics with limited resources.